WO2019153731A1

WO2019153731A1 - Image processing method and system

Info

Publication number: WO2019153731A1
Application number: PCT/CN2018/103920
Authority: WO
Inventors: 金羽锋
Original assignee: 深圳市华星光电半导体显示技术有限公司
Priority date: 2018-02-12
Filing date: 2018-09-04
Publication date: 2019-08-15
Also published as: CN108470329A

Abstract

An image processing method and system. When a target image is partitioned, the maximum and minimum brightness values of the target image and partitioned images are additionally detected to provide reference values for subsequent histogram equalization; and a black frame which is not black enough or a bright region which is too bright in the target image is processed, thus reducing local distortion and scene distortion.

Description

Image processing method and system

Technical field

The present application relates to the field of image processing technologies, and in particular, to an image processing method and system.

Background technique

With the development of displays, image quality is increasingly valued by consumers, so various image algorithms that can improve display effects have been widely used. In the process of image generation, transmission and transformation, due to the influence of various factors, the image quality will always be degraded, thus resulting in image processing technology.

In the prior art, CLAHE (Contrast) is generally adopted. The Limited Adaptive Histogram Equalization (CLAHE) algorithm corrects the image. The CLAHE algorithm is an improvement in the traditional digital image processing algorithm AHE (Adaptive Histogram Equalization).

The CLAHE algorithm uses a recursive process and enhances the visibility of local details of the image by enhancing the contrast of the local regions. The key to the CLAHE algorithm is to limit the magnification of the contrast by cropping the histogram before calculating the conversion function. A parameter of the CLAHE algorithm is used to limit the predefined values: first, the image is divided into several sub-blocks, and the histogram of each sub-block is calculated separately, and each histogram is reallocated by limiting the contrast. However, the existing CLAHE algorithm still leads to excessive enhancement of the local area of the image, and the black picture is not black enough and local distortion.

technical problem

The present application provides an image processing method and system to improve the local distortion of a target image caused by an existing CLAHE algorithm.

Technical solution

The application proposes an image processing method, which includes:

Step S10: input a target image, perform partition processing on the target image, and obtain a partition image;

Step S20, acquiring a maximum brightness Max, a minimum brightness Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

Step S30, performing histogram accumulation on the partition image, performing histogram equalization processing on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image, to obtain the partition. The LUT lookup table corresponding to the image;

Step S40: Obtain a corresponding value of a neighboring partition of each pixel in the target image by using a lookup table (Look Up Table), and calculate a corresponding value of the adjacent partition to obtain a corresponding value. a final value of the brightness of the pixel;

Step S50, combining the final values of the brightness of all the pixels into a new image;

Step S60, correcting the new image by using a correction function, and outputting a final image.

The correction function is a linear function that corrects the maximum brightness value and the minimum brightness value of the new image.

In the image processing method of the present application, the step S30 includes:

Step S31, performing histogram accumulation on the partition image;

Step S32: Obtain a maximum value and a minimum value of the histogram equalization according to the obtained maximum brightness value SubMax and the minimum brightness value SubMin of the partition image;

Step S33, performing histogram equalization processing on the image accumulated by the histogram by using a contrast limiting algorithm;

Step S34: Obtain a LUT lookup table corresponding to the partition image.

In the image processing method of the present application, the maximum value of the histogram equalization is 255-a*(255-SubMax), and the minimum value of the histogram equalization is b*(SubMin), where 0≤a ≤1,0≤b≤1.

In the image processing method of the present application, the step S40 includes:

Step S41: Obtain a corresponding value of four partitions around each pixel in the target image by using the LUT lookup table;

Step S42: Calculate the corresponding values of the four partitions by using bilinear interpolation;

Step S43, obtaining a final value of the brightness of the corresponding pixel.

The present application also provides an image processing system, where the image processing system includes: a partitioning module, a scanning module, a pre-processing module, a computing module, a collection module, and a correction module;

The partitioning module is configured to input a target image, perform partition processing on the target image, and obtain a partition image;

The scanning module is configured to acquire a maximum brightness Max, a minimum brightness Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

The pre-processing module is configured to perform histogram accumulation on the partition image, and perform histogram equalization on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image. Processing, obtaining a LUT lookup table corresponding to the partition image;

The calculating module is configured to obtain a corresponding value of a neighboring partition of each pixel in the target image by using the LUT lookup table, and calculate a corresponding value of the adjacent partition to obtain a corresponding pixel point. The final value of the brightness;

The aggregation module is configured to combine the final values of the brightness of all pixels into a new image;

The correction module is configured to correct the new image by using a correction function, and output a final image.

In the image processing system of the present application, the pre-processing module includes: a histogram accumulation unit, a histogram equalization pre-processing unit, a histogram equalization unit, and an acquisition look-up table unit;

The histogram accumulation unit is configured to perform histogram accumulation on the partition image;

The histogram equalization pre-processing unit is configured to obtain a maximum value and a minimum value of the histogram equalization according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image;

The histogram equalization unit is configured to perform histogram equalization processing on the image accumulated by the histogram by using a contrast limiting algorithm;

The acquiring a lookup table unit is configured to obtain a LUT lookup table corresponding to the partition image.

In the image processing system of the present application, the maximum value of the histogram equalization is 255-a*(255-SubMax), and the minimum value of the histogram equalization is b*(SubMin), where 0≤a≤ 1,0 ≤ b ≤ 1.

In the image processing system of the present application, the calculation module includes: a search unit, a bilinear interpolation unit, and an output unit;

The searching unit is configured to obtain, by using the LUT lookup table, a corresponding value of four partitions around each pixel in the target image;

The bilinear interpolation unit is configured to calculate the corresponding values of the four partitions by using bilinear interpolation;

The output unit obtains a final value of the brightness of the corresponding pixel.

In the image processing system of the present application, the correction function is a linear function that corrects the maximum brightness value and the minimum brightness value of the new image.

The application also proposes an image processing method, which includes:

Step S20, acquiring a maximum brightness value Max, a minimum brightness value Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

Step S30, performing histogram accumulation on the partition image, performing histogram equalization processing on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image, to obtain the partition. a display lookup table corresponding to the image;

Step S40: Obtain a corresponding value of a neighboring partition of each pixel in the target image by using the display lookup table, calculate a corresponding value of the adjacent partition, and obtain a final value of a brightness of the corresponding pixel. ;

Step S31, performing histogram accumulation on the partition image;

Step S34: Obtain a display lookup table corresponding to the partition image.

Step S41: Obtain a corresponding value of four partitions around each pixel in the target image by using the display lookup table;

Step S43, obtaining a final value of the brightness of the corresponding pixel.

Beneficial effect

When the target image is partitioned, the detection of the maximum and minimum values of the target image and the brightness of the partition image is added, and the reference value is provided for the subsequent histogram equalization, and the black image in the target image is not black or bright. The area is too bright for processing, reducing local and scene distortion.

DRAWINGS

In order to more clearly illustrate the embodiments or the technical solutions in the prior art, the drawings to be used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description are merely inventions. For some embodiments, other drawings may be obtained from those of ordinary skill in the art without departing from the drawings.

1 is a schematic flow chart of an image processing method according to the present application;

2 is a schematic diagram of a target image partitioning process of an image processing method according to the present application;

3 is a schematic structural diagram of an image processing system according to the present application;

4 is a schematic structural diagram of an image processing system according to the present application.

BEST MODE FOR CARRYING OUT THE INVENTION

The following description of the various embodiments is provided to illustrate the specific embodiments of the invention. Directional terms mentioned in this application, such as [upper], [lower], [previous], [post], [left], [right], [inside], [outside], [side], etc., are only references Attach the direction of the drawing. Therefore, the directional terminology used is for the purpose of illustration and understanding, and is not intended to be limiting. In the figures, structurally similar elements are denoted by the same reference numerals.

FIG. 1 is a schematic flowchart diagram of an image processing method according to a preferred embodiment, wherein the image processing method includes:

First, the target image is input, and the target image is divided into a plurality of partition images of equal size; wherein the partition refers to horizontal and vertical division of the target image. Partitions can't be divided too much, which will affect the speed of operation; partitions can't be divided too little, which will make the calculation result not accurate enough; for example, the target image can be divided into 4*4 or 3*3 partition images, as shown in Figure 2. The target image is divided into nine equal-sized partition images.

Before performing the partition processing on the target, first scanning the target image to obtain a maximum brightness Max and a minimum brightness Min of the image; and after the target image is partitioned into a plurality of equal partition images, acquiring each using the relevant software. a maximum brightness value SubMax of the partition image, a minimum brightness value SubMin;

Preferably, when the target image is a black and white image, the scanned data is a maximum grayscale and a minimum grayscale of the target image; when the target image is colored, separately processing the three colors may cause serious Color cast, so it can generally be converted into color space (such as RGB to HSV), and then only the luminance component processing, and then inversely converted back to the RGB color space.

First, performing histogram accumulation on the partition image, taking a black and white image as an example. The histogram accumulation refers to performing probability distribution under the accumulation of gray levels, and each probability value represents less than or equal to the gray. Probability of degree

Secondly, according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image, a maximum value and a minimum value of the histogram equalization are obtained; the maximum value of the histogram equalization is 255-a* (255-SubMax) The minimum value of the histogram equalization is b*(SubMin), where 0≤a≤1, 0≤b≤1, and the maximum value and the minimum value are used as the limit values of the subsequent contrast limiting algorithm;

And performing a histogram equalization process on the image accumulated by the histogram by using a contrast limiting algorithm, and obtaining a LUT lookup table corresponding to the partition image;

Taking the black and white image as an example, the histogram equalization process refers to changing the gray histogram of the original image from a certain gray interval in the comparative set to a uniform distribution in the entire gray range. Histogram equalization nonlinearly stretches the image and redistributes the image pixel values so that the number of pixels in a certain gray range is approximately the same, that is, the histogram distribution of a given image is changed to a "uniform" distribution histogram. distributed. For example, if the original image has a gray level of f at (x, y) and the changed image is g, the method of image enhancement can be expressed as mapping the gray level f at (x, y) to g. ;

The LUT is essentially a RAM (Random Access Memory). After it writes the data to the RAM in advance, each time a signal is input, it is equivalent to inputting an address to look up the table, find out the content corresponding to the address, and then output. Therefore, the LUT lookup table is a mapping table of pixel grayscale values, which converts the actually sampled pixel grayscale values into certain transformations such as threshold, inversion, binarization, contrast adjustment, linear transformation, etc. into another A grayscale value corresponding to it.

Step S40: Obtain a corresponding value of a neighboring partition of each pixel in the target image by using the LUT lookup table, and calculate a corresponding value of the adjacent partition to obtain a final value of the brightness of the corresponding pixel. ;

First, a corresponding value of four partitions around each pixel in the target image is obtained by using the LUT lookup table; as shown in FIG. 2, taking the pixel point G as an example, searching for four adjacent partitions 1 of the pixel point G LUT values of the hollow origin corresponding to 2, 4, and 5;

Secondly, calculating the corresponding values of the four partitions by using bilinear interpolation to obtain a final value of the brightness of the corresponding pixel point;

The bilinear interpolation shown, also known as bilinear interpolation. Mathematically, bilinear interpolation is a linear interpolation extension of an interpolation function with two variables. The core idea is to perform linear interpolation in two directions. Bilinear interpolation is a prior art and will not be described in detail here.

Step S60, correcting the new image by using a correction function, and outputting a final image;

The final values of the brightness of all the pixels are combined into a new image. Due to the CLAHE algorithm, the Max of the new image brightness must be smaller than the Max of the target image, and the Min of the new image brightness must be larger than the Min of the target image, which may result in a combination. The local image brightness of the new image is not bright enough or the black area is not black enough, resulting in distortion of the partial image; therefore, in the subsequent steps, the new image needs to be corrected by using a correction function, which is a linear function for the new image. The maximum brightness value and the minimum brightness value are corrected. The linear function is f(x)=mx+n, the new image is mapped to the interval (Min, Max) of the target image, and the final image is output.

The present application proposes an image processing method, first inputting a target image, performing partition processing on the target image to obtain a partition image; secondly, acquiring a maximum brightness Max, a minimum brightness Min, and a maximum of the partition image of the target image. a brightness value SubMax, a minimum brightness value SubMin; performing histogram accumulation on the partition image, and performing histogram equalization on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image Processing, obtaining a LUT lookup table corresponding to the partition image; again, obtaining a corresponding value of a neighboring partition of each pixel in the target image by using the LUT lookup table, and performing corresponding values of the adjacent partitions Calculating, obtaining the final value of the brightness of the corresponding pixel; finally, combining the final values of the brightness of all the pixels into a new image, and correcting the new image with a correction function, and outputting the final image. When the target image is partitioned, the detection of the maximum and minimum values of the target image and the brightness of the partition image is added, and the reference value is provided for the subsequent histogram equalization, and the black image in the target image is not black or bright. The area is too bright for processing, reducing local and scene distortion.

FIG. 3 is a schematic structural diagram of an image processing system according to a preferred embodiment of the present application. The image processing system 30 includes a partitioning module 301, a scanning module 302, a pre-processing module 303, a computing module 304, and a collection module 305. And a correction module 306;

The partitioning module 301 is configured to input a target image, perform partition processing on the target image, and obtain a partition image;

The scanning module 302 is configured to acquire a maximum brightness Max, a minimum brightness Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

The pre-processing module 303 is configured to perform histogram accumulation on the partition image, and perform histogram equalization on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image. Processing, obtaining a LUT lookup table corresponding to the partition image,

The maximum value of the histogram equalization is 255-a*(255-SubMax), and the minimum value of the histogram equalization is b*(SubMin), where 0≤a≤1, 0≤b≤ 1;

The calculating module 304 is configured to obtain, by using the LUT lookup table, a corresponding value of a neighboring partition of each pixel in the target image, and calculate a corresponding value of the adjacent partition to obtain a corresponding pixel. The final value of the brightness;

The aggregation module 305 is configured to combine the final values of the brightness of all pixels into a new image;

The correction module 306 is configured to correct the new image by using a correction function, and output a final image, wherein the correction function is a linear function, and corrects a maximum brightness value and a minimum brightness value of the new image. The correction function is f(x)=mx+n.

As shown in FIG. 4, the pre-processing module 303 includes: a histogram accumulation unit 3031, a histogram equalization pre-processing unit 3032, a histogram equalization unit 3033, and an acquisition look-up table unit 3034;

The histogram accumulation unit 3031 is configured to perform histogram accumulation on the partition image;

The histogram equalization pre-processing unit 3032 is configured to obtain a maximum value and a minimum value of the histogram equalization according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image;

The histogram equalization unit 3033 is configured to perform histogram equalization processing on the image accumulated by the histogram by using a contrast limiting algorithm;

The acquisition lookup table unit 3034 is configured to obtain a LUT lookup table corresponding to the partition image.

As shown in FIG. 4, the calculation module 304 includes: a search unit 3041, a bilinear interpolation unit 3042, and an output unit 3043;

The searching unit 3041 is configured to obtain, by using the LUT lookup table, a corresponding value of four partitions around each pixel in the target image;

The bilinear interpolation unit 3042 is configured to calculate a corresponding value of the four partitions by using bilinear interpolation;

The output unit 3043 obtains a final value of the brightness of the corresponding pixel.

The present application provides a schematic structural diagram of an image processing system, where the image processing system includes: a partitioning module, a scanning module, a pre-processing module, a computing module, a collection module, and a correction module. When the target image is partitioned, the detection of the maximum and minimum values of the target image and the brightness of the partition image is added, and the reference value is provided for the subsequent histogram equalization, and the black image in the target image is not black or bright. The area is too bright for processing, reducing local and scene distortion.

It should be noted that those skilled in the art can understand that all or part of the steps of the foregoing embodiments can be completed by a program to instruct related hardware, and the program can be stored in a computer readable storage medium, such as It is stored in a memory of the terminal and executed by at least one processor within the terminal, and may include a flow of an embodiment of a display method based on a virtual location during execution. The storage medium may include: a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

An image processing method and system provided by the embodiments of the present application are described in detail above, and each functional module may be integrated into one processing chip, or each module may exist separately or may be integrated by two or more modules. In one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. The principles and implementations of the present application are described in the following by using specific examples. The description of the above embodiments is only for helping to understand the method of the present application and its core ideas. Meanwhile, for those skilled in the art, according to the present application, There is a change in the scope of the present invention and the scope of application, and the contents of this specification should not be construed as limiting the present application.

Claims

An image processing method comprising:

Step S10: input a target image, perform partition processing on the target image, and obtain a partition image;

Step S20, acquiring a maximum brightness value Max, a minimum brightness value Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

Step S30, performing histogram accumulation on the partition image, performing histogram equalization processing on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image, to obtain the partition. a display lookup table corresponding to the image;

Step S40: Obtain a corresponding value of a neighboring partition of each pixel in the target image by using the display lookup table, calculate a corresponding value of the adjacent partition, and obtain a final value of a brightness of the corresponding pixel. ;

Step S50, combining the final values of the brightness of all the pixels into a new image;

Step S60, correcting the new image by using a correction function, and outputting a final image.

The correction function is a linear function for correcting the maximum brightness value and the minimum brightness value of the new image.
The image processing method according to claim 1, wherein said step S30 comprises:

Step S31, performing histogram accumulation on the partition image;

Step S32: Obtain a maximum value and a minimum value of the histogram equalization according to the obtained maximum brightness value SubMax and the minimum brightness value SubMin of the partition image;

Step S33, performing histogram equalization processing on the image accumulated by the histogram by using a contrast limiting algorithm;

Step S34: Obtain a display lookup table corresponding to the partition image.
The image processing method according to claim 2, wherein the maximum value of the histogram equalization is 255-a*(255-SubMax), and the minimum value of the histogram equalization is b*(SubMin), wherein 0 ≤ a ≤ 1, 0 ≤ b ≤ 1.
The image processing method according to claim 1, wherein said step S40 comprises:

Step S41: Obtain a corresponding value of four partitions around each pixel in the target image by using the display lookup table;

Step S42: Calculate the corresponding values of the four partitions by using bilinear interpolation;

Step S43, obtaining a final value of the brightness of the corresponding pixel.
An image processing system, comprising: a partitioning module, a scanning module, a preprocessing module, a computing module, a collection module, and a correction module;

The partitioning module is configured to input a target image, perform partition processing on the target image, and obtain a partition image;

The scanning module is configured to acquire a maximum brightness Max, a minimum brightness Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

The pre-processing module is configured to perform histogram accumulation on the partition image, and perform histogram equalization processing on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image. Obtaining a display lookup table corresponding to the partition image;

The calculating module is configured to obtain a corresponding value of a neighboring partition of each pixel in the target image by using the display lookup table, and calculate a corresponding value of the adjacent partition to obtain a brightness of the corresponding pixel Final value

The set module is configured to combine the final values of the brightness of all pixels into a new image;

The correction module is configured to correct the new image with a correction function and output a final image.
The image processing system according to claim 5, wherein the pre-processing module comprises: a histogram accumulation unit, a histogram equalization pre-processing unit, a histogram equalization unit, and an acquisition look-up table unit;

The histogram accumulation unit is configured to perform histogram accumulation on the partition image;

The histogram equalization pre-processing unit is configured to obtain a maximum value and a minimum value of the histogram equalization according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image;

The histogram equalization unit is configured to perform histogram equalization processing on the image accumulated by the histogram by using a contrast limiting algorithm;

The acquisition lookup table unit is configured to obtain a display lookup table corresponding to the partition image.
The image processing system according to claim 6, wherein the maximum value of the histogram equalization is 255-a*(255-SubMax), and the minimum value of the histogram equalization is b*(SubMin), wherein 0 ≤ a ≤ 1, 0 ≤ b ≤ 1.
The image processing system according to claim 5, wherein said calculation module comprises: a lookup unit, a bilinear interpolation unit, and an output unit;

The searching unit is configured to obtain, by using the display lookup table, a corresponding value of four partitions around each pixel in the target image;

The bilinear interpolation unit is configured to calculate the corresponding values of the four partitions by using bilinear interpolation;

The output unit obtains a final value of the brightness of the corresponding pixel.
The image processing system according to claim 5, wherein said correction function is a linear function for correcting a maximum brightness value and a minimum brightness value of said new image.
An image processing method comprising:

Step S10: input a target image, perform partition processing on the target image, and obtain a partition image;

Step S20, acquiring a maximum brightness value Max, a minimum brightness value Min of the target image, and a maximum brightness value SubMax and a minimum brightness value SubMin of the partition image;

Step S30, performing histogram accumulation on the partition image, performing histogram equalization processing on the image accumulated by the histogram according to the obtained maximum brightness value SubMax and minimum brightness value SubMin of the partition image, to obtain the partition. a display lookup table corresponding to the image;

Step S40: Obtain a corresponding value of a neighboring partition of each pixel in the target image by using the display lookup table, calculate a corresponding value of the adjacent partition, and obtain a final value of a brightness of the corresponding pixel. ;

Step S50, combining the final values of the brightness of all the pixels into a new image;

Step S60, correcting the new image by using a correction function, and outputting a final image.
The image processing method according to claim 10, wherein said step S30 comprises:

Step S31, performing histogram accumulation on the partition image;

Step S32: Obtain a maximum value and a minimum value of the histogram equalization according to the obtained maximum brightness value SubMax and the minimum brightness value SubMin of the partition image;

Step S33, performing histogram equalization processing on the image accumulated by the histogram by using a contrast limiting algorithm;

Step S34: Obtain a display lookup table corresponding to the partition image.
The image processing method according to claim 11, wherein a maximum value of said histogram equalization is 255-a*(255-SubMax), and a minimum value of said histogram equalization is b*(SubMin), wherein 0 ≤ a ≤ 1, 0 ≤ b ≤ 1.
The image processing method according to claim 10, wherein said step S40 comprises:

Step S41: Obtain a corresponding value of four partitions around each pixel in the target image by using the display lookup table;

Step S42: Calculate the corresponding values of the four partitions by using bilinear interpolation;

Step S43, obtaining a final value of the brightness of the corresponding pixel.