CN108810320B - Image quality improving method and device - Google Patents
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Abstract
The embodiment of the invention provides a method and a device for improving image quality. The method comprises the following steps: acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map; denoising the brightness component diagram according to the denoising histogram to obtain a denoising component diagram; acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component diagram and the brightness component diagram; and according to the correction coefficient of each pixel point, carrying out noise reduction on the original image to obtain a noise-reduced image. The image quality improving method and the image quality improving device provided by the embodiment of the invention have the advantages of less calculated amount and high processing speed.
Description
Technical Field
The present invention relates to image processing technologies, and in particular, to a method and an apparatus for improving image quality.
Background
With the continuous innovation and rapid development of science and technology, video images as important information sources for human recognition and exploration are spread in various subjects and fields, such as traffic monitoring, medical assistance, night driving, movie production and the like. However, during the shooting and transmission of the video images, noise is inevitably generated under the influence of shooting environments and shooting equipment, and the quality of the video images is affected. In particular, when a video image is subjected to processing such as contrast enhancement and feature extraction, noise is amplified, so that video image noise reduction is the basis of improving the quality of the video image, and video image noise reduction is becoming a research hotspot in the field of image processing.
To improve the quality of video images, methods for improving the performance of the photographing apparatus, such as using a large photosensitive element and a large lens aperture, may be used, but the method is costly and the degree of image noise reduction is insignificant at night or in other poor lighting conditions. In order to avoid the above problems, in the prior art, a filter (such as a median filter, an average filter, a wiener filter, etc.) is usually adopted to perform noise reduction processing on pixel points in a video image one by one, so as to reduce image noise. However, the method of reducing image noise by using the filter has the problems of large calculation amount, complex algorithm, low processor speed and the like,
disclosure of Invention
The embodiment of the invention provides an image quality improving method and device, which are used for solving the problems of large calculated amount, complex algorithm, low processor speed and the like in the conventional image quality improving method.
In a first aspect, an embodiment of the present invention provides an image quality improving method, including:
acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map;
denoising the brightness component diagram according to the denoising histogram to obtain a denoising component diagram;
acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component map and the brightness component map;
and according to the correction coefficient of each pixel point, carrying out noise reduction on the original image to obtain a noise-reduced image.
In a possible implementation manner of the first aspect, the obtaining a noise reduction histogram corresponding to a luminance component map of an original image includes:
acquiring edge pixel points in the brightness component diagram;
acquiring the number of edge pixel points corresponding to each brightness value according to the brightness value of the edge pixel points;
and acquiring the noise reduction histogram according to the number of the edge pixel points corresponding to each brightness value.
In a possible implementation manner of the first aspect, the obtaining edge pixel points in the brightness component map includes:
traversing all pixel points in the brightness component map, and determining any pixel point in the brightness component map as an edge pixel point if the absolute value of the difference between the brightness value of the pixel point and the brightness value of the adjacent pixel point is greater than a preset difference;
and the adjacent pixel points are any pixel points in the brightness component diagram, wherein the distance between the adjacent pixel points and the pixel points is smaller than a preset distance.
In a feasible implementation manner of the first aspect, the obtaining, according to the noise reduction component map and the brightness component map, a correction coefficient of each pixel point in the original image includes:
acquiring a correction coefficient of a third pixel point according to the ratio of the brightness value of a first pixel point in the noise reduction component map to the brightness value of a second pixel point in the brightness component map;
the third pixel point is any pixel point in the original image, and the first pixel point is a pixel point which has the same coordinate with the third pixel point in the noise reduction component map; and the second pixel point is a pixel point which has the same coordinate with the third pixel point in the brightness component map.
In a possible implementation manner of the first aspect, the denoising the luminance component map according to the denoising histogram to obtain a denoising component map includes:
acquiring a normalized histogram of the noise reduction histogram, and acquiring a pixel mapping vector according to the normalized histogram and the differential matrix;
and acquiring the noise reduction component map according to the pixel mapping vector and the brightness component map.
In a feasible implementation manner of the first aspect, the denoising the original image according to the correction coefficient of each pixel point to obtain a denoised image includes:
correcting the value of each corresponding pixel point in the chromaticity component diagram of the original image according to the correction coefficient of each pixel point to obtain a chromaticity component diagram after noise reduction;
and obtaining the noise-reduced image according to the noise-reduced brightness component image and the noise-reduced chromaticity component image.
In a feasible implementation manner of the first aspect, the denoising the original image according to the correction coefficient of each pixel point to obtain a denoised image includes:
and according to the correction coefficient of each pixel point, correcting the value of the corresponding pixel point in the red component diagram, the green component diagram and the blue component diagram of the original image respectively to obtain the noise-reduced image.
In a possible implementation manner of the first aspect, after obtaining the noise reduction histogram corresponding to the luminance component map of the original image, the method further includes:
modifying the noise reduction histogram based on logarithmic mapping to obtain a modified histogram;
the denoising of the luminance component map according to the denoising histogram to obtain a denoising component map includes:
and denoising the brightness component diagram according to the corrected histogram to obtain a denoising component diagram.
In a second aspect, an embodiment of the present invention further provides an image quality improving apparatus, configured to perform the image quality improving method in any one of the possible implementations of the first aspect. An image quality improving apparatus comprising:
the noise reduction histogram acquisition module is used for acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map;
the noise reduction component map acquisition module is used for carrying out noise reduction on the brightness component map according to the noise reduction histogram to obtain a noise reduction component map;
a correction coefficient obtaining module, configured to obtain a correction coefficient of each pixel in the original image according to the noise reduction component map and the brightness component map;
and the noise reduction module is used for reducing the noise of the original image according to the correction coefficient of each pixel point to obtain a noise-reduced image.
In one possible embodiment of the second aspect,
the noise reduction histogram acquisition module includes: the device comprises an edge pixel point acquisition unit, an edge pixel point number acquisition unit and a noise reduction histogram acquisition unit;
the edge pixel point acquisition unit is used for acquiring edge pixel points in the brightness component diagram;
the edge pixel number obtaining unit is used for obtaining the number of edge pixels corresponding to each brightness value according to the brightness value of the edge pixels;
and the noise reduction histogram acquisition unit is used for acquiring the noise reduction histogram according to the number of the edge pixel points corresponding to each brightness value.
In a possible implementation manner of the second aspect, the edge pixel point obtaining unit is specifically configured to,
traversing all pixel points in the brightness component map, and determining any pixel point in the brightness component map as an edge pixel point if the absolute value of the difference between the brightness value of the pixel point and the brightness value of the adjacent pixel point is greater than a preset difference;
and the adjacent pixel points are any pixel points in the brightness component diagram, wherein the distance between the adjacent pixel points and the pixel points is smaller than a preset distance.
In a possible implementation manner of the second aspect, the correction coefficient obtaining module is specifically configured to,
acquiring a correction coefficient of a third pixel point according to the ratio of the brightness value of a first pixel point in the noise reduction component map to the brightness value of a second pixel point in the brightness component map;
the third pixel point is any pixel point in the original image, and the first pixel point is a pixel point which has the same coordinate with the third pixel point in the noise reduction component map; and the second pixel point is a pixel point which has the same coordinate with the third pixel point in the brightness component map.
In a possible implementation manner of the second aspect, the denoising component map obtaining module includes: a pixel mapping vector acquisition unit and a noise reduction component map acquisition unit;
the pixel mapping vector obtaining unit is used for obtaining a normalized histogram of the noise reduction histogram and obtaining a pixel mapping vector according to the normalized histogram and the differential matrix;
and the denoising component map obtaining unit is used for obtaining the denoising component map according to the pixel mapping vector and the brightness component map.
In a possible implementation of the second aspect, the noise reduction module comprises: the device comprises a correction unit and a noise-reduced image acquisition unit;
the correction unit is used for correcting the value of each corresponding pixel point in the chromaticity component diagram of the original image according to the correction coefficient of each pixel point to obtain the chromaticity component diagram after noise reduction;
and the image obtaining unit after noise reduction is used for obtaining an image after noise reduction according to the brightness component image after noise reduction and the chromaticity component image after noise reduction.
In a possible embodiment of the second aspect, the noise reduction module is particularly adapted to,
and according to the correction coefficient of each pixel point, correcting the value of the corresponding pixel point in the red component diagram, the green component diagram and the blue component diagram of the original image respectively to obtain the noise-reduced image.
In a possible implementation manner of the second aspect, the image quality improving apparatus further includes:
a modified histogram acquisition module, configured to modify the noise reduction histogram based on logarithmic mapping to obtain a modified histogram;
the noise reduction component map obtaining module is specifically configured to perform noise reduction on the luminance component map according to the modified histogram to obtain a noise reduction component map.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a program stored in the memory, where the program is configured to be executed by the processor, and the processor executes the program to implement the steps of the image quality improvement method in any possible implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a storage medium, where the storage medium stores a computer program, and the computer program, when executed by a processor, implements the steps of the image quality improvement method in any one of the possible implementations of the first aspect.
The image quality improving method and device provided by the embodiment of the invention comprise the following steps: acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map; denoising the brightness component diagram according to the denoising histogram to obtain a denoising component diagram; acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component diagram and the brightness component diagram; and according to the correction coefficient of each pixel point, carrying out noise reduction on the original image to obtain a noise-reduced image. The noise of the image is filtered by obtaining the noise reduction histogram of the brightness component map, and the image is restored according to the noise reduction histogram, so that the noise-reduced image is obtained, and the image quality is improved. Meanwhile, the mode of filtering the image noise adopts the mode of a noise reduction histogram, so that point-by-point noise reduction based on each pixel point is avoided, the calculated amount during image noise reduction is reduced, and the processing speed of image noise reduction is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an image quality improving method according to an embodiment of the present invention;
FIG. 2 is a diagram of a luminance component according to an embodiment of the present invention;
FIG. 3 is a histogram of the luminance component map of FIG. 2;
FIG. 4 is a noise reduction histogram of the luma component map of FIG. 2;
fig. 5 is a schematic flowchart of an image quality improving method according to a second embodiment of the present invention;
fig. 6 is a schematic flowchart of an image quality improving method according to a third embodiment of the present invention;
fig. 7 is a schematic flowchart of an image quality improving method according to a fourth embodiment of the present invention;
fig. 8 is a schematic structural diagram of an image quality improving apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an image quality improving apparatus according to a second embodiment of the present invention;
fig. 10 is a schematic structural diagram of an image quality improving apparatus according to a third embodiment of the present invention;
fig. 11 is a schematic structural diagram of an image quality improving apparatus according to a fourth embodiment of the present invention;
fig. 12 is a schematic structural diagram of an image quality improving apparatus according to a fifth embodiment of the present invention;
fig. 13 is a schematic structural diagram of an electronic device according to a first embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution provided by the present invention is exemplarily described below with reference to specific embodiments.
An aspect of an embodiment of the present invention provides a method for improving image quality. Fig. 1 is a flowchart illustrating an image quality improving method according to an embodiment of the present invention. In this embodiment, image noise reduction is performed on the luminance component map of the image, and first, a noise reduction histogram of the luminance component map of the original image is obtained, and then, noise reduction processing is performed on the luminance component map according to the noise reduction histogram, so as to obtain a noise-reduced image. The execution subject of the present embodiment may be a computer, software, an integrated circuit, or the like. As shown in fig. 1, the image quality improving method includes:
s101, acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image.
The noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map.
For example, the original image may be an image captured by a camera or an image received by the image quality improving apparatus. The original image may have a problem of being noisy, so that the image quality is low. Particularly, for an image obtained by shooting under a low illumination condition, such as night, the defects of high noise, low contrast, dark brightness and the like exist, and the image quality improving method provided by the invention is used for solving the problem of high noise of an original image.
Illustratively, an image is composed of MxN pixel points, M and N are positive integers, M is the number of pixel points included in each row of the image, and N is the number of pixel points included in each column of the image. The format of the raw image may be a YUV color space format or an RGB image format.
When the format of the original image is a YUV color space format, the value of each pixel includes Y, U, V three components, and Y represents the brightness (Luminance) of the pixel, also called a gray-scale value. U and V represent the chroma (chroma) of the pixel, which indicates the color and saturation of the image. Thus, the original image can be represented by Y, U, V three MxN-sized matrices. Wherein, Y[MxN]As a luminance component map of the original image, U[MxN]And V[MxN]Is a chrominance component map of the original image.
When the format of the original image is an RGB image format, the value of each pixel point includes R, G, B three components. R represents the Red (Red) component of the pixel point, G represents the Green (Green) component of the pixel point, B represents the Blue (Blue) component of the pixel point, and R, G, B the superposition of the three components by adopting different proportions can obtain various colors. Thus, the original image can be represented by R, G, B three MxN-sized matrices. Wherein R is[MxN]Is a red component map of the original image, G[MxN]Is a green component map of the original image, B[MxN]Is a blue component map of the original image.
In this case, before the luminance component map and the chrominance component map of the original image are obtained, format conversion may be performed on the original image, that is, the RGB format image is converted into the YUV format image, so as to obtain the luminance component map and the chrominance component map of the original image. Optionally, the format conversion formula is specifically as follows:
Y[MxN]＝T(1,1)·R[MxN]+T(1,2)·G[MxN]+T(1,3)·B[MxN]equation 1
U[MxN]＝T(2,1)·R[MxN]+T(2,2)·G[MxN]+T(2,3)·B[MxN]+128 formula 2
V[MxN]＝T(3,1)·R[MxN]+T(3,2)·G[MxN]+T(3,3)·B[MxN]+128 formula 3
Where T is a 3x3 transformation matrix.
Optionally, the format of the original image may also be in other formats such as CMYK, and it is only necessary to detect the format of the original image first before acquiring the luminance component diagram and the chrominance component diagram of the original image, and then convert the format of the original image by using a corresponding format conversion formula according to the format of the original image.
Illustratively, after the luminance component map is acquired, a noise reduction histogram of the luminance component map is acquired. Specifically, fig. 2 is a luminance component diagram according to an embodiment of the present invention. Fig. 3 is a histogram diagram of the luminance component map shown in fig. 2. Illustratively, the histogram provides the distribution of values of each pixel point of the image, and gives an overall description of all the values. As shown in fig. 3, the abscissa value represents the value of the pixel in the image, and the ordinate value represents the total number of pixels in the image for each value. The histogram may be represented by a one-dimensional vector. Considering that the value range of each pixel point of the image is [0, 255], the one-dimensional vector comprises 256 elements.
The embodiment of the invention provides a method for acquiring a noise reduction histogram, which filters noise in a brightness component map in the process of acquiring the noise reduction histogram. The noise reduction histogram only indicates the brightness value distribution condition of the edge pixel points in the brightness component map. The edge pixel points are pixel points in the brightness component graph, wherein the brightness value difference between the adjacent pixel points is larger than a preset difference value. Fig. 4 is a schematic diagram of a noise reduction histogram of the luminance component map shown in fig. 2. As shown in fig. 4, the abscissa value represents the value of the edge pixel, and the ordinate value represents the total number of edge pixels in the luminance component map for each value. Illustratively, the noise reduction histogram may also be represented by a one-dimensional vector. Considering that the value range of each pixel point of the image is [0, 255], the one-dimensional vector comprises 256 elements.
And S102, denoising the brightness component image according to the denoising histogram to obtain a denoising component image.
Illustratively, after obtaining the noise reduction histogram, the luminance component map is restored according to the noise reduction histogram to obtain the noise reduction component map. At this time, since the noise reduction histogram does not include the noise information in the luminance component map, a noise reduction component map from which noise is filtered can be obtained.
S103, acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component image and the brightness component image.
Illustratively, for an original image containing MxN pixel points, the noise reduction component map and the luminance component map also contain MxN pixel points, and the correction coefficient for each pixel point can be determined according to different values of the pixel point at the same position in the noise reduction component map and the luminance component map.
Optionally, the obtaining of the correction coefficient of each pixel point in the original image may specifically be: and acquiring a correction coefficient of a third pixel point according to the ratio of the brightness value of the first pixel point in the noise reduction component diagram to the brightness value of the second pixel point in the brightness component diagram.
The third pixel point is any pixel point in the original image, and the first pixel point is a pixel point which has the same coordinate with the third pixel point in the noise reduction component image; the second pixel point is a pixel point in the brightness component image, and the pixel point has the same coordinate with the third pixel point.
Illustratively, for a brightness component map containing MxN pixel points, each pixel point can be marked as f (i, j) according to the position of each pixel point, where i is [0, N-1] and j is [0, M-1 ].
For example, for any pixel point f (i, j) in the original image, the ratio of the value of the pixel point with the coordinate (i, j) in the noise reduction component map to the value of the pixel point with the coordinate (i, j) in the brightness component map may be used as the correction coefficient of the pixel point f (i, j).
And S104, according to the correction coefficient of each pixel point, carrying out noise reduction on the original image to obtain a noise-reduced image.
Illustratively, after the correction coefficient of each pixel is obtained, the value of each pixel in the original image can be corrected according to the correction coefficient, so as to obtain the noise-reduced image.
For example, regarding the different formats of the images to be denoised, one possible way to obtain the denoised images is:
s11, correcting the value of each corresponding pixel point in the chromaticity component diagram of the original image according to the correction coefficient of each pixel point to obtain the chromaticity component diagram after noise reduction;
and S12, obtaining the noise-reduced image according to the noise-reduced brightness component diagram and the noise-reduced chroma component diagram.
Illustratively, when a noise reduction image in a YUV format needs to be obtained, each pixel in the chromaticity component map may be corrected according to the correction coefficient of each pixel, so as to obtain a chromaticity component map after noise reduction. And obtaining the noise-reduced image according to the noise-reduced brightness component image and the noise-reduced chrominance component image.
Another possible noise-reduced image acquisition method is as follows:
and respectively correcting the values of corresponding pixels in the red component diagram, the green component diagram and the blue component diagram of the original image according to the correction coefficient of each pixel to obtain the noise-reduced image.
Illustratively, when a noise-reduced image in an RGB format needs to be obtained, red, green, and blue components of each pixel point may be respectively corrected according to the correction coefficient of each pixel point, so as to obtain a noise-reduced image.
For example, when the noise-reduced image is obtained, format conversion may be performed on the noise-reduced image, so that the format of the noise-reduced image is the same as that of the original image.
The image quality improving method provided by the embodiment of the invention comprises the following steps: acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map; denoising the brightness component diagram according to the denoising histogram to obtain a denoising component diagram; acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component diagram and the brightness component diagram; and according to the correction coefficient of each pixel point, carrying out noise reduction on the original image to obtain a noise-reduced image. The noise of the image is filtered by obtaining the noise reduction histogram of the brightness component map, and the image is restored according to the noise reduction histogram, so that the noise-reduced image is obtained, and the image quality is improved. Meanwhile, the mode of filtering the image noise adopts the mode of a noise reduction histogram, so that point-by-point noise reduction based on each pixel point is avoided, the calculated amount during image noise reduction is reduced, and the processing speed of image noise reduction is improved.
Optionally, on the basis of any of the above embodiments, an embodiment of the present invention further provides an image quality improving method, and this embodiment describes in detail a process of obtaining a noise reduction histogram. Fig. 5 is a flowchart illustrating an image quality improving method according to a second embodiment of the present invention. As shown in fig. 5, the image quality improving method includes:
s501, edge pixel points in the brightness component diagram are obtained.
Optionally, edge pixel points in the brightness component map may be obtained by methods such as edge detection and feature extraction.
Optionally, in order to improve the acquisition efficiency of the edge pixel points, the invention provides an edge pixel point detection algorithm. The algorithm mainly comprises the following steps:
traversing all pixel points in the brightness component diagram, and determining a pixel point as an edge pixel point if the absolute value of the difference between the brightness value of the pixel point and the brightness value of the adjacent pixel point is greater than a preset difference for any pixel point in the brightness component diagram;
and the adjacent pixel points are any pixel points in the brightness component diagram, wherein the distance between the adjacent pixel points and the pixel points is smaller than the preset distance.
For example, taking a brightness component map containing MxN pixel points as an example, according to the position of each pixel point, each pixel point can be marked as f (i, j), and the value of f (i, j) is the brightness value of the pixel point.
When determining whether the pixel point f (i, j) is an edge pixel point, comparing whether the difference between the brightness values of f (i, j) and f (i + p, j + q) is greater than a preset difference, and when the difference between f (i, j) and f (i + p, j + q) is greater than the preset difference, considering f (i, j) as an edge pixel point. Wherein f (i + p, j + q) is an adjacent pixel point of f (i, j), and values of p and q can be (+/-r, (+/-r), (0, (+/-r) and (+/-r, 0). Wherein, the value of r can be 1, 2, 3 and other numerical values. The preset difference may be 1, 2, 3, etc. as an example.
S502, acquiring the number of edge pixel points corresponding to each brightness value according to the brightness value of the edge pixel points.
S503, obtaining a noise reduction histogram according to the number of the edge pixel points corresponding to each brightness value.
Exemplarily, if the value of f (i, j) is k, after determining that the pixel point f (i, j) is an edge pixel point, H is addedkThe value of (d) is increased by 1. Wherein k is the abscissa in the histogram shown in fig. 3 and 4, k ranges from 0 to 255, and H iskThe number of edge pixels with a value of k is also the ordinate in the histogram shown in fig. 3 and 4. Detecting whether each pixel point is an edge point or not by scanning the brightness component graph point by point, and modifying H after determining that the pixel point is the edge pixel pointkTo obtain a noise reduction histogram.
S504, denoising the brightness component image according to the denoising histogram to obtain a denoising component image.
And S505, acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component image and the brightness component image.
S506, according to the correction coefficient of each pixel point, denoising the original image to obtain a denoised image.
For example, S504, S505, and S506 in this embodiment are the same as the related features in S101, S102, and S103 in the embodiment shown in fig. 1, and the details of this invention are not repeated.
In the image quality improvement method provided by this embodiment, edge pixel points in the brightness component map are first obtained, and according to the brightness values of the edge pixel points, the number of the edge pixel points corresponding to each brightness value is counted to obtain a noise reduction histogram. In the image quality improving method provided by the embodiment of the invention, the method for acquiring the noise reduction histogram is simple, the calculated amount is less, and the processing speed is high.
Optionally, on the basis of any of the above embodiments, an embodiment of the present invention further provides an image quality improvement method, and this embodiment describes in detail a process of restoring a luminance component map according to a noise reduction histogram. Fig. 6 is a flowchart illustrating an image quality improving method according to a third embodiment of the present invention. As shown in fig. 6, the image quality improving method includes:
s601, acquiring a noise reduction histogram corresponding to the brightness component diagram of the original image.
For example, S601 in this embodiment is the same as the related features in S101 in the embodiment shown in fig. 1, and the details of this embodiment are not repeated.
S602, acquiring a normalized histogram of the noise reduction histogram, and acquiring a pixel mapping vector according to the normalized histogram and the differential matrix.
For example, after the noise reduction histogram H is obtained by using the method in any of the above embodiments, the noise reduction histogram H is normalized. Illustratively, using formulas in particular
The noise reduction histogram H is normalized. Wherein, KThe maximum value 255 of the pixel value.
Illustratively, in obtaining normalized histograms
Then according to the formula
A pixel mapping vector X is obtained.
Where D is a differential matrix of size KxK, as follows:
the pixel mapping vector X may be a column vector containing 256 elements, and X is a mapping function. The (k + 1) th component X in the pixel mapping vector XkMeans that the value of the pixel with the pixel value of k in the brightness component image of the original image is modified into the pixel value of XkAnd traversing all pixel points in the brightness component image to obtain the noise reduction component image. Wherein K is more than or equal to 0 and less than or equal to K. For example, the 1 st component X in the pixel mapping vector X0Means that the value of the pixel with the pixel value of 0 in the brightness component image of the original image is modified into the pixel value X0。
And S603, acquiring a noise reduction component image according to the pixel mapping vector and the brightness component image.
Illustratively, after acquiring the pixel mapping vector, mapping the pixel with the pixel value k in the luminance component image of the original image into the pixel with the pixel value XkThe pixel of (2).
And S604, acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component image and the brightness component image.
And S605, according to the correction coefficient of each pixel point, denoising the original image to obtain a denoised image.
For example, S604 and S605 in this embodiment are the same as S102 and S103 in the embodiment shown in fig. 1, and the details of this embodiment are not repeated.
The embodiment of the invention provides an image quality improving method, which comprises the steps of firstly, obtaining a pixel mapping vector according to a noise reduction histogram and a differential matrix; and then obtaining a luminance component image after noise reduction according to the pixel mapping vector and the luminance component image of the original image. The image quality improving method provided by the embodiment of the invention has the advantages of simple algorithm and higher processing speed.
Optionally, on the basis of the foregoing embodiment, an embodiment of the present invention further provides an image quality improvement method, and in this embodiment, after image denoising, a denoising histogram is further modified based on log mapping, so as to further improve image quality. Fig. 7 is a flowchart illustrating an image quality improving method according to a fourth embodiment of the present invention. As shown in fig. 7, the image quality improving method includes:
s701, acquiring a noise reduction histogram corresponding to the brightness component diagram of the original image.
For example, S701 in this embodiment is the same as the related features in S101 in the embodiment shown in fig. 1, and the details of this embodiment are not repeated.
S702, correcting the noise reduction histogram based on logarithmic mapping to obtain a corrected histogram.
Illustratively, considering that the pixel value distribution of most natural images is concentrated in a narrow interval, the image details are usually not clear enough, especially for night images, the values of the pixel points are concentrated in a low-brightness range, the visual effect of the images is usually poor, and the histogram effect of the images is usually that the histogram distribution is concentrated. Therefore, the histogram of the image can be expanded by adopting a histogram correction method, so that the values of all pixel points in the image are distributed relatively, and the aim of improving the image quality is fulfilled.
The embodiment of the invention adopts a logarithmic mapping mode which accords with the visual characteristics of human eyes to modify the noise reduction histogram so as to expand the dynamic range of the pixel values of the image and further achieve the aim of enhancing the image.
For example, the manner of log mapping may be specifically implemented according to the formula y ═ cln (1+ x), where x is an input value, c is a preset value, and y is an output value. The method can enhance the overall contrast of the image and improve the effect of image detail information, and the enhanced image visibility effect is good.
Illustratively, the manner of logarithmic mapping may specifically be according to a formula
In which HmaxIs the maximum value of all components in H, mu is a correction degree parameter, HH represents a correction histogram, HkRepresents the (k + 1) th component of H.
And S703, denoising the brightness component image according to the corrected histogram to obtain a denoising component image.
For example, the principle of obtaining the noise reduction component map based on the modified histogram in this embodiment is the same as that of obtaining the noise reduction component map based on the noise reduction histogram in the embodiment shown in fig. 7, and details of this embodiment are not repeated.
And S704, acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component image and the brightness component image.
S705, according to the correction coefficient of each pixel point, denoising the original image to obtain a denoised image.
For example, S704 and S705 in this embodiment are the same as S102 and S103 in the embodiment shown in fig. 1, and the description of this embodiment is omitted.
According to the quality improvement method provided by the embodiment of the invention, after the image is subjected to noise reduction based on logarithm, the noise reduction histogram is corrected based on logarithm mapping, so that the image quality is further improved.
Another aspect of the embodiments of the present invention further provides an image quality improving apparatus, configured to perform the image quality improving method shown in any one of fig. 1 to 7, which has the same or similar technical effects, and the description of the present invention is omitted.
Fig. 8 is a schematic structural diagram of an image quality improving apparatus according to an embodiment of the present invention. As shown in fig. 8, the image quality improving apparatus includes:
a noise reduction histogram obtaining module 801, configured to obtain a noise reduction histogram corresponding to a luminance component map of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map;
a noise reduction component map obtaining module 802, which performs noise reduction on the luminance component map according to the noise reduction histogram to obtain a noise reduction component map;
a correction coefficient obtaining module 803, configured to obtain a correction coefficient of each pixel in the original image according to the noise reduction component map and the luminance component map;
and the noise reduction module 804 is configured to perform noise reduction on the original image according to the correction coefficient of each pixel point to obtain a noise-reduced image.
Exemplarily, on the basis of the embodiment shown in fig. 8, fig. 9 is a schematic structural diagram of an image quality improving apparatus according to a second embodiment of the present invention. As shown in fig. 9, the noise reduction histogram acquisition module 801 includes: an edge pixel point acquisition unit 8011, an edge pixel point number acquisition unit 8012 and a noise reduction histogram acquisition unit 8013;
an edge pixel point obtaining unit 8011 configured to obtain an edge pixel point in the brightness component map;
the edge pixel number obtaining unit 8012 is configured to obtain, according to the brightness values of the edge pixels, the number of edge pixels corresponding to each brightness value;
the noise reduction histogram obtaining unit 8013 is configured to obtain a noise reduction histogram according to the number of edge pixel points corresponding to each brightness value.
Optionally, the edge pixel point obtaining unit 8011 is specifically configured to traverse all pixel points in the brightness component map, and determine, for any pixel point in the brightness component map, that a pixel point is an edge pixel point if an absolute value of a difference between a brightness value of the pixel point and a brightness value of an adjacent pixel point is greater than a preset difference;
and the adjacent pixel points are any pixel points in the brightness component diagram, wherein the distance between the adjacent pixel points and the pixel points is smaller than the preset distance.
Optionally, the correction coefficient obtaining module 803 is specifically configured to obtain a correction coefficient of a third pixel according to a ratio of a luminance value of a first pixel in the noise reduction component map to a luminance value of a second pixel in the luminance component map;
the third pixel point is any pixel point in the original image, and the first pixel point is a pixel point which has the same coordinate with the third pixel point in the noise reduction component image; the second pixel point is a pixel point in the brightness component image, and the pixel point has the same coordinate with the third pixel point.
Exemplarily, on the basis of the embodiment shown in fig. 8 or fig. 9, fig. 10 is a schematic structural diagram of an image quality improving apparatus provided in a third embodiment of the present invention. As shown in fig. 10, the noise reduction component map acquisition module 802 includes: a pixel mapping vector acquisition unit 8021 and a noise reduction component map acquisition unit 8022;
a pixel mapping vector obtaining unit 8021, configured to obtain a normalized histogram of the noise reduction histogram, and obtain a pixel mapping vector according to the normalized histogram and the differential matrix;
a denoising component map obtaining unit 8022, configured to obtain a denoising component map according to the pixel mapping vector and the brightness component map.
For example, on the basis of any one of the embodiments shown in fig. 8 to fig. 10, fig. 11 is a schematic structural diagram of an image quality improving apparatus according to a fourth embodiment of the present invention. As shown in fig. 11, the noise reduction module 804 includes: a correction unit 8041 and a post-noise reduction image acquisition unit 8042;
a correcting unit 8041, configured to correct, according to the correction coefficient of each pixel, a value of each corresponding pixel in the chromaticity component map of the original image, to obtain a chromaticity component map after noise reduction;
and a denoised image obtaining unit 8042, configured to obtain a denoised image according to the denoised luminance component map and the denoised chrominance component map.
Optionally, the denoising module 804 may be further specifically configured to correct values of corresponding pixels in the red component map, the green component map, and the blue component map of the original image according to the correction coefficient of each pixel, so as to obtain a denoised image.
Exemplarily, on the basis of any one of the embodiments shown in fig. 8 to 11, fig. 12 is a schematic structural diagram of an image quality improving apparatus according to a fifth embodiment of the present invention. As shown in fig. 12, the image quality improving apparatus further includes:
a modified histogram obtaining module 805, configured to modify the noise reduction histogram based on log mapping to obtain a modified histogram;
correspondingly, the noise reduction component map obtaining module 802 is specifically configured to perform noise reduction on the luminance component map according to the modified histogram to obtain a noise reduction component map.
An embodiment of the present invention further provides an electronic device, and fig. 13 is a schematic structural diagram of the electronic device according to the embodiment of the present invention. As shown in fig. 13, the electronic device includes a memory 1301, a processor 1302, and a program stored in the memory 1301, the program being configured to be executed by the processor 1302, the steps of the image quality improvement method shown in fig. 1 to 7 being implemented when the processor 1302 executes the program.
An embodiment of the present invention further provides a storage medium, where the storage medium stores a computer program, and the computer program, when executed by a processor, implements the steps of the image quality improvement method shown in fig. 1 to 7.
The apparatus in this embodiment and the method in the foregoing embodiments are based on two aspects of the same inventive concept, and the method implementation process has been described in detail in the foregoing, so that those skilled in the art can clearly understand the structure and implementation process of the system in this embodiment according to the foregoing description, and for the sake of brevity of the description, details are not repeated here.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. An image quality improvement method, comprising:
acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map;
denoising the brightness component diagram according to the denoising histogram to obtain a denoising component diagram;
acquiring a correction coefficient of each pixel point in the original image according to the noise reduction component map and the brightness component map;
according to the correction coefficient of each pixel point, carrying out noise reduction on the original image to obtain a noise-reduced image;
the acquiring of the noise reduction histogram corresponding to the luminance component map of the original image includes:
acquiring edge pixel points in the brightness component diagram;
acquiring the number of edge pixel points corresponding to each brightness value according to the brightness value of the edge pixel points;
and acquiring the noise reduction histogram according to the number of the edge pixel points corresponding to each brightness value.
2. The method of claim 1, wherein the obtaining edge pixel points in the luminance component map comprises:
traversing all pixel points in the brightness component map, and determining any pixel point in the brightness component map as an edge pixel point if the absolute value of the difference between the brightness value of the pixel point and the brightness value of the adjacent pixel point is greater than a preset difference;
and the adjacent pixel points are any pixel points in the brightness component diagram, wherein the distance between the adjacent pixel points and the pixel points is smaller than a preset distance.
3. The method according to any one of claims 1 to 2, wherein the obtaining a correction coefficient of each pixel point in the original image according to the noise reduction component map and the brightness component map comprises:
acquiring a correction coefficient of a third pixel point according to the ratio of the brightness value of a first pixel point in the noise reduction component map to the brightness value of a second pixel point in the brightness component map;
the third pixel point is any pixel point in the original image, and the first pixel point is a pixel point which has the same coordinate with the third pixel point in the noise reduction component map; and the second pixel point is a pixel point which has the same coordinate with the third pixel point in the brightness component map.
4. The method according to any one of claims 1 to 2, wherein the denoising the original image according to the correction coefficient of each pixel point to obtain a denoised image comprises:
correcting the value of each corresponding pixel point in the chromaticity component diagram of the original image according to the correction coefficient of each pixel point to obtain a chromaticity component diagram after noise reduction;
and obtaining the noise-reduced image according to the noise-reduced brightness component image and the noise-reduced chromaticity component image.
5. The method according to any one of claims 1 to 2, wherein the denoising the original image according to the correction coefficient of each pixel point to obtain a denoised image comprises:
and according to the correction coefficient of each pixel point, correcting the value of the corresponding pixel point in the red component diagram, the green component diagram and the blue component diagram of the original image respectively to obtain the noise-reduced image.
6. The method according to any one of claims 1 to 2, wherein after obtaining the noise reduction histogram corresponding to the luminance component map of the original image, the method further comprises:
modifying the noise reduction histogram based on logarithmic mapping to obtain a modified histogram;
the denoising of the luminance component map according to the denoising histogram to obtain a denoising component map includes:
and denoising the brightness component diagram according to the corrected histogram to obtain a denoising component diagram.
7. An image quality improvement device, comprising:
the noise reduction histogram acquisition module is used for acquiring a noise reduction histogram corresponding to a brightness component diagram of an original image; the noise reduction histogram indicates the brightness value distribution of edge pixel points in the brightness component map;
the noise reduction component map acquisition module is used for carrying out noise reduction on the brightness component map according to the noise reduction histogram to obtain a noise reduction component map;
a correction coefficient obtaining module, configured to obtain a correction coefficient of each pixel in the original image according to the noise reduction component map and the brightness component map;
the noise reduction module is used for carrying out noise reduction on the original image according to the correction coefficient of each pixel point to obtain a noise-reduced image;
the noise reduction histogram acquisition module includes: the device comprises an edge pixel point acquisition unit, an edge pixel point number acquisition unit and a noise reduction histogram acquisition unit;
the edge pixel point acquisition unit is used for acquiring edge pixel points in the brightness component diagram;
the edge pixel number obtaining unit is used for obtaining the number of edge pixels corresponding to each brightness value according to the brightness value of the edge pixels;
and the noise reduction histogram acquisition unit is used for acquiring the noise reduction histogram according to the number of the edge pixel points corresponding to each brightness value.
8. The apparatus according to claim 7, wherein the edge pixel point obtaining unit is specifically configured to,
traversing all pixel points in the brightness component map, and determining any pixel point in the brightness component map as an edge pixel point if the absolute value of the difference between the brightness value of the pixel point and the brightness value of the adjacent pixel point is greater than a preset difference;
and the adjacent pixel points are any pixel points in the brightness component diagram, wherein the distance between the adjacent pixel points and the pixel points is smaller than a preset distance.
9. The apparatus according to any one of claims 7 to 8, wherein the correction coefficient obtaining module is specifically configured to,
acquiring a correction coefficient of a third pixel point according to the ratio of the brightness value of a first pixel point in the noise reduction component map to the brightness value of a second pixel point in the brightness component map;
the third pixel point is any pixel point in the original image, and the first pixel point is a pixel point which has the same coordinate with the third pixel point in the noise reduction component map; and the second pixel point is a pixel point which has the same coordinate with the third pixel point in the brightness component map.
10. The apparatus of any of claims 7 to 8, wherein the noise reduction module comprises: the device comprises a correction unit and a noise-reduced image acquisition unit;
the correction unit is used for correcting the value of each corresponding pixel point in the chromaticity component diagram of the original image according to the correction coefficient of each pixel point to obtain the chromaticity component diagram after noise reduction;
and the image obtaining unit after noise reduction is used for obtaining an image after noise reduction according to the brightness component image after noise reduction and the chromaticity component image after noise reduction.
11. The apparatus according to any one of claims 7 to 8, characterized in that the noise reduction module is in particular adapted to,
and according to the correction coefficient of each pixel point, correcting the value of the corresponding pixel point in the red component diagram, the green component diagram and the blue component diagram of the original image respectively to obtain the noise-reduced image.
12. The apparatus of any one of claims 7 to 8, further comprising:
a modified histogram acquisition module, configured to modify the noise reduction histogram based on logarithmic mapping to obtain a modified histogram;
the noise reduction component map obtaining module is specifically configured to perform noise reduction on the luminance component map according to the modified histogram to obtain a noise reduction component map.
13. An electronic device, characterized in that: comprising a memory, a processor and a program stored in the memory, the program being configured to be executed by the processor, the processor when executing the program implementing the steps of the image quality improvement method as claimed in any one of claims 1 to 6.
14. A storage medium storing a computer program, characterized in that: the computer program when executed by a processor implements the steps of the image quality improvement method of any one of claims 1 to 6.
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