CN113536214B - Image noise reduction method and device and storage device - Google Patents
Image noise reduction method and device and storage device Download PDFInfo
- Publication number
- CN113536214B CN113536214B CN202010292363.2A CN202010292363A CN113536214B CN 113536214 B CN113536214 B CN 113536214B CN 202010292363 A CN202010292363 A CN 202010292363A CN 113536214 B CN113536214 B CN 113536214B
- Authority
- CN
- China
- Prior art keywords
- noise
- image
- pixel
- reduced
- initial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Algebra (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Image Processing (AREA)
- Picture Signal Circuits (AREA)
Abstract
The application discloses a method, a device and a storage device for image noise reduction, comprising the following steps: acquiring an initial image, and determining a plurality of reference pixel points in the initial image; respectively calculating the noise value of the area where each reference pixel point is located in the initial image; selecting an image block to be noise-reduced containing the reference pixel points from the initial image according to the noise value of the area where each reference pixel point is located, and acquiring a noise reduction threshold value corresponding to the image block to be noise-reduced; based on the noise reduction threshold value corresponding to each image block to be noise reduced, noise reduction processing is carried out on the image block to be noise reduced so as to obtain a corresponding image block with noise reduced; fusing the obtained noise-reduced image blocks to obtain a noise-reduced image; the distance between adjacent reference pixel points is smaller than or equal to the size of the image block to be noise reduced. The technical scheme provided by the application can improve the image noise reduction effect.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to a method and apparatus for image noise reduction, and a storage device.
Background
In recent years, research in the field of computer vision has been continuously developed and advanced, which benefits from the development of image sensing technology and the rapid development of the capability of software and hardware of a computer. Image sensing techniques are widely used in applications such as face recognition, vehicle detection, medical image analysis, etc., and the actual effectiveness of these applications often depends heavily on the noise level of the image. Generally, when the image noise level is low, the reliability of the results output by these applications is high, and conversely, the reliability is low. However, in most cases, the image acquisition is often affected by various factors, and noise is difficult to avoid, so that an image noise reduction technology with good noise reduction effect is urgently needed to solve the problem of poor image noise reduction in the prior art.
Disclosure of Invention
The application mainly solves the technical problem of providing a method, a device and a storage device for image noise reduction, which can realize the effect of improving image noise reduction.
In order to solve the technical problems, the application adopts a technical scheme that: there is provided a method of image denoising, comprising:
acquiring an initial image, and determining a plurality of reference pixel points in the initial image;
respectively calculating the noise value of the area where each reference pixel point is located in the initial image;
Selecting an image block to be noise-reduced containing the reference pixel points from the initial image according to the noise value of the area where each reference pixel point is located, and acquiring a noise reduction threshold value corresponding to the image block to be noise-reduced;
Based on the noise reduction threshold value corresponding to each image block to be noise reduced, carrying out noise reduction processing on the image block to be noise reduced so as to obtain a corresponding image block with noise reduced;
Fusing the obtained noise-reduced image blocks to obtain a noise-reduced image;
the distance between the adjacent reference pixel points is smaller than or equal to the size of the image block to be noise reduced.
In order to solve the technical problems, the application adopts another technical scheme that: there is provided an apparatus for image noise reduction, the apparatus comprising a memory and a processor coupled, wherein,
The memory includes local storage and stores a computer program;
the processor is configured to run the computer program to perform the method as described above.
In order to solve the technical problems, the application adopts another technical scheme that: there is provided a storage device storing a computer program executable by a processor for implementing a method as described above.
According to the technical scheme provided by the application, after an initial image is acquired, a plurality of reference pixel points are determined in the initial image, the noise value of each reference pixel point in the initial image is calculated respectively, then the image block to be noise-reduced comprising the reference pixel point is correspondingly selected in the initial image according to the obtained noise value of the area where each reference pixel point is located, and the noise threshold value corresponding to the current image block to be noise-reduced is acquired according to the noise value of the area where each image block to be noise-reduced is located, so that different noise reduction threshold values are carried out on the image block to be noise-reduced where the reference pixel point is located according to the noise level of the area where different reference pixel points are located.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for image denoising according to the present application;
FIG. 2 is a schematic view illustrating reference pixel selection in an embodiment of a method for image denoising according to the present application;
FIG. 3 is a schematic view illustrating reference pixel selection in an embodiment of a method for image denoising according to the present application;
FIG. 4 is a schematic diagram of image block acquisition corresponding to reference pixel points in an embodiment of a method for image denoising according to the present application;
FIG. 5 is a flowchart illustrating a method for image denoising according to another embodiment of the present application;
FIG. 6 is a flowchart illustrating a method for image denoising according to another embodiment of the present application;
FIG. 7 is a schematic diagram illustrating an edge extension process performed on an initial image according to an embodiment of a method for image denoising of the present application;
FIG. 8 is a flowchart illustrating a method for image denoising according to another embodiment of the present application;
FIG. 9 is a schematic diagram of mapping relationships corresponding to fusion weights and pixel values in an embodiment of a method for image denoising according to the present application;
FIG. 10 is a flowchart illustrating a method for image denoising according to another embodiment of the present application;
FIG. 11 is a flowchart illustrating a method for image denoising according to another embodiment of the present application;
FIG. 12 is a flowchart illustrating a method for image denoising according to another embodiment of the present application;
FIG. 13 is a schematic view of an embodiment of an apparatus for image noise reduction according to the present application;
FIG. 14 is a schematic diagram of a memory device according to an embodiment of the application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the prior art, the image is noise-reduced by a transform domain-based method, that is, the image is converted into the transform domain so as to conveniently extract high-frequency information containing noise, and then the high-frequency information is adjusted by setting a threshold value, so that the image is noise-removed, but the actual noise reduction effect often cannot be expected.
Referring to fig. 1, fig. 1 is a flowchart illustrating an image denoising method according to an embodiment of the application. In the current embodiment, the method provided by the application comprises the following steps:
S110: an initial image is acquired, and a plurality of reference pixel points are determined in the initial image.
The initial image is an image which needs noise reduction processing. The reference pixel points are pixel points selected from the initial image according to a set reference pixel point selection rule. The number of reference pixel points in each initial image is determined based on the size of the initial image and the set reference pixel point selection rule, which is not limited herein. In addition, the set reference pixel selection rule can be set and adjusted by a user according to actual requirements, and the set reference pixel selection rule is not limited in any way.
In an embodiment, after the initial image is acquired, the initial image may be further subjected to data decomposition, so as to obtain YUV data, i.e., Y-channel data, U-channel data, and V-channel data. After the data is decomposed, the image data obtained by the decomposition is respectively subjected to the following related noise reduction method so as to respectively obtain noise-reduced Y-channel data, noise-reduced U-channel data and noise-reduced V-channel data, and then the noise-reduced Y-channel data, the noise-reduced U-channel data and the noise-reduced V-channel data are subjected to data synthesis so as to obtain a noise-reduced image with a better noise reduction effect. It will be appreciated that the types of initial images described above are not limited in particular herein.
S120: and respectively calculating the noise value of the area where each reference pixel point is located in the initial image.
After determining a plurality of reference pixel points in the initial image, the step S110 calculates the noise value of the region where each reference pixel point is located in the initial image.
Further, step S120 calculates a noise value of an area where each reference pixel point is located in the initial image, including: the noise value of the region where the reference point is located is determined by determining a region with the reference pixel point as a vertex and a size of the predetermined region based on pixel values of all pixel points in the region, and a specific calculation manner may be described in the corresponding section below, which is not described in detail herein.
The area where each reference pixel point is located is an image block with a preset size taking the reference pixel point as a vertex in the initial image, and the image block with the preset size is used for determining a noise value of the area where the current reference pixel point is located, so that the size of the image block to be noise reduced corresponding to the current reference pixel point and a corresponding noise reduction threshold value are determined according to the noise value of the area where the reference pixel point is located. Specifically, in one embodiment, each reference pixel is an upper left vertex of an image block of a preset size, and the preset size is 8×8. In another embodiment, each reference pixel is an upper right vertex of an image block of a preset size, and the preset size is 16×16.
The image block is an image block which takes a reference pixel point as an apex in an initial image, and the image block which is obtained by selecting the image block comprising a plurality of pixel points according to a preset image block selection rule, the image block to be noise-reduced is an image block waiting for noise reduction, the image block to be noise-reduced is an image block which takes the reference pixel point as the apex and the size of which is reselected according to the noise value of the area where the reference apex is located, and the specific orientation of the reference pixel point in the image block to be noise-reduced is not limited. In the same initial image, the sizes of the image blocks to be noise-reduced corresponding to different reference pixel points may be different, and the size of the image block to be noise-reduced corresponding to each reference pixel point is determined according to a preset image block selection rule and the noise value of the area where the current reference pixel point is located, and the image blocks to be noise-reduced corresponding to adjacent reference pixel points may have mutually overlapped portions, that is, the same pixel point may be located in a plurality of adjacent image blocks to be noise-reduced simultaneously. Wherein, the preset image block selection rules are explained in detail in the corresponding parts below.
In an embodiment, step S120 may be to calculate the noise estimation value of the area where each reference pixel point in the initial image is located. That is, a noise estimation value in an image block of a preset size where the reference pixel point is located is calculated, and a pixel variance of the image block of the preset size is selected as the noise estimation value in the present embodiment. The preset size is determined according to an empirical value, and is not limited herein, for example, the preset size may be 8×8.
S130: and selecting an image block to be noise reduced containing the reference pixel points from the initial image according to the noise value of the area where each reference pixel point is located, and acquiring a noise reduction threshold value corresponding to the image block to be noise reduced.
The distance between adjacent reference pixel points is smaller than or equal to the size of the image block to be noise reduced.
After calculating the noise value of the area where each reference pixel point is located in the initial image through step S120, selecting an image block to be noise reduced including the reference pixel point in the initial image according to the noise value of the area where each reference pixel point is located calculated in step S120. The image block to be noise-reduced is an image block including reference pixels selected according to the noise value of the region where the reference pixels are located, and since the noise values of the regions where different reference pixels are located in the same initial image are different, the sizes of the image blocks to be noise-reduced corresponding to different reference pixels in the same initial image may be different, which may be described in the following corresponding embodiments.
And simultaneously or after selecting the image block to be noise reduced containing the reference pixel points from the initial image, acquiring a noise reduction threshold value corresponding to the current image block to be noise reduced according to the noise value of the area where each reference pixel point is located. The noise reduction threshold is a noise reduction experience value set by a user according to noise reduction effects corresponding to different noise values in advance, namely, when the noise values are in different preset noise ranges, the noise values correspond to different noise reduction thresholds.
Further, the step S130 selects the image block to be noise reduced including the reference pixel point from the initial image according to the noise value of the region where each reference pixel point is located, and obtains the noise reduction threshold corresponding to the image block to be noise reduced, which includes: determining a preset noise range of a noise value of an area where the reference pixel point is located, selecting an image block to be noise-reduced, which contains the reference pixel point and has a preset block size corresponding to the preset noise range, from an initial image, and acquiring a noise reduction threshold corresponding to the preset noise range as a noise reduction threshold corresponding to the image block to be noise-reduced. Wherein, when the noise in the preset noise range is larger, the corresponding preset block size is larger.
The determining of the preset noise range to which the noise value of the region where the reference pixel point is located refers to determining the preset noise range to which the noise value of the region where the current reference pixel point is located, so as to determine the size of the image block to be noise reduced to be selected according to the determination result. The preset noise range is determined according to the bit width of the initial image, that is, the upper limit value and/or the lower limit value of the preset noise range are determined according to the bit width of the initial image, and the number of the preset noise ranges is greater than or equal to three.
And further acquiring a noise reduction threshold value corresponding to the image block to be noise reduced corresponding to the preset noise range at the same time or after the image block to be noise reduced is selected. Specifically, according to the preset noise range of the noise value of the area where the current reference pixel point is located, which is judged in the step, a noise reduction threshold of the image block to be noise reduced where the current reference pixel point is located is determined. Wherein, when the noise in the preset noise range is larger, the corresponding noise reduction threshold is larger.
Specifically, in the image noise reduction process, for a high noise area, the purpose of image noise reduction is to remove noise as much as possible, and then the information of pixels around the reference pixel point is fully utilized in the noise reduction process to achieve the purpose, so that a relatively large image block needs to be selected by taking the reference pixel point as a vertex. Correspondingly, a larger threshold value needs to be selected for the high noise area to realize effective suppression of noise signals.
As in the present embodiment, to achieve the above-described assumption, the noise value of the region where the reference pixel point is located is first calculated, that is, the noise level of the region where the reference pixel point is located is estimated. Specifically, the variance of pixels in an image block with the current reference pixel point as the top left vertex and the preset size of 8×8 is calculated, the noise level of the image block is reflected by the variance, if the variance is larger, the area where the current reference pixel point is located can be considered to be a high noise area, otherwise, the area where the current reference pixel point is located is considered to be a low noise area.
Specifically, given a two-dimensional image a and a reference pixel point P, wherein the coordinate index of the reference pixel point P is (i, j), firstly, calculating a pixel mean μ of an area where the reference pixel point P is located, and the calculation formula of the pixel mean is as follows:
after the mean value of the pixels at the reference pixel point P is obtained, the noise value (may also be referred to as a noise estimation value in other embodiments) of the region where the reference pixel point P is located is obtained, that is, the variance σ 2 of the region where the reference pixel point P is located is obtained. The variance is calculated by the following formula:
wherein (x, y) in the above formula refers to the pixel coordinates of each pixel point in the region where the reference pixel point is located.
After the noise estimation value of the region where the reference pixel point P is located is obtained, the image block to be noise-reduced, which contains the reference pixel point and has a size equal to the size of the preset block, is selected from the initial image according to the noise value of the region where the reference pixel point P is located and the given preset noise range, and the noise reduction threshold of the image to be noise-reduced is determined.
For example, when the given predetermined noise range is a noise range defined by the first variance threshold t 1 and the second variance threshold t 2, where t 1＜t2 is the predetermined noise range to which the noise value σ 2 of the region where the reference pixel point P is located belongs. Wherein, in the present embodiment, the preset noise ranges include three ranges of less than t 1, greater than or equal to t 1, less than or equal to t 2, and greater than t 2. Specifically, in the present embodiment, the size of the image to be noise reduced, which needs to be selected with the reference pixel point P as the vertex, is determined according to the following formula. The following formula may also be understood as a rule for selecting image block sizes for high, medium, and low noise regions and different regions thereof. Specifically, the definition formula of the image block sizes of different areas is as follows:
It should be noted that, the image block sizes corresponding to different preset noise ranges and the variance threshold used for defining the preset noise ranges may be adjusted according to actual needs, which is not limited herein.
Correspondingly, when or after determining the size of the image block to be noise reduced to be selected, determining a noise reduction threshold T corresponding to the image block to be noise reduced according to the noise value of the region where the reference pixel point is located. When σ 2 is smaller than T 1, the noise reduction threshold T is th_low, when σ 2 is greater than or equal to T 1 and smaller than or equal to T 2, the noise reduction threshold T is th_mid, and when σ 2 is greater than T 2, the noise reduction threshold T is th_high, specifically, the selection formula of the noise reduction threshold T is as follows:
In the above equation, th_low, th_mid, and th_high determine the noise reduction levels of the different noise level regions. Wherein, th_low is less than or equal to th_mid is less than or equal to th_high, namely the noise reduction degree is greater when the noise reduction threshold value T is greater, and conversely the noise reduction degree is smaller when the noise reduction threshold value T is smaller. Here, it should be noted that, th_low, th_mid, th_high, t1, t2 are all empirical parameters preset by the algorithm, the specific values of the parameters are not limited herein, and in different embodiments, the user can adjust the values of the parameters to realize the noise reduction effect of the adjustment algorithm.
The values of the five parameters, th_low, th_mid, th_high, t1 and t2, all need to be adjusted according to the bit width of the initial image according to experience. For example, taking an 8-bit width as an example, the default values of th_low, th_mid, and th_high may be 100, 280, and 1200, respectively, and the default values of t1 and t2 may be 5000 and 10000, respectively.
S140: and carrying out noise reduction processing on the image blocks to be noise reduced based on the noise reduction threshold value corresponding to each image block to be noise reduced so as to obtain corresponding image blocks to be noise reduced.
After the noise reduction threshold corresponding to the image to be reduced is obtained in step S130, based on the noise reduction threshold corresponding to each image block to be reduced, noise reduction processing is performed on each image block to be reduced according to the noise reduction threshold corresponding to each image block to be reduced. In an embodiment, the set frequency domain transform method may be used to perform the noise reduction processing on the image block to be noise reduced, and for details of the noise reduction processing, reference may be made to the following description of the embodiment corresponding to fig. 5.
S150: and fusing the obtained noise-reduced image blocks to obtain a noise-reduced image.
After each image block to be noise-reduced in the initial image is noise-reduced and the noise-reduced image block is obtained, the obtained noise-reduced image blocks are fused according to a set rule to obtain a noise-reduced image, and then the noise reduction of the initial image is completed.
In the embodiment corresponding to fig. 1, after an initial image is acquired and a plurality of reference pixel points are determined in the initial image, the noise value of each reference pixel point in the initial image is calculated respectively, then according to the obtained noise value of the area where each reference pixel point is located, an image block to be noise-reduced containing the reference pixel point is selected correspondingly in the initial image, and according to the noise value of the area where each image block to be noise-reduced is located, the noise reduction threshold corresponding to the current image block to be noise-reduced is acquired, so that the noise reduction processing of different noise reduction thresholds is carried out on the image block to be noise-reduced where the reference pixel points are located according to the noise values of the areas where different reference pixel points are located.
Further, determining a plurality of reference pixel points in the initial image in step S110 includes: and searching a plurality of reference pixel points in the initial image according to a preset step length. The preset step length can be set and adjusted by a user according to the experience value.
Furthermore, a value range of the preset step length is set for the user to set and adjust the preset step length in the set range. If the preset step length value ranges are 1,2 and 3, the user selects the required step length in the preset value range according to the needs.
Referring to fig. 2 and fig. 3, fig. 2 and fig. 3 are schematic views illustrating reference pixel selection in an embodiment of a method for image noise reduction according to the present application. In one embodiment, given an edge image of size wb×hb (where wb is the edge image width, hb is the edge image height, and reference is made to the following description for edge details), taking reference pixel search step s=3 as an example, the calculation process of the reference pixel coordinate index array is as follows (d is the image edge width):
① Defining two one-dimensional arrays for storing the row and column coordinate index values of the reference pixel points respectively;
② The row coordinate range 0, hb-d is traversed with 0 as an initial point and s as a step. For point i traversed each time, its value is added to the row coordinate index array, the specific process is shown in fig. 2.
③ Similarly, the column coordinate range [0, wb-d ] is traversed with 0 as an initial point and s as a step size. For each traversed point j, its value is added to the column coordinate index array, as shown in FIG. 3.
④ And traversing the initial image according to the reference pixel row coordinate index array and the reference pixel column coordinate index array to obtain all the reference pixel points, and obtaining an image block to be noise reduced by taking the traversed current reference pixel point as a vertex. As described above, the size of the acquired image block is determined according to the noise value of the area where the current reference pixel point is located. Referring to fig. 4, fig. 4 is a schematic diagram illustrating an image block acquisition process corresponding to a reference pixel point in an embodiment of a method for image noise reduction according to the present application. As illustrated in fig. 4, the current reference pixel P is taken as the vertex, and the image block to be noise-reduced with the size of 8×8 is selected by taking P as the vertex as illustrated in fig. 4 according to the given preset noise range where the noise value of the region where the reference pixel is located is determined to be 8×8.
Referring to fig. 5, fig. 5 is a flowchart illustrating a method for image denoising according to another embodiment of the present application. The step S140 performs noise reduction processing on the image block to be noise reduced based on the noise reduction threshold corresponding to each image block to be noise reduced to obtain a corresponding image block to be noise reduced, which in the present embodiment includes:
S501: and carrying out frequency domain transformation on each image block to be noise reduced so as to obtain a corresponding frequency domain block.
Firstly, carrying out frequency domain transformation on each image block to be noise reduced by adopting a preset frequency domain transformation method so as to obtain a corresponding frequency domain block. The frequency domain transformation method at least comprises the following steps: discrete cosine transform, discrete sine transform, and the like, are not limited thereto.
Further, step S501 includes: and carrying out frequency domain transformation on each image block to be noise reduced by adopting 2D Walsh Hadamard transformation so as to obtain a corresponding frequency domain block. In the current embodiment, selecting 2D walsh hadamard transforms can better reduce the complexity of algorithm operations, while also reducing the resource requirements for hardware, so that the algorithm can be applied to hardware that does not support floating point operations.
S502: and judging whether the absolute value of the pixel value of each pixel point in the frequency domain block is smaller than a noise reduction threshold value corresponding to the image block to be noise reduced.
After each image block to be noise-reduced is subjected to frequency domain transformation to obtain a corresponding frequency domain block, whether the absolute value of the pixel value of each pixel point in each frequency domain block is smaller than a noise reduction threshold value corresponding to each image block to be noise-reduced is further judged.
S503: and adjusting the pixel value of the pixel point to zero.
If the absolute value of the pixel point is smaller than the noise reduction threshold value corresponding to the image block to be noise reduced where the pixel point is located, the pixel value of the pixel point is adjusted to be zero.
Otherwise, if the pixel value of the pixel point in the frequency domain block is larger than or equal to the noise reduction threshold value corresponding to the image block to be noise reduced where the pixel point is located, the pixel value of the pixel point is kept unchanged. After each pixel point in the frequency domain block is determined to obtain the adjusted frequency domain block, the following step S504 is performed.
S504: and carrying out inverse transformation of frequency domain transformation on the adjusted frequency domain block to obtain a corresponding noise-reduced image block.
And carrying out inverse transformation on the corresponding frequency domain transformation on the adjusted frequency domain block to obtain a corresponding noise-reduced image block.
As described above, if the 2D walsh hadamard transform is used in step S501 to perform the frequency domain transform on each image block, then the 2D walsh hadamard inverse transform is used in step S504 to obtain the corresponding noise reduced image block.
Taking the frequency domain transform method as an example of 2D walsh hadamard transform, assuming that the current image block size to be noise reduced is n×n (n=2 n, n=2, 3, 4), the 2D walsh hadamard transform calculation formula is as follows:
In the above formula, the calculation formula of the transformation kernel g (x, u, y, v) is as follows:
Where bi (z) represents the value of the i+1th bit of the z binary representation, u and v represent coordinates in the converted frequency domain, and z is used to represent x, y, u and v.
And for the 2D walsh hadamard transform, which is similar to the 2D walsh hadamard forward transform, the calculation formula is as follows:
the definition of the transform kernel g (x, u, y, v) in the above equation is consistent with the computation in the 2D walsh hadamard transform, and the computation equation is not repeated here.
Referring to fig. 6, fig. 6 is a flowchart illustrating a method for image denoising according to another embodiment of the present application. In the present embodiment, the step S150 fuses the obtained noise-reduced image blocks to obtain a noise-reduced image, which includes:
s601: and assigning pixel values of all pixel points in the noise-reduced image block to corresponding pixel points in the initial image so as to obtain the initial noise-reduced image.
After the noise reduction processing is performed on the image block to be noise reduced, pixel values of all pixel points in the noise reduced image block are correspondingly assigned to corresponding pixel points in the initial image, and then the initial noise reduced image is obtained. If the initial image has 9 image blocks to be denoised, after the image blocks to be denoised are denoised, the pixel values of all the pixel points in the denoised image obtained after the denoising processing are correspondingly assigned to the corresponding pixel points in the initial image, namely, the pixel values of the pixel points in the 9 denoised images are used for replacing the pixel values of the corresponding pixel points in the initial image, so that the initial denoised image is obtained.
Further, in an embodiment, the step S601 assigns pixel values of each pixel point in the noise-reduced image block to corresponding pixel points in the initial image to obtain an initial noise-reduced image, including: and if the at least two noise-reduced image blocks contain the same pixel point, respectively acquiring pixel values of the pixel point in the at least two noise-reduced image blocks.
After the pixel values of the pixel points in at least two noise-reduced image blocks are respectively obtained, determining the final pixel value of the pixel points based on the pixel values of the pixel points in the at least two noise-reduced image blocks, and assigning the final pixel value to the corresponding pixel point in the initial image. For example, in one embodiment, the pixel q is in the three noise-reduced image blocks A1, A2 and A3 at the same time, then the pixel values of the pixel q in the noise-reduced image blocks A1, A2 and A3 are respectively obtained, and then the average value of the obtained three pixel values is obtained as the final pixel value of the pixel q and assigned to the pixel q. In another embodiment, a pixel value of the pixel point with a high probability in at least two noise-reduced image blocks may be selected as a final pixel value of the pixel point, and the final pixel value obtained is assigned to a corresponding pixel point in the initial image.
Further, referring to fig. 7, fig. 7 is a schematic diagram illustrating an edge extension process for an initial image according to an embodiment of the present application. In an embodiment, before determining a plurality of reference pixel points in the initial image, the method provided by the present application further includes: and carrying out edge rubbing treatment on the initial image according to a set rule so as to increase an edge rubbing area for each edge of the initial image.
Specifically, the data of each channel is subjected to edge development. Taking an initial image a with an image size of w×h as an example, where w is an initial image width, h is an initial image height, and assuming that the width of the image edge is d, an edge image B with a size of wb×hb is calculated (where wb=w+2d is an edge image width, and hb=h+2d is an edge image height), the steps are as follows:
For the region with the middle size of w×h in the edge-rubbing image, the pixels of the initial image are used for one-to-one assignment, and the calculation process of the pixels in the central region of the edge-rubbing image is shown in fig. 7 (a). For this purpose, the initial image is traversed, the column-row coordinate index value of the accessed current pixel is recorded, then the index position of the current pixel in the topology image is calculated according to the column-row coordinate index value, and the value of the current pixel is assigned to the pixel in the topology image. Here, d is set according to the size of the initial image, and is not particularly limited herein. E.g. d may take a width of 8 pixels.
The pixel values of the pixel points of the edge-rubbing region in the edge-rubbing image can be assigned in a mirror assignment manner, for example, when the pixel values of the i-th row (i=0, 1,2,3,..d-1) in the edge-rubbing image are assigned one by using the pixel values of the 2 d-i-th row in the edge-rubbing image, and the process is shown in (b) in fig. 7.
Similarly, for pixels in lines hb-i (i=1, 2,., d) in the edge image, the pixels in lines hb+i-2d-2 in the edge image are used for one-to-one assignment, as shown in (c) of fig. 7.
As shown in (d) of fig. 7, for the pixel of the i-th column (i=0, 1,2,3,.,. D-1) in the topology edge image, the pixels of the 2 d-i-th column in the topology edge image are used for assignment one by one.
As illustrated in (f) of fig. 7, for the pixels of the (i=1, 2,., d) th column of the topology edge image, the pixels of the (wb+i-2 d-2) th column of the topology edge image are used for one-to-one assignment.
In the present embodiment, when the above edge extension processing is further performed on the initial image according to the set rule before determining the plurality of reference pixel points, the corresponding pixel values of each pixel point in the noise-reduced image block are assigned to the corresponding pixel point in the initial image in step S601, so as to obtain the initial noise-reduced image, and then the method provided by the present application further includes: and cutting out a corresponding edge rubbing region in the initial noise reduction image, and further reserving a part corresponding to the initial image in the initial noise reduction image.
In the embodiment corresponding to fig. 7, by performing edge extension processing on the initial image, better noise reduction processing on the edge area of the initial image can be better realized, so that the overall effect of image noise reduction is better improved.
S602: and determining fusion weights corresponding to the pixel points according to the pixel values of the pixel points in the assigned initial noise reduction image.
After the noise-reduced image is obtained, the fusion weight corresponding to each pixel point is further determined according to the pixel value of each pixel point in the assigned initial noise-reduced image, so that the initial noise-reduced image and the initial image are fused according to the pixel value of each pixel point, and a final noise-reduced image is obtained.
Further, referring to fig. 8, fig. 8 is a flowchart illustrating a method for image denoising according to another embodiment of the present application. The embodiment corresponding to fig. 8 mainly illustrates the process of determining the fusion weights. Specifically, step S602 includes:
s801: the maximum pixel value in the initial noise reduction image is obtained.
After the initial noise reduction image is obtained, the maximum pixel value in all pixel points in the initial noise reduction image is obtained first. In an embodiment, in the method provided by the application, in a process of assigning pixel values of each pixel point in the noise-reduced image block to corresponding pixel points in the initial image, a maximum pixel value in the initial noise-reduced image may be directly obtained according to a latest assignment result of each pixel point.
S802: at least a first pixel threshold and a second pixel threshold are determined between zero and a maximum pixel value.
Wherein the first pixel threshold is less than the second pixel threshold and less than a maximum pixel value in the initial noise reduction image. The first pixel threshold and the second pixel threshold may be determined according to a maximum pixel value in the initial noise reduction image, for example, a set proportion of the maximum pixel value in the initial noise reduction image may be selected as the first pixel threshold and the second pixel threshold according to an empirical value. In another embodiment, the first pixel threshold and the second pixel threshold may be further determined according to a distribution probability of pixel values corresponding to each pixel point in the noise-reduced image, that is, specific values of each pixel threshold are determined according to a percentage of an interval where each pixel value is located, for example, between zero and a maximum value of the pixel values, an end value corresponding to the first fifteen percent of the small and large distributions of the pixel values may be selected as the first pixel threshold, and a right end value corresponding to the fifteen percent to the eighty percent of the small and large distributions of the pixel values may be selected as the second pixel threshold.
It will be appreciated that, in different embodiments, a plurality of pixel thresholds may be set according to actual needs, for example, 4 pixel thresholds may be set, and when a plurality of pixel thresholds are set, a mapping function between each pixel threshold and a corresponding preset ratio may be further obtained according to the set pixel threshold, which may be specifically described in the corresponding embodiments below.
S803: and determining a mapping function between the fusion weight and the pixel value according to the first pixel threshold value, the first preset ratio, the second pixel value threshold value and the second preset ratio.
Specifically, a mapping function between the fusion weight and the pixel value may be obtained by fitting or calculating according to the first pixel threshold, the first preset ratio, the second pixel threshold and the second preset ratio, so as to determine the mapping function between the fusion weight and the pixel value.
S804: and determining the fusion weight corresponding to the pixel point in the initial noise reduction image according to the mapping function.
And respectively determining the fusion weights corresponding to the pixel points according to the mapping function between the fusion weights and the pixel values determined in the step S803 and the pixel value corresponding to each pixel in the initial noise reduction image.
For image fusion, the core idea is to make up for the loss of detail information by introducing initial image information. In one embodiment, assuming that the initial image is ori, the initial noise reduction image is den, and the final noise reduction image is res, there is: res=α×den+ (1- α) ×ori.
In the above formula, α is a fusion weight, and the calculation method of the fusion weight value α is shown in fig. 9.
Fig. 9 is a schematic diagram of mapping relationships corresponding to fusion weights and pixel values in an embodiment of a method for image denoising according to the present application. In fig. 9, α0, α1, α2, p1, p2, p3, p4, and p5 are algorithm parameters, and p3 is the maximum pixel value in the initial noise reduction image, which may specifically be changed according to the input image. Wherein p3 may be the maximum pixel value in the initial image in another embodiment.
In the method provided by the application, the noise of the bright and dark areas of the image is considered to be less, so that the information of the initial image is focused more in the process of denoising the image, and more initial images are taken when the images are fused; for other areas, the information of the initial noise reduction image is focused more, more initial noise reduction images are obtained when the images are fused, better noise reduction processing is further achieved on the images, meanwhile, the loss of details of the image data due to noise reduction is avoided, and the accuracy of the images is guaranteed. In an actual scene, noise reduction processing for different scene images can be achieved by adjusting and controlling α0, α1, α2 and the values of the corresponding pixel thresholds (the pixel thresholds in the current embodiment include p1, p2, p3, p4 and p 5).
In the embodiment illustrated in fig. 9, after the maximum pixel value is obtained in the initial noise reduction image, α0, α1, α2, p1, p2, p3, p4, and p5 are further determined, then mapping functions between the fusion weights of the pixel values from 0 to p1 and between the pixel values, and mapping functions between the fusion weights of the pixel values from p1 to p4, between p4 and p5, between p5 and p2, and between p2 and p3 and between the pixel values are respectively obtained, and then the fusion weights corresponding to the respective pixel points in the initial noise reduction image are respectively determined according to the respective mapping functions. In an embodiment, α0, α1, and α2 are corresponding empirical values selected according to the noise value of the image, and may be adjusted according to actual needs, for example, in the present embodiment, α0, α1, and α2 may be default values of 0.1, 0.3, and 0.8, and values of p1, p2, p4, and p5 may be determined according to the maximum pixel value p3 of the image, for example, p1=0.15×p3, p2=0.85×p3, p4=0.25×p3, and p5=0.75×p3.
S603: and fusing the initial noise reduction image and the initial image by utilizing the fusion weight to obtain a noise reduction image.
After determining the fusion weights corresponding to the pixel points, the initial noise reduction image and the initial image are fused according to the determined fusion weights, so as to obtain the noise reduction image.
Further, the step S603 above fuses the initial noise reduction image and the initial image by using the fusion weight to obtain a noise reduction image, which includes: the initial noise-reduced image and the initial image are fused using the following formula to obtain a noise-reduced image,
Wherein a is the fusion weight,For the pixel value of the i-th pixel point in the noise-reduced image,For the pixel value of the i-th pixel point in the initial noise reduction image,Is the pixel value of the i-th pixel point in the initial image.
Referring to fig. 10, fig. 10 is a flowchart illustrating a method for image denoising according to another embodiment of the present application.
Step S140 is based on the noise reduction threshold value corresponding to each image block to be noise reduced, and after performing noise reduction processing on the image block to be noise reduced to obtain a corresponding image block to be noise reduced, the method further includes:
s1001: and acquiring pixel values of pixel points simultaneously belonging to the plurality of noise-reduced image blocks in the plurality of noise-reduced image block image blocks.
Since different image blocks to be noise reduced may include the same pixel points at the same time when the image block to be noise reduced is selected, in the present embodiment, after the noise reduction processing is performed on the image block to be noise reduced to obtain the noise reduced image block, the pixel values of the pixels belonging to the multiple noise reduced image blocks in the multiple noise reduced image blocks at the same time are further obtained.
S1002: and determining a final pixel value of the pixel point based on the pixel values of the pixel point in the image blocks of the plurality of noise-reduced image blocks, and assigning the final pixel value to the corresponding pixel point in the plurality of noise-reduced image blocks.
After obtaining the pixel value of the pixel point in the image blocks of the noise-reduced image blocks, determining the average value of the pixel values or the majority value of the pixel values as the final pixel value of the pixel point, and respectively assigning the final pixel value to the corresponding pixel point in the noise-reduced image blocks, so that when the image blocks are spliced and fused, any value in the noise-reduced image blocks simultaneously comprising the pixel point is selected as the pixel value of the pixel point.
Referring to fig. 11, fig. 11 is a flowchart illustrating a method for image denoising according to another embodiment of the present application. In the present embodiment, the step S150 of fusing the obtained noise-reduced image blocks to obtain a noise-reduced image includes:
S1101: and determining the fusion weight corresponding to the pixel point according to the pixel value of the pixel point in the noise-reduced image block, and fusing the noise-reduced image block and the image block to be noise-reduced according to the fusion weight ratio so as to obtain a final noise-reduced image block.
In the present embodiment, the noise-reduced image block and the image to be noise-reduced are fused first to obtain a final noise-reduced image block, and then step S1102 is performed to aggregate the final noise-reduced image block to obtain a noise-reduced image. The determination of the fusion weight α is the same as that of the embodiment corresponding to fig. 8, and is not described herein.
Further, referring to fig. 12, fig. 12 is a flowchart illustrating a method for image denoising according to another embodiment of the present application. In the present embodiment, the determining the fusion weight corresponding to the pixel point according to the pixel value of the pixel point in the noise-reduced image block in the step S1101 further includes:
s1201: the maximum pixel value in the denoised image block is obtained.
The maximum pixel value in the current denoised image block is obtained.
S1202: a first pixel threshold and a second pixel threshold are determined between zero and the maximum pixel value, the first pixel threshold being less than the second pixel threshold.
S1203: and determining a mapping function between the fusion weight and the pixel value according to the first pixel threshold value, the first preset ratio, the second pixel threshold value and the second preset ratio.
S1204: and determining the fusion weight corresponding to the pixel point in the noise-reduced image block according to the mapping function. And determining the fusion weight corresponding to each pixel point in the noise reduction image block according to the obtained mapping function, so as to fuse the noise reduction image block and the image block to be noise reduction according to the fusion weight.
In the embodiment corresponding to fig. 12, the method for obtaining the fusion weight ratio in the embodiment that the image blocks are fused by taking the image block as a unit after the image block to be noise reduced is obtained after the noise reduction processing is performed on the image block to be noise reduced is described. In the current embodiment, since the number of pixels included in the image block is smaller than that of the complete noise-reduced image, a smaller number of pixel thresholds may be selected, then a mapping function between the fusion weights and the pixel values is obtained, and finally the fusion weights of the pixels may be determined according to the obtained mapping function. The content of steps S1201 to S1204 in the present embodiment may also be correspondingly described with reference to the corresponding portion of fig. 8, and will not be repeated.
Further, in an embodiment, if the image blocks are fused, the image blocks to be noise reduced and the noise reduced image blocks with smaller size (i.e. the image blocks including fewer pixels) may be directly fused according to a preset fusion weight.
Still further, in another embodiment, when the image blocks are fused, for the image blocks smaller than or equal to the preset size, the corresponding preset fusion weights may be directly determined according to the average pixel values of the image blocks. The preset fusion weight is an empirical value obtained through testing.
The step S1101 of fusing the image block subjected to noise reduction and the image block to be noise reduced according to the fusion weight ratio to obtain a final noise reduction image block, further includes: and fusing the image block subjected to noise reduction and the image block to be subjected to noise reduction by using the following formula so as to obtain a final noise reduction image block.
Wherein alpha is the fusion weight,For the pixel value of the i-th pixel point in the final noise-reduced image block,For the pixel value of the i-th pixel point in the denoised image block,The determination of the fusion weight α is the same as that of the embodiment corresponding to fig. 8, and is not described herein.
S1102: and aggregating the final noise reduction image blocks to obtain a noise reduction image.
The step of aggregating the final noise reduction image blocks means that the final noise reduction image blocks are simply aggregated according to pixel coordinates of the final noise reduction image blocks, so as to obtain a noise reduction image.
Referring to fig. 13, fig. 13 is a schematic structural diagram of an image noise reduction device according to an embodiment of the application. In the current embodiment, the image noise reduction apparatus 1300 provided by the present application includes a processor 1301 and a memory 1302 coupled. The image denoising apparatus 1300 may perform the image denoising method described in any one of fig. 1 to 12 and the corresponding embodiments.
The memory 1302 includes a local storage (not shown) and stores a computer program that, when executed, implements the methods described in any of the embodiments of fig. 1-12 and corresponding thereto.
A processor 1301 is coupled to the memory 1302, the processor 1301 being configured to run a computer program to perform the method of image noise reduction as described in any of the above fig. 1 to 12 and their corresponding embodiments.
Further, in another embodiment, the image denoising apparatus 1300 further includes a communication circuit (not shown) connected to the processor 1301 and configured to perform data interaction with an external terminal device under the control of the processor 1301 to obtain initial image data or instruction data. The instruction data at least comprises a computer program upgrading instruction and a data packet required by the computer program upgrading.
Referring to fig. 14, fig. 14 is a schematic structural diagram of a memory device according to an embodiment of the application. The storage 1400 stores a computer program 1401 capable of being executed by a processor, the computer program 1401 being adapted to implement the method of image denoising as described in any of the embodiments of fig. 1 to 12 and corresponding thereto. Specifically, the storage 1400 may be one of a memory, a personal computer, a server, a network device, a usb disk, and the like, which is not limited in any way.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (14)
1. A method of image denoising, comprising:
acquiring an initial image, and determining a plurality of reference pixel points in the initial image;
respectively calculating the noise value of the area where each reference pixel point is located in the initial image;
Selecting an image block to be noise-reduced containing the reference pixel points from the initial image according to the noise value of the area where each reference pixel point is located, and acquiring a noise reduction threshold value corresponding to the image block to be noise-reduced;
Based on the noise reduction threshold value corresponding to each image block to be noise reduced, carrying out noise reduction processing on the image block to be noise reduced so as to obtain a corresponding image block with noise reduced;
Fusing the obtained noise-reduced image blocks to obtain a noise-reduced image;
the distance between the adjacent reference pixel points is smaller than or equal to the size of the image block to be noise reduced;
Selecting an image block to be noise reduced containing the reference pixel points from the initial image according to the noise value of the area where each reference pixel point is located, and acquiring a noise reduction threshold corresponding to the image block to be noise reduced, wherein the noise reduction threshold comprises:
determining a preset noise range of a noise value of an area where the reference pixel point is located, selecting an image block to be noise-reduced, which contains the reference pixel point and has a preset block size corresponding to the preset noise range, from the initial image, and acquiring a noise reduction threshold corresponding to the preset noise range as a noise reduction threshold corresponding to the image block to be noise-reduced; the larger the noise in the preset noise range is, the larger the corresponding preset block size is, and the larger the corresponding noise reduction threshold is.
2. The method according to claim 1, wherein the upper and/or lower limit of the preset noise range is determined according to the bit width of the initial image;
The number of the preset noise ranges is greater than or equal to three.
3. The method of claim 1, wherein the image block to be noise reduced is an image block having a vertex corresponding to the reference pixel point;
The determining a plurality of reference pixel points in the initial image comprises the following steps:
Searching a plurality of reference pixel points in the initial image according to a preset step length;
the calculating the noise value of the area where each reference pixel point is located in the initial image includes:
Determining an area taking a reference pixel point as a vertex and the size of the area is the size of a preset area, and obtaining the noise value of the area where the reference pixel point is located based on the pixel value of the pixel point in the area.
4. The method according to claim 1, wherein the denoising processing of the image blocks to be denoised based on the denoising threshold value corresponding to each image block to be denoised to obtain corresponding denoised image blocks, includes:
carrying out frequency domain transformation on each image block to be noise reduced so as to obtain a corresponding frequency domain block;
Judging whether the absolute value of the pixel value of each pixel point in the frequency domain block is smaller than the noise reduction threshold value corresponding to the image block to be noise reduced;
if yes, the pixel value of the pixel point is adjusted to be zero;
And carrying out inverse transformation on the frequency domain transformation on the adjusted frequency domain block so as to obtain a corresponding noise-reduced image block.
5. The method of claim 4, wherein said performing a frequency domain transform on each of said image blocks to obtain a corresponding frequency domain block comprises:
And carrying out frequency domain transformation on each image block by adopting Walsh Hadamard transformation so as to obtain a corresponding frequency domain block.
6. The method of claim 1, wherein the fusing the obtained denoised image blocks to obtain a denoised image comprises:
assigning pixel values of all pixel points in the noise-reduced image block to corresponding pixel points in the initial image to obtain an initial noise-reduced image;
Determining fusion weights corresponding to all the pixel points according to the assigned pixel values of all the pixel points in the initial noise reduction image;
and fusing the initial noise reduction image and the initial image by utilizing the fusion weight so as to obtain the noise reduction image.
7. The method of claim 6, wherein determining the fusion weights corresponding to the pixels according to the assigned pixel values of the pixels in the initial noise reduction image comprises:
obtaining a maximum pixel value in the initial noise reduction image;
Determining at least a first pixel threshold and a second pixel threshold between zero and the maximum pixel value, wherein the first pixel threshold is less than the second pixel threshold;
Determining a mapping function between the fusion weight and the pixel value according to the first pixel threshold, the first preset ratio, the second pixel value threshold and the second preset ratio;
determining the fusion weight corresponding to the pixel point in the initial noise reduction image according to the mapping function;
The fusing the initial noise reduction image and the initial image by using the fusion weight to obtain the noise reduction image includes:
Fusing the initial noise reduction image and the initial image using the following formula to obtain the noise reduction image,
Wherein, In order to fuse the weights, the weights are,For the pixel value of the i-th pixel point in the noise-reduced image,For the pixel value of the i-th pixel point in the initial noise reduction image,Is the pixel value of the i-th pixel point in the initial image.
8. The method of claim 6, wherein prior to determining a number of reference pixels in the initial image, the method further comprises:
Performing edge extension processing on the initial image according to a set rule, so that an edge extension area is externally added on each edge of the initial image;
After assigning the pixel value of each pixel point in the noise-reduced image block to the corresponding pixel point in the initial image to obtain the initial noise-reduced image, the method further includes:
and cutting out a corresponding edge rubbing area in the initial noise reduction image.
9. The method of claim 7, wherein assigning pixel values of pixels in the denoised image block to corresponding pixels in the initial image to obtain an initial denoised image comprises:
if at least two of the noise-reduced image blocks contain the same pixel point, respectively acquiring pixel values of the pixel point in at least two of the noise-reduced image blocks;
and determining a final pixel value of the pixel point based on the pixel values of the pixel point in at least two noise-reduced image blocks, and assigning the final pixel value to the corresponding pixel point in the initial image.
10. The method according to claim 1, wherein after performing noise reduction processing on the image block to be noise reduced based on the noise reduction threshold value corresponding to each image block to be noise reduced to obtain a corresponding image block to be noise reduced, the method further comprises:
Acquiring pixel values of pixel points belonging to a plurality of noise-reduced image blocks in the noise-reduced image blocks;
and determining a final pixel value of the pixel point based on the pixel values of the pixel point in the plurality of noise-reduced image block image blocks, and assigning the final pixel value to the corresponding pixel point in the plurality of noise-reduced image blocks.
11. The method of claim 1, wherein the fusing the obtained denoised image blocks to obtain a denoised image comprises:
determining a fusion weight corresponding to a pixel point in the noise-reduced image block according to the pixel value of the pixel point, and fusing the noise-reduced image block and the image block to be noise-reduced according to the fusion weight ratio to obtain a final noise-reduced image block;
and aggregating the final noise reduction image blocks to obtain the noise reduction image.
12. The method of claim 11, wherein determining the fusion weight corresponding to the pixel point according to the pixel value of the pixel point in the denoised image block comprises:
Obtaining a maximum pixel value in the noise-reduced image block;
determining a first pixel threshold and a second pixel threshold between zero and the maximum pixel value, the first pixel threshold being less than the second pixel threshold;
determining a mapping function between the fusion weight and the pixel value according to the first pixel threshold, the first preset ratio, the second pixel threshold and the second preset ratio;
Determining the fusion weight corresponding to the pixel point in the noise-reduced image block according to the mapping function;
The step of fusing the noise-reduced image block and the image block to be noise-reduced according to the fusion weight ratio to obtain the final noise-reduced image block comprises the following steps:
Fusing the denoised image block and the image block to be denoised using the following formula to obtain the final denoised image block,
;
Wherein, In order to fuse the weights, the weights are,For the pixel value of the i-th pixel point in the final noise-reduced image block,For the pixel value of the i-th pixel point in the denoised image block,And the pixel value of the ith pixel point in the image block to be noise reduced.
13. An apparatus for image noise reduction, comprising a memory and a processor coupled, wherein,
The memory includes local storage and stores a computer program;
The processor is configured to run the computer program to perform the method of any one of claims 1 to 12.
14. A storage device storing a computer program executable by a processor for implementing the method of any one of claims 1-12.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010292363.2A CN113536214B (en) | 2020-04-14 | 2020-04-14 | Image noise reduction method and device and storage device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010292363.2A CN113536214B (en) | 2020-04-14 | 2020-04-14 | Image noise reduction method and device and storage device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113536214A CN113536214A (en) | 2021-10-22 |
| CN113536214B true CN113536214B (en) | 2024-10-18 |
Family
ID=78119993
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010292363.2A Active CN113536214B (en) | 2020-04-14 | 2020-04-14 | Image noise reduction method and device and storage device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113536214B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787902A (en) * | 2016-03-22 | 2016-07-20 | 天津大学 | Image noise reduction method which utilizes partitioning ordering to detect noise |
| CN110689496A (en) * | 2019-09-25 | 2020-01-14 | 北京迈格威科技有限公司 | Method and device for determining noise reduction model, electronic equipment and computer storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102682429A (en) * | 2012-04-13 | 2012-09-19 | 泰山学院 | De-noising method of filtering images in size adaptive block matching transform domains |
| TWI573096B (en) * | 2013-12-31 | 2017-03-01 | 智原科技股份有限公司 | Method and apparatus for estimating image noise |
| CN105654442B (en) * | 2015-12-31 | 2019-01-15 | 大连理工大学 | A Method for Image Denoising with Shock Noise |
| CN107248148B (en) * | 2017-06-14 | 2023-08-25 | 思特威(上海)电子科技股份有限公司 | Image noise reduction method and system |
| US20190188829A1 (en) * | 2017-12-14 | 2019-06-20 | Multitek Inc. | Method, Apparatus, and Circuitry of Noise Reduction |
| CN110490824A (en) * | 2019-08-23 | 2019-11-22 | 广东浪潮大数据研究有限公司 | The method and device of image denoising |
-
2020
- 2020-04-14 CN CN202010292363.2A patent/CN113536214B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787902A (en) * | 2016-03-22 | 2016-07-20 | 天津大学 | Image noise reduction method which utilizes partitioning ordering to detect noise |
| CN110689496A (en) * | 2019-09-25 | 2020-01-14 | 北京迈格威科技有限公司 | Method and device for determining noise reduction model, electronic equipment and computer storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113536214A (en) | 2021-10-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kim et al. | Optimized contrast enhancement for real-time image and video dehazing | |
| US9483835B2 (en) | Depth value restoration method and system | |
| JP7030493B2 (en) | Image processing equipment, image processing methods and programs | |
| US20180122051A1 (en) | Method and device for image haze removal | |
| KR20180105876A (en) | Method for tracking image in real time considering both color and shape at the same time and apparatus therefor | |
| CN104794685B (en) | A kind of method and device for realizing image denoising | |
| US20120321139A1 (en) | Content-adaptive systems, methods and apparatus for determining optical flow | |
| CN112734654B (en) | Image processing method, device, equipment and storage medium | |
| US9449371B1 (en) | True motion based temporal-spatial IIR filter for video | |
| CN105243371A (en) | Human face beauty degree detection method and system and shooting terminal | |
| CN111598796A (en) | Image processing method and device, electronic device and storage medium | |
| CN112149672B (en) | Image processing method and device, electronic equipment and storage medium | |
| US11526963B2 (en) | Image processing apparatus, image processing method, and storage medium | |
| CN108335267A (en) | A kind of processing method of depth image, device, equipment and storage medium | |
| US8995755B2 (en) | Two-dimensional to stereoscopic conversion systems and methods | |
| Khmag | Image dehazing and defogging based on second-generation wavelets and estimation of transmission map | |
| CN113536214B (en) | Image noise reduction method and device and storage device | |
| CN108346128B (en) | Method and device for beautifying and peeling | |
| US20170150014A1 (en) | Method and electronic device for video denoising and detail enhancement | |
| CN110895789B (en) | Face beautifying method and device | |
| CN103618904B (en) | Motion estimation method and device based on pixels | |
| Zain et al. | Hybrid singular value decomposition based alpha trimmed mean-median filter in eliminating high density salt and pepper noise from grayscale image | |
| CN116757952A (en) | Image enhancement model training method, image enhancement method and related device | |
| CN110363723B (en) | Image processing method and device for improving image boundary effect | |
| CN114529828A (en) | Method, device and equipment for extracting residential area elements of remote sensing image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |