CN113191990B - Image processing method, device, electronic equipment and medium - Google Patents

Abstract

The embodiment of the present application discloses an image processing method, device, electronic equipment and medium. The method includes: determining an initial histogram and a detail histogram according to the high-pass filtered image of the image to be processed and the image to be processed; according to the relationship between the first reference threshold and the statistical value of the detail cumulative histogram, and the second reference threshold and the detail Determine the relationship between the statistical values of the cumulative histogram, determine the first platform threshold and the second platform threshold; determine the dual platform histogram according to the first platform threshold, the second platform threshold and the initial histogram; determine the gray level according to the dual platform cumulative histogram mapping relationship, and process the image to be processed based on the grayscale mapping relationship to obtain the target image. The above solution can take into account the details of the image when determining the dual-platform threshold, so that the determined dual-platform threshold can be applied to image processing in different scenarios, improving the scene adaptability of the image processing method.

Description

Image processing method, device, electronic equipment and medium

technical field

The embodiments of the present application relate to the technical field of image processing, and in particular, to an image processing method, device, electronic device, and medium.

Background technique

Image processing is the technique of analyzing an image with a computer to achieve a desired result. Image processing includes image transformation, image coding and compression, image enhancement and restoration, image segmentation, image description and image classification, etc.

Image enhancement technology is often used in image preprocessing and image display quality improvement. It is widely used in digital image processing systems. Image enhancement is usually processed in the spatial domain or frequency domain. The most commonly used method in the spatial domain is the linear stretching algorithm. , histogram equalization processing, exponential transformation processing, etc. Among them, the histogram equalization method is widely used because of its simple algorithm and strong adaptability.

The current histogram equalization algorithm does not distinguish between the image background and the target, which leads to the background and noise occupying too much gray scale range after some scenes are enhanced, but the target details and other information occupy less gray scale range, resulting in poor final image effect. good. Although the improved histogram equalization algorithm can alleviate the problems of background overstretching and image detail loss to a certain extent, it is difficult for the existing improved histogram equalization algorithm to adaptively determine the enhancement parameters for different scenarios, so that the image enhancement Effect is limited.

Contents of the invention

Embodiments of the present application provide an image processing method, device, electronic equipment, and medium, so as to improve image processing effects in different scenarios.

In one embodiment, the embodiment of the present application provides an image processing method, the method comprising:

determining an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;

According to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, determine the first value from the statistical value of the detailed cumulative histogram. A platform threshold and a second platform threshold; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detail cumulative histogram, and the detail cumulative histogram is the accumulation of the detail histogram histogram;

determining a dual platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;

Determine the grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image; wherein, the dual-platform cumulative histogram is the dual-platform histogram The cumulative histogram of .

In another embodiment, the embodiment of the present application also provides an image processing device, which includes:

A detail histogram determining module, configured to determine an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determine a detail histogram of the image to be processed according to the initial histogram, the detail cumulative histogram The figure is a cumulative histogram of the detail histogram;

Threshold determination module, for according to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, from the detailed cumulative histogram A first platform threshold and a second platform threshold are determined in the statistical value; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detailed cumulative histogram;

A dual-platform histogram determination module, configured to determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram;

A processing module, configured to determine a grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image; wherein, the dual-platform cumulative histogram is the Cumulative histogram of the two-platform histogram described above.

In yet another embodiment, the embodiment of the present application also provides an electronic device, including: one or more processors;

memory for storing one or more programs;

When the one or more programs are executed by the one or more processors, the one or more processors implement the image processing method described in any one of the embodiments of the present application.

In one embodiment, the embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the image processing as described in any one of the embodiments of the present application is realized. method.

In the embodiment of the present application, the initial histogram is determined according to the high-pass filtered image of the image to be processed and the image to be processed, and the detail histogram of the image to be processed is determined according to the initial histogram, so as to count the high Frequency component, determine the detail part of the image to be processed, according to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, from the Determining the first platform threshold and the second platform threshold in the statistical value of the detail cumulative histogram, so that the details are taken into account in the determination process of the first platform threshold and the second platform threshold, so as to be suitable for the image to be processed in the current scene, According to the first platform threshold, the second platform threshold and the initial histogram, determine a dual-platform histogram; determine the grayscale mapping relationship according to the dual-platform cumulative histogram, and determine the grayscale mapping relationship based on the grayscale mapping relationship The image to be processed is processed to obtain a target image, thereby improving the processing effect of the image to be processed.

Description of drawings

FIG. 1 is a flowchart of an image processing method provided by an embodiment of the present application;

FIG. 2 is a flowchart of an image processing method provided by another embodiment of the present application;

FIG. 3 is a flowchart of an image processing method provided in another embodiment of the present application;

FIG. 4 is a schematic structural diagram of an image processing device provided by an embodiment of the present application;

Fig. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only some structures related to the present application are shown in the drawings but not all structures.

FIG. 1 is a flowchart of an image processing method provided by an embodiment of the present application. The image processing method provided in the embodiment of the present application is applicable to the case of processing an image. Typically, this embodiment of the present application is applicable to the situation where the image to be processed is enhanced based on the dual-platform histogram. The method can specifically be executed by an image processing device, which can be implemented by software and/or hardware, and which can be integrated into an electronic device capable of implementing the image processing method, and the electronic device can be a processor of an intelligent image collector, A local processor or a cloud processor independent of the image collector. Referring to Figure 1, the method of the embodiment of the present application specifically includes:

S110. Determine an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determine a detail histogram of the image to be processed according to the initial histogram.

在采用滤波核对待处理图像进行处理前，先根据滤波核的大小，对待处理图像进行扩边处理，以使边缘的像素点能够位于滤波核的中心进行卷积处理。Wherein, the image to be processed can be an image collected by an image collector, or an image obtained locally or from the Internet. The format and bit width of the image to be processed are not specifically limited, and can be an image to be processed in any format and bit width. All can be processed by using the method in the embodiment of the present application. The high-pass filter algorithm is not limited, for example, it may be an unsharp mask filter, a Sobel operator filter, a DOG operator filter, a LOG operator filter, a Laplacian operator filter, and the like. The embodiment of the present application does not limit the size of the filter kernel, for example, it can be 3*3, or 5*5, or 7*7, etc., and the form of the filter kernel can be

Before using the filter kernel to process the image to be processed, the image to be processed is first expanded according to the size of the filter kernel, so that the pixels on the edge can be located in the center of the filter kernel for convolution processing.

The initial histogram is directly determined according to the high-pass filtered image of the image to be processed and the image to be processed. The abscissa value of the initial histogram can be the gray level in the image to be processed, and the ordinate value of the initial histogram can be the gray level The cumulative value of the detail data corresponding to the pixel in the high-pass filtered image. The initial histogram is further processed to obtain the detail histogram. Processing the initial histogram may be, for example, filtering out some data in the initial histogram, adjusting the sequence of data in the initial histogram, etc., and may be processed according to actual requirements.

S120. According to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, determine from the statistical value of the detailed cumulative histogram A first platform threshold and a second platform threshold; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detailed cumulative histogram. Wherein, the detail cumulative histogram is a cumulative histogram of the detail histogram;

Wherein, the statistical value of the detail cumulative histogram may be a statistical value obtained by accumulating the sum of the detail data of the detail histogram. The first reference threshold and the second reference threshold can be determined according to the maximum statistical value of the detail cumulative histogram, for example, the first reference threshold and the second reference threshold can be calculated according to the following formula:

histSumUp=histSum*t

histSumDown＝histSum*(1-t)

Wherein, histSumDown can be the first reference threshold, histSumUp can be the second reference threshold, histSum can be the maximum statistical value of the detail accumulation histogram, 0<t<1, t can be selected according to the actual situation, for example, it can be selected as 0.85. In different scenarios, t can take different values.

The calculation formula of detail cumulative histogram is:

accSortHist(g)=accSortHist(g-1)+sortHist(g)

Among them, accSortHist(g) is the detail cumulative histogram, sortHist(g) is the detail histogram, and g is the gray level.

Exemplarily, with the first reference threshold and the second reference threshold as references, the first platform threshold and the second platform threshold are determined from the statistical values of the detail cumulative histogram. For example, among the statistical values of the detail cumulative histogram, the statistical value closest to and greater than the first reference threshold can be used as the first platform threshold, and the statistical value closest to the second reference threshold and greater than the second reference threshold can be used as the first platform threshold. The statistical value is used as the second platform threshold, or the statistical value in the detail cumulative histogram that is closest to the first reference threshold and smaller than the first reference threshold is used as the first platform threshold, which is the closest to the second reference threshold and smaller than the second The statistical value of the reference threshold is used as the second platform threshold, or, among the statistical values of the detail cumulative histogram, the statistical value that is closest to the first reference threshold and greater than the first reference threshold is used as the first platform threshold, which will be compared with the second reference threshold. The statistical value closest to the threshold and smaller than the second reference threshold is used as the second platform threshold, or the statistical value closest to the first reference threshold and smaller than the first reference threshold in the detail cumulative histogram is used as the first platform threshold, which will be compared with A statistical value that is closest to the second reference threshold and greater than the second reference threshold is used as the second platform threshold.

The beneficial effect of the above scheme is that, since the detail accumulation histogram includes the detail information of the image to be processed, the first platform threshold and the second platform threshold are determined according to the detail accumulation histogram, so that the first platform threshold and the second platform threshold are suitable for Improve the image processing effect by processing the image to be processed in the current scene.

S130. Determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram.

Exemplarily, a dual-platform histogram is constructed according to the relationship between the first platform threshold and the statistical value of the initial histogram, and the relationship between the second platform threshold and the statistical value of the initial histogram. Specifically, for example, if the first platform threshold is smaller than the second platform threshold, determine the relationship between the statistical value of the initial histogram and the first platform threshold and the second platform threshold. If the statistical value of the initial histogram is less than the first platform threshold, replace the statistical value of the initial histogram with the first platform threshold, and if the statistical value of the initial histogram is greater than the second platform threshold, replace the initial histogram with the second platform threshold If the statistical value of the initial histogram is greater than or equal to the threshold of the first platform and less than or equal to the threshold of the second platform, the statistical value of the initial histogram is retained to form a dual platform histogram.

S140. Determine a grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image. Wherein, the dual-platform cumulative histogram is a cumulative histogram of the dual-platform histogram.

Exemplarily, the grayscale mapping relationship is determined according to the dual-platform cumulative histogram, thereby determining the value corresponding to each grayscale level, and then determining the value corresponding to the pixel point according to the grayscale level of each pixel in the image to be processed, Assign this pixel to get the target image.

The grayscale mapping relationship can be determined according to the actual situation, for example, the grayscale mapping relationship can be determined according to the statistical value corresponding to each grayscale level in the dual-platform cumulative histogram, the statistical value corresponding to the maximum grayscale level, and the grayscale mapping range value.

In the embodiment of the present application, the initial histogram is determined according to the high-pass filtered image of the image to be processed and the image to be processed, and the detail histogram of the image to be processed is determined according to the initial histogram, so as to count the high Frequency component, determine the detail part of the image to be processed, according to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, from the Determining the first platform threshold and the second platform threshold in the statistical value of the detail cumulative histogram, so that the details are taken into account in the determination process of the first platform threshold and the second platform threshold, so as to be suitable for the image to be processed in the current scene, According to the first platform threshold, the second platform threshold and the initial histogram, determine a dual-platform histogram; determine the grayscale mapping relationship according to the dual-platform cumulative histogram, and determine the grayscale mapping relationship based on the grayscale mapping relationship The image to be processed is processed to obtain a target image, thereby improving the processing effect of the image to be processed.

FIG. 2 is a flowchart of an image processing method provided by another embodiment of the present application. In this embodiment of the present application, to further optimize the foregoing embodiments, S110 is subdivided into S210-S240. Details that are not described in detail in the embodiments of the present application can be found in the above-mentioned embodiments. Referring to Figure 2, the image processing method provided by the embodiment of the present application may include:

S210. Traverse the pixels of the image to be processed, and use the pixels of the image to be processed with the same gray level as target pixels.

Exemplarily, it traverses from the first pixel in the upper left corner of the image to be processed to determine the gray level of each pixel. Take the pixels with the same pixel level as the target pixels. For gray levels where the target pixel is not zero, each gray level corresponds to a group of target pixels, and the number of a group of pixels is at least one.

S220. Determine the initial histogram according to the sum of the detail data corresponding to the target pixel in the high-pass filtered image and the corresponding gray level.

Exemplarily, for each gray level, the detail data corresponding to the corresponding target pixel in the high-pass filtered image is determined, and the details corresponding to a group of target pixels corresponding to each gray level in the high-pass filtered image The sum of the data is used as the statistical value of the detailed data corresponding to the gray level, and the gray level is used as the abscissa value, and the statistical value of the detailed data corresponding to the gray level is used as the vertical coordinate value to determine the initial histogram. In the embodiment of this application, the gray level of the image to be processed starts from 0. If you want the gray level in the histogram to start from 1, you can add one to each gray level, and the corresponding detailed data statistics The value is unchanged so that the abscissa values start at 1.

Among them, the detail data can be imgHpss(i,j) ^α , imgHpss(i,j) is the value corresponding to the pixel point (i,j) in the high-pass filter image, and α is the degree of detail, which can be selected according to the actual situation. If you want To emphasize details and increase the influence of details, you can set α to a larger value. If you do not want to emphasize details and reduce the influence of details, you can set α to a smaller value, α≥0.

S230. Remove items whose ordinate values are zero in the initial histogram, and compress the abscissa values of the initial histogram to obtain a filtered histogram.

In this embodiment of the application, the initial histogram is determined according to the sum of the detail data corresponding to the target pixel in the high-pass filtered image and the corresponding gray level. The default is to use the sum of the detail data of the target pixel As the ordinate, the corresponding gray level is used as the abscissa, and then the operations on the abscissa and ordinate in S230-S240 are performed. Exemplarily, if the ordinate value in the initial histogram is zero, it means that the pixel point range corresponding to the gray level does not include detailed information, and the ordinate value corresponding to the gray level can be removed to simplify the initial histogram picture. The ordinate values are removed, and the corresponding abscissa values are removed, so the abscissa values are reduced. In order to make the abscissa values equally spaced, the abscissa values can be compressed so that they are still equally spaced points. For example, the abscissa value of the initial histogram has a total of 256 values from 1 to 256, and the interval is 1. If 10 items with a ordinate value of zero are removed, the corresponding abscissa value is also removed, leaving 246 values in the abscissa value. Then compress the abscissa values to 1-246, so that the abscissa values are equally spaced. The filtered histogram is formed by the compressed abscissa value and the remaining ordinate value after removing the item whose ordinate value is zero, and the abscissa value and the ordinate value correspond to each other in order.

Exemplarily, the initial histogram is determined according to the sum of the detail data corresponding to the target pixel in the high-pass filtered image and the corresponding gray level. In actual situations, the sum of the detail data corresponding to the target pixel can also be As the abscissa, the corresponding gray level is used as the ordinate, then when the operations of S230-S240 are subsequently performed, the operation for the ordinate is modified to the operation for the abscissa, and the operation for the abscissa is modified to be for the ordinate The operation, that is, "remove the item whose abscissa value is zero in the initial histogram, and compress the ordinate value of the initial histogram to obtain a filtered histogram. The abscissa value of the filtered histogram Arrange in order of size to get a detail histogram".

S240. Arrange the ordinate values of the filtering histogram in order of size to obtain a detail histogram.

Exemplarily, for the filtering histogram, the abscissa values remain unchanged, and the ordinate values are arranged in order of size to obtain a detail histogram. The sorted detail histogram can more intuitively display the change of the ordinate value, which is convenient for subsequent intuitive and quick determination of the first platform threshold and the second platform threshold according to the detail histogram.

S250. According to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, determine from the statistical value of the detailed cumulative histogram A first platform threshold and a second platform threshold; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detailed cumulative histogram.

S260. Determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram.

S270. Determine a grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image.

In the embodiment of the present application, the initial histogram is determined according to the sum of the detail data corresponding to the target pixel in the high-pass filtered image and the corresponding gray level, and the ordinate value in the initial histogram is removed to be zero , and compress the abscissa values of the initial histogram to obtain a filtered histogram, and arrange the ordinate values of the filtered histogram in order of size to obtain a detail histogram, thereby realizing the The high-frequency detail information is counted to facilitate subsequent determination of the dual-platform threshold based on the detail information and improve the scene applicability of the image processing method.

FIG. 3 is a flow chart of an image processing method provided in another embodiment of the present application. In order to further optimize the above-mentioned embodiment, the embodiment of the present application refines S120 into S320, and refines S140 into S350-S370. When it needs to be explained, the refinement of S120 and the refinement of S140 may not be performed at the same time. Without affecting the dependence, only one step can be refined, and both S120 and S140 can be refined. For the details described in the embodiment of this application, refer to the above embodiment for details. Referring to Fig. 3, the image processing method provided by the embodiment of the present application may include:

S310. Determine an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determine a detail histogram of the image to be processed according to the initial histogram.

S320, taking the statistical value of the detail cumulative histogram that is greater than the first reference threshold and having the smallest difference with the first reference threshold as the first platform threshold; and, setting it greater than the second reference threshold, and The statistical value of the detail cumulative histogram with the smallest difference from the second reference threshold is used as the second platform threshold.

Exemplarily, in the detailed cumulative histogram, if there is a statistical value of the detailed cumulative histogram greater than the first reference threshold, and the difference with the first reference threshold is the smallest, then the statistical value of the detailed cumulative histogram is taken as the first Platform threshold, if the statistical value of the detail accumulation histogram is greater than the second reference threshold and the difference with the second reference threshold is the smallest, then this statistical value is used as the second platform threshold.

S330. Determine the first gray level corresponding to the first platform threshold and the second gray level corresponding to the second platform threshold in the detail cumulative histogram.

Exemplarily, the first gray level corresponding to the first platform threshold and the second gray level corresponding to the second platform threshold in the detail accumulation histogram are recorded. If the first plateau threshold is less than the second plateau threshold, the first gray scale is less than the second gray scale.

S340. Determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram.

In the embodiment of the present application, the first platform threshold is smaller than the second platform threshold; correspondingly, according to the first platform threshold, the second platform threshold and the initial histogram, a dual-platform histogram is determined , including: if the statistical value of the initial histogram is smaller than the first platform threshold, then replacing the statistical value of the initial histogram with the first platform threshold; if the statistical value of the initial histogram is greater than the second platform threshold, then replace the statistical value of the initial histogram with the second platform threshold; if the statistical value of the initial histogram is greater than or equal to the first platform threshold and less than or equal to the second platform threshold, the statistical value of the initial histogram is retained.

Exemplarily, if t>0.5, the first reference threshold is smaller than the second reference threshold, the first platform threshold is smaller than the second platform threshold, and thus the first gray level is smaller than the second gray level. Then, according to the first platform threshold, the second platform threshold and the initial histogram, determine the dual-platform histogram. Exemplarily, the dual-platform histogram can be determined according to the following formula:

Among them, DPHist(g) is the statistical value of the dual-platform histogram, Hist(g) is the statistical value of the initial histogram, TDown is the threshold of the first platform, TUp is the threshold of the second platform, and g is the gray level.

S350. Determine the statistical value ratio according to the statistical value of the dual-platform cumulative histogram corresponding to each gray level and the statistical value of the dual-platform cumulative histogram corresponding to the maximum gray level.

Exemplarily, the dual-platform cumulative histogram is determined according to the dual-platform cumulative histogram, which can be determined according to the following formula:

accDPHist(g)=accDPHist(g-1)+DPHist(g)

Among them, accDPHist(g) is the statistical value of the dual-platform cumulative histogram, and DPHist(g) is the statistical value of the dual-platform histogram.

Exemplarily, the statistical value ratio can be determined according to the following formula:

Among them, P is the statistical value ratio, accDPHist(g) is the statistical value of the dual-platform cumulative histogram, accDPHist(m) is the statistical value of the dual-platform cumulative histogram corresponding to the maximum gray level, and m is the dual-platform cumulative histogram maximum gray level.

S360. Determine the grayscale mapping relationship according to the product of the statistical value ratio and the grayscale mapping range value.

Exemplarily, the grayscale mapping relationship can be expressed as:

HistB(g)=P*R

Among them, HistB(g) is the grayscale mapping relationship, R is the grayscale mapping range value, and P is the statistical value ratio.

In the embodiment of the present application, the process of determining the grayscale mapping range value includes: determining the maximum grayscale level of the target image according to the preset bit width of the target image; If the absolute value of the difference between the gray levels is less than or equal to the maximum gray level, then the gray scale mapping range value is the absolute value of the difference between the first gray level and the second gray level; if the If the absolute value of the difference between the first gray level and the second gray level is greater than the maximum gray level, then the gray scale mapping range value is the maximum gray level.

Exemplarily, the grayscale mapping range value is determined according to the following formula:

Wherein, R is the grayscale mapping range value, rangeDown is the first grayscale level, rangeUp is the second grayscale level, and n is the maximum grayscale level determined according to the preset bit width of the target image.

S370: Process the image to be processed based on the grayscale mapping relationship to obtain a target image. Exemplarily, the image to be processed is processed based on the following formula:

imgOut(i,j)=HistB(imgIn(i,j)+1)

Among them, imgOut(i,j) is the target image, HistB(imgIn(i,j)+1) is the grayscale mapping relationship, imgIn(i,j) is the grayscale corresponding to the pixel (i,j) of the image to be processed Since the gray level of the histogram in the embodiment of the present application is counted from 1, one is added to the gray level of the image to be processed, so as to correspond to the gray level in the histogram.

In the embodiment of the present application, based on the detailed information of the image to be processed, the first platform threshold and the second platform threshold are determined, and then the dual platform histogram is determined to process the image to be processed, thereby improving the accuracy of the image to be processed in different scenarios. In terms of processing effect, the image processing method in the embodiment of the present application has better scene adaptability for image processing in different scenes.

Fig. 4 is a schematic structural diagram of an image processing device provided by an embodiment of the present application. The device is suitable for processing images. Typically, this embodiment of the present application is applicable to the situation where the image to be processed is enhanced based on the dual-platform histogram. The device can be realized by software and/or hardware, and the device can be integrated in electronic equipment. Referring to Figure 4, the device specifically includes:

A detail histogram determining module 410, configured to determine an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determine a detail histogram of the image to be processed according to the initial histogram;

Threshold determination module 420, configured to, according to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, from the detailed cumulative histogram The first platform threshold and the second platform threshold are determined in the statistical value; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detailed cumulative histogram, and the detailed cumulative histogram is a cumulative histogram of said detail histogram;

A dual-platform histogram determination module 430, configured to determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram;

The processing module 440 is configured to determine a grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image; wherein, the dual-platform cumulative histogram is Cumulative histogram of the two-platform histogram.

In the embodiment of the present application, the detail histogram determination module 410 includes:

a target pixel determination unit, configured to traverse the pixels of the image to be processed, and use the pixels of the image to be processed with the same gray level as the target pixel;

The initial histogram determining unit is configured to determine the initial histogram according to the sum of the detail data corresponding to the target pixel in the high-pass filtered image and the corresponding gray level.

In the embodiment of the present application, the detail histogram determination module 410 includes:

A filter histogram determination unit, configured to remove items whose ordinate values are zero in the initial histogram, and compress the abscissa values of the initial histogram to obtain a filter histogram;

The sorting unit is configured to sort the ordinate values of the filtered histogram in order of size to obtain a detail histogram.

In the embodiment of this application, the threshold determination module 420 includes:

The first platform threshold determination unit is configured to use the statistical value of the detail accumulation histogram that is greater than the first reference threshold and has the smallest difference with the first reference threshold as the first platform threshold; and,

The second platform threshold determination unit is configured to use the statistical value of the detail accumulation histogram that is greater than the second reference threshold and has the smallest difference with the second reference threshold as the second platform threshold;

Correspondingly, the device also includes:

A gray level determining unit, configured to determine a first gray level corresponding to the first platform threshold and a second gray level corresponding to the second platform threshold in the detail cumulative histogram.

In this embodiment of the application, the first platform threshold is smaller than the second platform threshold;

Correspondingly, the dual-platform histogram determination module 430 includes:

A first comparison unit, configured to replace the statistical value of the initial histogram with the first platform threshold if the statistical value of the initial histogram is smaller than the first platform threshold;

A second comparison unit, configured to replace the statistical value of the initial histogram with the second platform threshold if the statistical value of the initial histogram is greater than the second platform threshold;

A third comparing unit, configured to retain the statistical value of the initial histogram if the statistical value of the initial histogram is greater than or equal to the first platform threshold and less than or equal to the second platform threshold.

In the embodiment of this application, the processing module 440 includes:

A statistical ratio value determining unit, configured to determine the statistical value ratio according to the statistical value of the dual-platform cumulative histogram corresponding to each gray level and the statistical value of the dual-platform cumulative histogram corresponding to the maximum gray level;

The gray-scale mapping relationship determination unit is configured to determine the gray-scale mapping relationship according to the product of the statistical value ratio and the gray-scale mapping range value.

In the embodiment of the present application, the device further includes:

The maximum gray level determination module is used to determine the maximum gray level of the target image according to the preset bit width of the target image;

The first range value determination module is configured to: if the absolute value of the difference between the first gray level and the second gray level is less than or equal to the maximum gray level, then the gray mapping range value is the absolute value of the difference between the first gray level and the second gray level;

A second range value determination module, configured to determine the grayscale mapping range value as a maximum grayscale if the absolute value of the difference between the first grayscale level and the second grayscale level is greater than the maximum grayscale level class.

The image processing device provided in the embodiment of the present application can execute the image processing method provided in any embodiment of the present application, and has corresponding functional modules and beneficial effects for executing the method.

Fig. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. FIG. 5 shows a block diagram of an exemplary electronic device 512 suitable for implementing embodiments of the present application. The electronic device 512 shown in FIG. 5 is only an example, and should not limit the functions and scope of use of this embodiment of the present application.

As shown in FIG. 5 , the electronic device 512 may include: one or more processors 516 ; a memory 528 for storing one or more programs, when the one or more programs are executed by the one or more processors 516 Execute, so that the one or more processors 516 implement the image processing method provided in the embodiment of the present application, including:

determining an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;

According to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, determine the first value from the statistical value of the detailed cumulative histogram. A platform threshold and a second platform threshold; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detail cumulative histogram, and the detail cumulative histogram is the accumulation of the detail histogram histogram;

determining a dual platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;

Determine the grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image; wherein, the dual-platform cumulative histogram is the dual-platform histogram The cumulative histogram of .

Components of electronic device 512 may include, but are not limited to: one or more processors or processor 516 , memory 528 , bus 518 connecting different device components including memory 528 and processor 516 .

Bus 518 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. These architectures include, by way of example, but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Process ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect ( PCI) bus.

Electronic device 512 typically includes a variety of computer device-readable storage media. These storage media may be any available storage media that can be accessed by the electronic device 512, including volatile and non-volatile storage media, removable and non-removable storage media.

Memory 528 may include computer device-readable storage media in the form of volatile memory, such as random access memory (RAM) 530 and/or cache memory 532 . Electronic device 512 may further include other removable/non-removable, volatile/nonvolatile computer device storage media. By way of example only, storage system 534 may be used to read and write to non-removable, non-volatile magnetic storage media (not shown in FIG. 5, commonly referred to as "hard drives"). Although not shown in FIG. 5, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") may be provided, as well as a removable non-volatile disk (such as a CD-ROM, DVD-ROM Or other optical storage media) CD-ROM drive. In these cases, each drive may be connected to bus 518 through one or more data storage media interfaces. The memory 528 may include at least one program product having a set (for example, at least one) of program modules configured to perform the functions of the various embodiments of the present application.

A program/utility tool 540 having a set (at least one) of program modules 542, including but not limited to operating the device, one or more application programs, other program modules, and program data, may be stored, for example, in memory 528 , each or some combination of these examples may include implementations of network environments. The program module 542 generally executes the functions and/or methods in the embodiments described in this application.

The electronic device 512 may also communicate with one or more external devices 514 (e.g., a keyboard, pointing device, display 524, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 512, and/or communicate with Any device that enables the electronic device 512 to communicate with one or more other computing devices (eg, network card, modem, etc.). Such communication may occur through input/output (I/O) interface 522 . Moreover, the electronic device 512 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 520 . As shown in FIG. 5 , network adapter 520 communicates with other modules of electronic device 512 via bus 518 . It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with electronic device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape Drives and data backup storage devices, etc.

The processor 516 executes at least one of the other programs stored in the memory 528 to perform various functional applications and data processing, for example, to implement an image processing method provided in the embodiment of the present application.

An embodiment of the present application provides a storage medium containing computer-executable instructions, the computer-executable instructions are used to execute an image processing method when executed by a computer processor, including:

determining an initial histogram according to the high-pass filtered image of the image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;

According to the relationship between the first reference threshold and the statistical value of the detailed cumulative histogram, and the relationship between the second reference threshold and the statistical value of the detailed cumulative histogram, determine the first value from the statistical value of the detailed cumulative histogram. A platform threshold and a second platform threshold; wherein, the first reference threshold and the second reference threshold are determined according to the maximum statistical value of the detail cumulative histogram, and the detail cumulative histogram is the accumulation of the detail histogram histogram;

determining a dual platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;

Determine the grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image; wherein, the dual-platform cumulative histogram is the dual-platform histogram The cumulative histogram of .

The computer storage medium in the embodiments of the present application may use any combination of one or more computer-readable storage media. The computer readable storage medium may be a computer readable signal storage medium or a computer readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer readable storage media include: electrical connections with one or more leads, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the embodiments of the present application, a computer-readable storage medium may be any tangible storage medium containing or storing a program, and the program may be used by or in combination with an instruction execution device, device, or device.

A computer readable signal storage medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave traveling as a data signal. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal storage medium may also be any computer-readable storage medium other than a computer-readable storage medium that can be sent, propagated, or transmitted for use by or in conjunction with an instruction execution device, apparatus, or device the program used.

Program code embodied on a computer readable storage medium may be transmitted using any appropriate storage medium, including - but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, and conventional Procedural Programming Language - such as "C" or a similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or device. In cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).

Note that the above are only preferred embodiments and technical principles used in this application. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can also include more other equivalent embodiments without departing from the concept of the present application, and the present application The scope is determined by the scope of the appended claims.

Claims (10)

1. An image processing method, the method comprising:

determining an initial histogram according to a high-pass filtered image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;

determining a first platform threshold value and a second platform threshold value from the statistic value of the detail cumulative histogram according to the relation between a first reference threshold value and the statistic value of the detail cumulative histogram and the relation between a second reference threshold value and the statistic value of the detail cumulative histogram; the first reference threshold value and the second reference threshold value are determined according to the maximum statistical value of the detail cumulative histogram, wherein the detail cumulative histogram is the cumulative histogram of the detail histogram; the statistical value of the detail cumulative histogram is a statistical value obtained by accumulating the sum of detail data of the detail histogram;

Determining a dual-plateau histogram according to the first plateau threshold, the second plateau threshold and the initial histogram;

determining a gray mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray mapping relation to obtain a target image; wherein the dual plateau cumulative histogram is a cumulative histogram of the dual plateau histogram.

2. The method of claim 1, wherein determining an initial histogram from a high pass filtered image of an image to be processed and the image to be processed comprises:

traversing the pixel points of the image to be processed, and taking the pixel points of the image to be processed with the same gray level as target pixel points;

and determining the initial histogram according to the sum of detail data corresponding to the target pixel point in the high-pass filtered image and the corresponding gray level.

3. The method of claim 2, wherein determining a detail histogram of the image to be processed from the initial histogram comprises:

removing the item with the ordinate value of zero in the initial histogram, and compressing the abscissa value of the initial histogram to obtain a filtered histogram;

And arranging the ordinate values of the filtering histogram according to the order of magnitude to obtain a detail histogram.

4. A method according to any of claims 1-3, wherein determining a first plateau threshold and a second plateau threshold from the statistics of the cumulative histograms of details based on the relationship of a first reference threshold to the statistics of the cumulative histograms of details and the relationship of a second reference threshold to the statistics of the cumulative histograms of details comprises:

taking the statistic value of the detail cumulative histogram which is larger than the first reference threshold and has the smallest difference value with the first reference threshold as a first platform threshold; the method comprises the steps of,

taking the statistic value of the detail cumulative histogram which is larger than the second reference threshold and has the smallest difference value with the second reference threshold as a second platform threshold;

accordingly, the method further comprises:

and determining a first gray level corresponding to the first platform threshold value and a second gray level corresponding to the second platform threshold value in the detail cumulative histogram.

5. A method according to any of claims 1-3, wherein the first plateau threshold is less than the second plateau threshold;

Accordingly, determining a dual plateau histogram from the first plateau threshold, the second plateau threshold, and the initial histogram, comprising:

if the statistical value of the initial histogram is smaller than the first platform threshold value, replacing the statistical value of the initial histogram by the first platform threshold value;

if the statistical value of the initial histogram is larger than the second platform threshold value, replacing the statistical value of the initial histogram by the second platform threshold value;

and if the statistical value of the initial histogram is greater than or equal to the first platform threshold value and less than or equal to the second platform threshold value, reserving the statistical value of the initial histogram.

6. The method of claim 4, wherein determining a gray mapping from the dual plateau cumulative histogram comprises:

determining a statistic ratio according to the statistic value of the double-platform cumulative histogram corresponding to each gray level and the statistic value of the double-platform cumulative histogram corresponding to the maximum gray level;

and determining the gray mapping relation according to the product of the statistic ratio and the gray mapping range value.

7. The method of claim 6, wherein the determining of the gray mapping range value comprises:

Determining the maximum gray level of the target image according to the preset bit width of the target image;

if the absolute value of the difference between the first gray level and the second gray level is smaller than or equal to the maximum gray level, the gray mapping range value is the absolute value of the difference between the first gray level and the second gray level;

and if the absolute value of the difference between the first gray level and the second gray level is larger than the maximum gray level, the gray mapping range value is the maximum gray level.

8. An image processing apparatus, characterized in that the apparatus comprises:

the detail histogram determining module is used for determining an initial histogram according to a high-pass filtered image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;

the threshold determining module is used for determining a first platform threshold and a second platform threshold from the statistic value of the detail cumulative histogram according to the relation between the first reference threshold and the statistic value of the detail cumulative histogram and the relation between the second reference threshold and the statistic value of the detail cumulative histogram; the first reference threshold value and the second reference threshold value are determined according to the maximum statistical value of the detail cumulative histogram, wherein the detail cumulative histogram is the cumulative histogram of the detail histogram; the statistical value of the detail cumulative histogram is a statistical value obtained by accumulating the sum of detail data of the detail histogram;

The dual-platform histogram determination module is used for determining a dual-platform histogram according to the first platform threshold value, the second platform threshold value and the initial histogram;

the processing module is used for determining a gray mapping relation according to the double-platform cumulative histogram and processing the image to be processed based on the gray mapping relation to obtain a target image; wherein the dual plateau cumulative histogram is a cumulative histogram of the dual plateau histogram.

9. An electronic device, the electronic device comprising:

one or more processors;

a memory for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the image processing method of any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the image processing method according to any one of claims 1-7.

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