WO2020038124A1

WO2020038124A1 - Image contrast enhancement method and apparatus, and device and storage medium

Info

Publication number: WO2020038124A1
Application number: PCT/CN2019/094263
Authority: WO
Inventors: 全晓荣; 陈洪波
Original assignee: 深圳创维－Rgb电子有限公司
Priority date: 2018-08-21
Filing date: 2019-07-01
Publication date: 2020-02-27
Also published as: CN109191395A; CN109191395B

Abstract

An image contrast enhancement method and apparatus, and a device and a storage medium. The method comprises: acquiring a grayscale image of a target image (S101); performing global contrast enhancement on the grayscale image to obtain a global grayscale image (S102); dividing the grayscale image into a plurality of sub-regions, taking each sub-region as a target sub-region on the basis of a pre-set order, and adjusting a grayscale value of the target sub-region according to a grayscale value of a sub-region adjacent to the target sub-region to generate a local grayscale image (S103); and determining, according to the global grayscale image and the local grayscale image, an enhanced grayscale image after the contrast of the target image is enhanced (S104).

Description

Image contrast enhancement method, device, equipment and storage medium

This application claims priority from a Chinese patent application filed with the Chinese Patent Office on August 21, 2018 with application number 201810952870.7, the entire contents of which are incorporated herein by reference.

Technical field

Embodiments of the present application relate to image processing, for example, an image contrast enhancement method, device, device, and storage medium.

Background technique

Nowadays, the most common image contrast enhancement method is global contrast enhancement. For global contrast enhancement, if the parameter setting is too strong, the processed image contrast will be too strong, unnatural, and some details will decrease or even disappear; if the parameter setting is too high, Weak, the ideal contrast enhancement effect cannot be achieved, so the two aforementioned global contrast enhancement methods have poor adaptability to the picture; after realizing the limitations of global contrast, consider adding local area contrast enhancement technology, but The effects of the local area contrast enhancement technology in the related art are also poorly adaptable to the picture.

In summary, the related art image contrast enhancement scheme has poor adaptability.

Summary of the Invention

The embodiments of the present application provide an image contrast enhancement method, device, device, and storage medium to avoid a situation where the adaptability of the image contrast enhancement scheme in the related art is poor.

In a first aspect, an embodiment of the present application provides an image contrast enhancement method, including: acquiring a grayscale image of a target image; performing global contrast enhancement on the grayscale image to obtain a global grayscale image; The map is divided into multiple sub-regions, each sub-region is taken as a target sub-region based on a preset order, and the gray-scale values of the target sub-region are adjusted according to the gray-scale values of adjacent sub-regions of the target sub-region to generate A local grayscale image; and an enhanced grayscale image with enhanced contrast of the target image according to the global grayscale image and the local grayscale image.

In a second aspect, an embodiment of the present application further provides an image contrast enhancement device, including: a grayscale image acquisition module configured to acquire a grayscale image of a target image; and a global grayscale image module configured to perform a grayscale image processing on the grayscale image. Global contrast enhancement is performed to obtain a global grayscale image; a local grayscale module is configured to divide the grayscale image into multiple sub-regions, and use each sub-region as a target sub-region based on a preset order. The gray value of an adjacent sub-region of the region adjusts the gray value of the target sub-region to generate a local gray map; the enhanced gray map determination module is configured to be based on the global gray map and the local The grayscale image determines the enhanced grayscale image after the contrast of the target image is enhanced.

According to a third aspect, an embodiment of the present application further provides a device, where the device includes: at least one processor; a storage device configured to store at least one program; and when the at least one program is executed by the at least one processor, The at least one processor is caused to implement the image contrast enhancement method according to the first aspect.

According to a fourth aspect, an embodiment of the present application further provides a storage medium including computer-executable instructions, wherein the computer-executable instructions are configured to execute the image contrast enhancement according to the first aspect when executed by a computer processor. method.

BRIEF DESCRIPTION OF THE DRAWINGS

1 is a flowchart of an image contrast enhancement method according to an embodiment of the present application;

2 is a flowchart of a global contrast enhancement method based on a fitted curve method according to an embodiment of the present application;

3 is a schematic diagram of a non-linear adjustment curve of a low-brightness area provided by an embodiment of the present application;

4 is a schematic diagram of a non-linear adjustment curve of a medium-bright area provided by an embodiment of the present application;

5 is a schematic diagram of a non-linear adjustment curve of a bright area provided by an embodiment of the present application;

6 is a schematic diagram of a histogram and a weight curve provided by an embodiment of the present application;

7 is a structural block diagram of an image contrast enhancement device according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a device according to an embodiment of the present application.

detailed description

The following will clearly and completely describe the technical solution of the present application through implementation with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are a part of the embodiments of the present application, but not all of the embodiments.

FIG. 1 is a flowchart of an image contrast enhancement method according to an embodiment of the present application. The technical solution of this embodiment is applicable to a case where contrast enhancement is performed on an image. This method may be executed by an image contrast enhancement device provided in the embodiment of the present application, and the device may be implemented by using at least one of software and hardware, and configured to be applied in a processor. The method includes steps S101 to S104.

In step S101, a grayscale image of a target image is acquired.

A grayscale image of the target image to be processed is obtained. The grayscale number of the grayscale image can be set according to the use scenario. This implementation is not limited herein, and the grayscale number is 1024.

In step S102, global contrast enhancement is performed on the grayscale image to obtain a global grayscale image.

In order to obtain a good contrast effect, this embodiment first performs global contrast enhancement on a grayscale image to obtain a global grayscale image, and global contrast enhancement can use a fitting curve method to perform global contrast enhancement on a grayscale image to obtain a global grayscale image. Degree chart.

In step S103, the grayscale image is divided into a plurality of subregions, each subregion is used as a target subregion based on a preset order, and the grayscale value of the target subregion is based on the grayscale values of the adjacent subregions of the target subregion. Make adjustments to generate a local grayscale image.

In order to improve the contrast enhancement effect of the image, in this embodiment, based on the global contrast enhancement, a local contrast enhancement is also introduced. The grayscale image is divided into multiple sub-regions, and each sub-region is used as a target sub-region based on a preset order. The gray values of adjacent sub-regions of the sub-regions are used to adjust the gray value of the target sub-region to generate a local gray map.

In an embodiment, in order to improve the flexibility of subregion division, this embodiment first performs edge filtering on the grayscale image to obtain an edge image, and then divides the grayscale image into multiple subregions based on the edges of the edge image. Among them, the edge filtering method can adopt Sobel operator, and the method for dividing the gray image based on the edge image includes:

The edge difference Sobel operator is used to calculate the horizontal difference G _x and the vertical difference G _y , as follows:

The gradient mode and direction are calculated based on the horizontal difference G _x and the vertical difference G _y , as follows:

θ = a tan 2 (G _y , G _x )

The gradient angle θ ranges from radians -π to π. It is approximated to four directions, representing horizontal, vertical, and two diagonal directions (0 °, 45 °, 90 °, 135 °). Can be

Segmentation, giving a specific value to the gradient angle falling in each area, representing one of the four directions, so that the edge image is obtained. Because the edge images and gradient results of different images are different, the shape and number of subregions of different images are different. Therefore, the number and shape of the sub-regions in this embodiment are not fixed and will vary with different target images. This improves the flexibility of local grayscale image processing and adaptability to the target image.

After the subregions are determined, the grayscale distribution of each subregion usually needs to be adjusted. In this embodiment, the grayscale value of the target subregion is adjusted according to the grayscale value of the adjacent subregion of the target subregion, such as the average value of the grayscale. To generate a local grayscale image. In one embodiment, the sum of the difference between the average gray value of the target sub-region and each adjacent sub-region of the target sub-region is calculated; the gray-scale distribution of the target sub-region is adjusted according to the sum of the differences to generate a local gray Degree chart. In order to improve the speed of image processing, this embodiment summarizes the adjustment strategies corresponding to the sum of each difference in a table. In this way, when processing each target subregion, as long as the correspondence is found in the table according to the sum of the differences The adjustment strategy is sufficient, and the adjustment strategy here is grayscale mapping. In an embodiment, assuming that the difference is the difference between the target subregion and its neighboring region, when the sum of the differences is greater than positive, it indicates that the overall gray value of the target subregion is high, and the The grayscale is compressed; when the sum of the difference values is negative, it indicates that the overall grayscale value of the target subregion is low, and then the grayscale is increased by grayscale mapping. Adjusting the gray distribution of the target subregion by the gray average value of adjacent images can help improve the adaptability of local contrast enhancement to different images, and make the contrast enhancement effect of the entire image more natural.

Exemplarily, assuming that the average gray value of the target sub-region is 100 and the average gray values of the four adjacent sub-regions are 80, 90, 110, and 110, respectively, The sum of the differences of the average gray values is -10, and then look up the table to search for the adjustment strategy corresponding to the sum of the differences of -10 to adjust the gray values of the target subregion.

In step S104, an enhanced grayscale image with enhanced contrast of the target image is determined according to the global grayscale image and the local grayscale image.

After the global grayscale image and the local grayscale image are determined, the global grayscale image and the local grayscale image are superimposed to generate an enhanced grayscale image after the target image is enhanced. The enhancement processing has high image adaptability, so the enhanced grayscale image has a better sense of detail and depth of field, and the picture is more natural and realistic.

The technical scheme of the image contrast enhancement method provided by the embodiment of the present application includes: obtaining a grayscale image of a target image; performing global contrast enhancement on the grayscale image to obtain a global grayscale image; dividing the grayscale image into multiple sub-regions, Based on a preset order, each sub-region is used as a target sub-region, and the gray-scale value of the target sub-region is adjusted according to the gray-scale values of adjacent sub-regions of the target sub-region to generate a local gray map; according to the global gray map And the local grayscale image to determine the enhanced grayscale image after the contrast of the target image is enhanced. The local gray map of the current sub-region is determined by combining the average gray values of adjacent sub-regions. While achieving the dynamic enhancement of local contrast, the overall effect of the picture can also be improved. Enhancing the grayscale image can show more details of the picture to present a more realistic and natural picture.

FIG. 2 is a flowchart of an image contrast enhancement method according to an embodiment of the present application. The embodiment of the present application further describes the global contrast enhancement method based on the fitted curve method based on the foregoing embodiment, and includes steps S1021 to S1025.

In step S1021, a grayscale histogram of the grayscale image is determined based on a preset number of grayscale levels, and the grayscale image is divided into a corresponding number of brightness regions based on a preset number of brightness levels.

The preset number of gray levels can be set according to the actual use scene. The preset number of gray levels in this embodiment can be 32 levels that can be distinguished by the human eye, that is, the 32 levels of gray of the target image gray map that needs to be determined Histogram.

The preset number of brightness levels can be set according to the actual use scenario. In this embodiment, the number of preset brightness levels is 3, that is, the grayscale image is divided into 3 brightness regions, which are a low-light region and a medium-light region. And highlights.

In step S1022, a non-linear adjustment curve of each brightness region is obtained.

According to the adjustment target of each brightness area, a non-linear adjustment curve is set for each brightness area. It can be understood that the shape of the non-linear adjustment curve can be set according to the usage scenario, and the non-linear adjustment curve is used to roughly reflect the contrast distribution of the brightness region where it is located. The non-linear adjustment curve of this embodiment is shown in FIG. 3, FIG. 4, and FIG. 5, and the dotted line is a 45 ° straight line with the coordinate axis, indicating that the contrast of the image before and after processing is the same. In general, when the value of the non-linear curve is greater than 1 (the non-linear adjustment curve is higher than the dotted line), the highlight portion of the image is compressed and the dark-tone portion is expanded. When the value of the non-linear curve is less than 1 (the non-linear adjustment curve is lower than the dotted line) ), The highlight part of the image is expanded and the dark part is compressed.

Among them, the three non-linear adjustment curves correspond to three look-up tables, and the low-luminance area corresponding to the low-luminance area adjustment curve look-up table for the low-luminance area is Lut_0 [32], and the medium-luminance area non-linear adjustment curve corresponds to the medium-luminance area. The lookup table of the adjustment curve of the high-brightness area is Lut_1 [32], and the lookup table of the adjustment curve of the high-brightness area corresponding to the non-linear adjustment curve of the high-brightness area is Lut_2 [32].

In step S1023, the global gain and brightness weight coefficient of each brightness area are obtained as the global gain and brightness weight coefficient corresponding to each gray level of the brightness area.

The global gain of each luminance region is obtained as the global gain corresponding to each gray level of the luminance region, where the global gain is an empirical value.

Obtain the brightness weight coefficient of each brightness area as the brightness weight coefficient corresponding to each gray level number of the brightness area. The method for obtaining the brightness weight coefficient is: as shown in FIG. 6, one and a gray scale is set for each brightness area. The brightness weight curve corresponding to the histogram, wherein the brightness weight curve is used to represent the ratio of the brightness value of each gray level in the current brightness area to the sum of the brightness values of each gray level in all brightness areas. The three brightness weight curves correspond to three lookup tables, the weight lookup table for the low brightness area corresponding to the weight curve for the low brightness area is BinWeighting_low_LUT [32], and the weight lookup table for the medium brightness area corresponding to the weight curve for the medium brightness area is BinWeighting_mid_LUT [ 32]. The weight lookup table of the high-brightness area corresponding to the weight curve of the high-brightness area is BinWeighting_high_LUT [32].

After the weight curve is determined, the product of the brightness weight curve of each brightness area and all corresponding gray levels is calculated as the weight of the brightness area, where the weight of the low light area is expressed as Metric [0], and the weight of the medium light area is expressed Is Metric [1], and the weight of the highlighted area is expressed as Metric [2]. MetricSum = Metric [0] + Metric [1] + Metric [2], corresponding to the sum of the weights of the three brightness regions, then the weight coefficients of the three brightness regions are:

The weight coefficient of the low light area is:

The weight coefficient of the middle bright area is:

The weighting coefficient of the highlighted area is:

In step S1024, the product of the non-linear adjustment curve value, the global gain, and the weight coefficient of each gray level of each luminance region is used as a single luminance adjustment value, and the single luminance adjustment value of different luminance regions corresponding to each gray level is used. The sum is used as the adjustment value for each gray level.

Find the product of the non-linear adjustment curve value, the global gain, and the weight coefficient of each gray level of each luminance region as a single luminance adjustment value of each gray level, and then obtain the different luminance regions corresponding to each gray level The sum of the single brightness adjustment values is used as the adjustment value for each gray level. The comprehensive expression of the adjustment value is:

LUT [i] = G1bGain0 * W0 * Lut_0 [i] + G1bGain1 * W1 * Lut_1 [i] + G1bGain2 * W2 * Lut_2 [i]

Among them, GlbGain0 is the global gain in the low light area, GlbGain1 is the global gain in the medium light area, and GlbGain2 is the global gain in the high light area.

From the above formula, it can be known that the adjustment value of each gray level contains information of each brightness area, which helps to improve the adaptability of the global contrast enhancement to the image and the image contrast enhancement effect of the global grayscale image.

In step S1025, the grayscale image is adjusted according to the adjustment value of each grayscale to obtain a global grayscale image.

Since the gray histogram and the non-linear adjustment curve graph are both 32-level gray histograms, the adjustment value of the gray level is also based on the 32 level. Therefore, after the adjustment value of each gray level is determined, the The adjustment value of each gray level of the degree histogram can be adjusted by linear interpolation to obtain the adjustment value of each gray level of the 1024-level gray histogram, and then obtain the global gray according to the adjustment value of each gray level of the 1024-level gray histogram. Degree chart.

In the embodiment of the present application, the adjustment effect of each non-linear adjustment curve can be effectively balanced by the weight coefficient, and then the global grayscale image of the target image can be determined quickly and accurately.

FIG. 7 is a structural block diagram of an image contrast enhancement device according to an embodiment of the present application. The device is configured to execute the image contrast enhancement method provided by any of the foregoing embodiments, and the device may be implemented by software or hardware. The device includes a grayscale image acquisition module 11, a global grayscale image module 12, a local grayscale image module 13, and an enhanced grayscale image determination module 14.

The gray image acquisition module 11 is configured to acquire a gray image of a target image.

The global grayscale image module 12 is configured to perform global contrast enhancement on a grayscale image to obtain a global grayscale image.

The local gray map module 13 is configured to divide the gray map into a plurality of sub-areas, use each sub-area as a target sub-area based on a preset order, and compare the gray-scale values of adjacent sub-areas of the target sub-area. The gray value of the target sub-region is adjusted to generate a local gray map.

The enhanced grayscale image determination module 14 is configured to determine an enhanced grayscale image with enhanced contrast of the target image according to the global grayscale image and the local grayscale image.

The image contrast enhancement device provided by the embodiment of the present application includes: a grayscale image acquisition module configured to acquire a grayscale image of a target image; a global grayscale image module configured to perform global contrast enhancement on a grayscale image to obtain a global grayscale Degree map; a local gray map module, configured to divide the gray map into a plurality of sub-areas, using each sub-area as a target sub-area based on a preset order, and according to the gray levels of adjacent sub-areas of the target sub-area The gray value of the target sub-region is adjusted to generate a local gray map; the enhanced gray map determination module is configured to determine the enhanced gray map of the target image after the contrast is enhanced according to the global gray map and the local gray map. . The local gray map of the current sub-region is determined by combining the average gray values of adjacent sub-regions. While achieving the dynamic enhancement of local contrast, the overall effect of the picture can also be improved. Enhancing the grayscale image can show more details of the picture to present a more realistic and natural picture.

The image contrast enhancement device provided by the embodiment of the present application can execute the image contrast enhancement method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

8 is a schematic structural diagram of a device according to an embodiment of the present application. As shown in FIG. 8, the device includes a processor 201, a memory 202, an input device 203, and an output device 204. The number of processors 201 in the device may be one or In FIG. 8, one processor 201 is taken as an example; the processor 201, the memory 202, the input device 203, and the output device 204 in the device may be connected through a bus or other methods. In FIG. 8, the connection through a bus is used as an example.

The memory 202 is a computer-readable storage medium, and may be configured to store software programs, computer-executable programs, and modules, such as program instructions / modules (for example, grayscale image acquisition modules) corresponding to the image contrast enhancement method in the embodiments of the present application. 11. Global gray map module 12, local gray map module 13, and enhanced gray map determination module 14). The processor 201 executes various functional applications and data processing of the device by running software programs, instructions, and modules stored in the memory 202, that is, the above-mentioned image contrast enhancement method is implemented.

The memory 202 may mainly include a storage program area and a storage data area, where the storage program area may store an operating system and application programs required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. In addition, the memory 202 may include a high-speed random access memory, and may further include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some examples, the memory 202 may further include memory remotely set with respect to the processor 201, and these remote memories may be connected to the device through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The input device 203 may be configured to receive inputted numeric or character information and generate key signal inputs related to user settings and function control of the device.

The output device 204 may include a display device such as a display screen, for example, a display screen of a user terminal.

Embodiment 5 of the present application further provides a storage medium containing computer-executable instructions. When the computer-executable instructions are executed by a computer processor, the method is configured to execute an image contrast enhancement method. The method includes: obtaining a gray image of a target image. Degree map; performing global contrast enhancement on the grayscale image to obtain a global grayscale image; dividing the grayscale image into a plurality of subregions, and using each subregion as a target subregion based on a preset order, according to the target The gray values of adjacent sub-regions of the sub-regions are used to adjust the gray value of the target sub-region to generate a local gray map.

An enhanced grayscale image with enhanced contrast of the target image is determined according to the global grayscale image and the local grayscale image.

Certainly, a storage medium including computer-executable instructions provided in the embodiments of the present application is not limited to the method operations described above, and may also be performed in the image contrast enhancement method provided by any embodiment of the present application. Related operations.

Through the above description of the implementation, those skilled in the art can clearly understand that this application can be implemented by software and necessary general hardware, and of course, can also be implemented by hardware, but in many cases the former is a better implementation. . Based on such an understanding, the technical solution of this application that is essential or contributes to related technologies may be embodied in the form of a software product. The computer software product may be stored in a computer-readable storage medium, such as a computer floppy disk, Read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including multiple instructions to make a computer device (can be a personal computer , Server, or network device, etc.) execute the image contrast enhancement method described in each embodiment of the present application.

It is worth noting that in the embodiment of the image contrast enhancement device described above, each unit and module included is only divided according to functional logic, but is not limited to the above division, as long as the corresponding function can be realized; in addition, The specific names of the functional units are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application.

Claims

An image contrast enhancement method includes:

Obtain the grayscale image of the target image;

Performing global contrast enhancement on the grayscale image to obtain a global grayscale image;

Dividing the grayscale image into multiple subregions, using each subregion as a target subregion based on a preset order, and comparing the grayscale value of the target subregion with the grayscale value of an adjacent subregion of the target subregion Make adjustments to generate a local grayscale image;

An enhanced grayscale image with enhanced contrast of the target image is determined according to the global grayscale image and the local grayscale image.
The method according to claim 1, wherein performing the global contrast enhancement on the grayscale image to obtain a global grayscale image comprises:

Global contrast enhancement is performed on the grayscale image based on a fitting curve method to obtain a global grayscale image.
The method according to claim 2, wherein the performing a global contrast enhancement on the grayscale image based on the fitted curve method to obtain a global grayscale image comprises:

Determining a grayscale histogram of the grayscale image based on a preset number of grayscale levels, and dividing the grayscale image into a corresponding number of brightness regions based on a preset number of brightness levels;

Obtaining a non-linear adjustment curve for each brightness region;

Obtaining a global gain and a brightness weighting coefficient of each brightness area as the global gain and the brightness weighting coefficient corresponding to each gray level of the brightness area;

The product of the non-linear adjustment curve value, the global gain, and the weight coefficient of each gray level of each luminance region is taken as a single luminance adjustment value, and the sum of the single luminance adjustment values of different luminance regions corresponding to each gray level is taken as each Gray scale adjustment value;

The grayscale image is adjusted according to the adjustment value of each grayscale to obtain a global grayscale image.
The method according to claim 3, wherein obtaining the brightness weight coefficient of each brightness region comprises:

For each luminance region, a luminance weight curve corresponding to the grayscale histogram is set, wherein the luminance weight curve is used to represent the luminance value of each grayscale in the current luminance region and each grayscale in all luminance regions. The ratio of the sum of the brightness values;

The ratio of the sum of all brightness weights corresponding to the brightness weight curve of each brightness area and the sum of all brightness weights corresponding to the brightness weight curves of all brightness areas is calculated as the weight coefficient of each brightness area.
The method according to claim 3, wherein the adjusting the grayscale image according to the adjustment value of each grayscale to obtain a global grayscale image comprises:

According to the adjustment value of each gray level, a global gray image is obtained by linear interpolation.
The method according to claim 1, wherein the dividing the grayscale image into a plurality of sub-regions comprises:

Performing edge filtering on the grayscale image to obtain an edge image;

The grayscale image is divided into a plurality of sub-regions based on the edges of the edge image.
The method according to claim 6, wherein the edge filtering uses a Sobel operator.
The method according to claim 6, wherein the adjusting the gray value of the target sub-region according to the gray value of an adjacent sub-region of the target sub-region to generate a local gray map comprises:

Calculate the average gray value of each sub-region;

The gray value of the target sub-region is adjusted according to the average gray value of adjacent sub-regions of the target sub-region to generate a local gray map.
The method according to claim 6, wherein the adjusting the gray value of the target sub-region based on an average gray value of an adjacent sub-region of the target sub-region to generate a local gray map includes: :

Calculating a sum of differences between an average gray value of the target sub-region and an average gray value of each adjacent sub-region of the target sub-region;

The gray distribution of the target sub-region is adjusted according to the sum of the differences to generate a local gray map.
The method according to claim 9, wherein the adjusting the gray distribution of the target sub-region based on the sum of the differences to generate a local gray map comprises:

Each sum of the differences corresponds to an adjustment strategy, and the sum of the differences and the adjustment strategy are summarized in a table, and the corresponding adjustment strategy is found in the table according to the calculated sum of the differences, To adjust the gray distribution of the target sub-region.
The method according to claim 10, wherein the adjustment strategy is a grayscale mapping; the grayscale mapping comprises:

When the sum of the difference between the average gray value of the target sub-region and the average gray value of its neighboring sub-regions is a positive value, the gray levels of the target sub-region are compressed by using the gray-level mapping ; In the case where the sum of the difference between the average gray value of the target sub-region and the average gray value of its neighboring sub-regions is negative, the gray-scale of the target sub-region is increased by the gray mapping high.
The method according to claim 1, wherein the determining an enhanced grayscale image with enhanced contrast of a target image based on the global grayscale image and the local grayscale image comprises:

After the global grayscale image and the local grayscale image are determined, the global grayscale image and the local grayscale image are superimposed to generate an enhanced grayscale image after the target image is enhanced.
An image contrast enhancement device includes:

A grayscale image acquisition module, configured to acquire a grayscale image of a target image;

A global grayscale image module configured to perform global contrast enhancement on the grayscale image to obtain a global grayscale image;

The local grayscale module is configured to divide the grayscale image into multiple sub-areas, use each sub-area as a target sub-area based on a preset order, and compare all the Adjust the gray value of the target sub-region to generate a local gray map;

The enhanced grayscale image determination module is configured to determine an enhanced grayscale image with enhanced target image contrast according to the global grayscale image and the local grayscale image.
A device includes:

At least one processor;

A storage device configured to store at least one or more programs;

When the at least one program is executed by the at least one processor, the at least one processor implements the image contrast enhancement method according to any one of claims 1-9.
A storage medium containing computer-executable instructions, the computer-executable instructions, when executed by a computer processor, are configured to perform the image contrast enhancement method according to any one of claims 1-9.