CN113838012A - Mura detection method and device, computer readable storage medium and terminal - Google Patents
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Abstract
A Mura detection method and device, a computer readable storage medium and a terminal are provided, and the Mura detection method comprises the following steps: acquiring an image of a display screen to be detected; converting the image into a frequency domain image; filtering the frequency domain image according to the frequency range of the frequency domain image to remove moire in the image; mura was detected in the image after moire removal. The technical scheme of the invention can realize the accuracy of Mura detection.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a Mura detection method and apparatus, a computer-readable storage medium, and a terminal.
Background
With the development of display technology, the application of display panels is becoming more and more extensive, and the requirements for display panels are becoming higher and higher accordingly.
Mura refers to various inconsistent shapes or defects caused by uneven brightness or color on a display screen. Although the function of the display screen is not affected, one display screen with Mura cannot be accepted by consumers and can only be sold at low price or even scrapped as a secondary product. Each display screen manufacturer does not pay much attention to the elimination of Mura generation, and rejects with Mura before the product is delivered from the factory.
Most manufacturers use workers to visually inspect Mura at low screen brightness in dark rooms. Although manual detection is most intuitive, due to the reasons of short detection time, wide defect range, limited attention scope, fatigue and the like, the evaluation mode of manual detection is not objective, and the interpretation results of different detection personnel are different. If one wants to measure the screen Mura accurately, a professional imaging colorimeter is necessary. Although many studies on the evaluation of Mura defects have been conducted now, no widely recognized standard has been established. If the digital camera and the image recognition software are used to replace the human eye, the measurement method is more objective and consistent in standard.
However, the above-mentioned method of performing Mura detection by the digital camera in combination with the image recognition software is affected by moire, resulting in misreading of the interpretation.
Disclosure of Invention
The technical problem solved by the invention is how to realize the accuracy of Mura detection.
In order to solve the above technical problem, an embodiment of the present invention provides a Mura detection method, where the Mura detection method includes: acquiring an image of a display screen to be detected; converting the image into a frequency domain image; filtering the frequency domain image according to the frequency range of the frequency domain image to remove moire in the image; mura was detected in the image after moire removal.
Optionally, the filtering the frequency domain image according to the frequency range of each region in the frequency domain image includes: performing Gaussian filtering on the image, and performing DFT (discrete Fourier transform) transformation on the image after the Gaussian filtering to obtain a frequency domain image; and performing high-frequency filtering on the frequency domain image, performing inverse DFT (discrete Fourier transform) on the high-frequency filtered image to obtain a space domain image, and taking the obtained space domain image as the image with the Moire removed.
Optionally, the detecting Mura in the image after removing moire includes: extracting a difference image in the image with the moire removed, wherein the difference image is a background removed image of the image with the moire removed; carrying out threshold segmentation on the difference image to obtain a Mura candidate region; and determining whether the Mura candidate region comprises Mura according to the brightness of the pixels in the Mura candidate region.
Optionally, the determining whether Mura is included in the Mura candidate regions according to the brightness of the pixels in the Mura candidate regions includes: calculating a Semu value of each Mura candidate region; and determining whether Mura exists in the Mura candidate area according to the relation between the Semu value of the Mura candidate area and a preset threshold.
Optionally, the extracting a difference image in the image with the moire removed includes: extracting a background image in the image without the moire fringes, wherein the background image does not comprise noise and Mura; and calculating to obtain the difference image according to the image without the moire fringes and the background image.
Optionally, the extracting the background image in the image with the moire fringes removed includes: performing DCT (discrete cosine transformation) on the image without the moire fringes to obtain a DCT image; carrying out high-frequency filtering on the DCT image, and carrying out IDCT transformation on the image subjected to high-frequency filtering to obtain a DCT inverse transformation image; and obtaining the background image based on the image difference value of the spatial domain image and the DCT inverse transformation image.
Optionally, calculating the Semu value of the Mura candidate region by using the following formula:
wherein, cjndTo a predetermined contrast value, cxIs the average contrast, I, of the Mura candidate regionBIs the block average brightness of the background image, IMIs the block average luminance of the Mura candidate region.
The embodiment of the invention also discloses a Mura detection device, which comprises: the acquisition module is used for acquiring an image of the display screen to be detected; the frequency domain conversion module is used for converting the image into a frequency domain image; the filtering module is used for filtering the frequency domain image according to the frequency range of the frequency domain image so as to remove moire in the image; and the detection module is used for detecting Mura in the image after the Moire fringes are removed.
The embodiment of the invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program executes the steps of the Mura detection method when being executed by a processor.
The embodiment of the invention also discloses a terminal which comprises a memory and a processor, wherein the memory is stored with a computer program which can be operated on the processor, and the processor executes the steps of the Mura detection method when operating the computer program.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
in the technical scheme of the invention, the image of the display screen to be detected can be converted into a frequency domain image, and the frequency domain image is filtered in the frequency domain, so that the aim of removing moire is fulfilled. Moir e can be removed by means of filtering in the frequency domain because moir e usually appears as high frequency fringes in the image. By detecting Mura in the image with the moire fringes removed, the interference of the moire fringes on detection can be avoided, and the accuracy of Mura detection is improved.
Further, extracting a difference image in the image with the moire removed, wherein the difference image is a background removed image of the image with the moire removed; carrying out threshold segmentation on the difference image to obtain a Mura candidate region; and determining whether the Mura candidate region comprises Mura according to the brightness of the pixels in the Mura candidate region. According to the embodiment of the invention, the Mura detection is carried out on the image with the background removed, so that the Mura can be more obviously highlighted, and the detection accuracy is further improved.
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FIG. 1 is a flow chart of a Mura detection method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of one embodiment of step 104 of FIG. 1;
FIG. 3 is a diagram illustrating an exemplary application scenario of the present invention;
FIG. 4 is a detailed flowchart of a Mura detection method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a Mura detection apparatus according to an embodiment of the present invention.
Detailed Description
As described in the background, the Mura detection performed by the digital camera in combination with the image recognition software is affected by moire, resulting in inaccurate interpretation.
Moire is a high frequency interference fringe generated by photosensitive elements on digital cameras and the like, which causes colored high frequency irregular fringes to appear in an image. Moire patterns are irregular with no fixed shape or regularity. When the spatial frequency of the photosensitive element and the fringes in the image are close, an interference pattern having a wavy shape, so-called moire, may be formed.
When taking an image, the moire is usually reduced or eliminated in several ways: 1. changing the angle of the lens; 2. changing a camera position; 3. changing the focus; 4. changing the focal length of the lens; 5. a front mirror filter is used. However, these methods are used in Mura recognition equipment, which may cause hardware adjustment limitation or sacrifice the image definition.
Therefore, through research, the inventor of the application finds that processing a clear image with moire patterns in a software mode can be a simple, convenient and quick method when the actual display screen is delivered from a factory to a detection screen.
In the technical scheme of the invention, the image of the display screen to be detected can be converted into a frequency domain image, and the frequency domain image is filtered in the frequency domain, so that the aim of removing moire is fulfilled. The reason why moire can be removed by filtering in the frequency domain is that: moire fringes typically appear as high frequency fringes in the image. By detecting Mura in the image after removing the moire fringes, the interference of the moire fringes on detection can be avoided, and the accuracy of Mura detection is improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Fig. 1 is a flowchart of a Mura detection method according to an embodiment of the present invention.
The Mura detection method of the embodiment of the invention can be used for the terminal equipment side, namely, the terminal equipment can execute each step of the method. The terminal device may include, but is not limited to, a mobile phone, a computer, a tablet computer, and the like.
Specifically, the Mura detection method may include the steps of:
step 101: acquiring an image of a display screen to be detected;
step 102: converting the image into a frequency domain image;
step 103: filtering the frequency domain image according to the frequency range of the frequency domain image to remove moire in the image;
step 104: mura was detected in the image after moire removal.
It should be noted that the sequence numbers of the steps in this embodiment do not represent a limitation on the execution sequence of the steps.
It is understood that, in a specific implementation, the Mura detection method may be implemented by a software program running in a processor integrated within a chip or a chip module.
In the specific implementation of step 101, acquiring the image of the display screen to be detected may refer to acquiring an image obtained by shooting the display screen to be detected by a shooting device, where the shooting device may be an image acquisition device of a device such as a camera and a mobile phone. The image can be used to express the display effect of the display screen, and therefore can also be called a screen shot image. The image comprises a display screen and Moire patterns generated in the shooting process. Moire is produced by the photosensitive element of shooting equipment, and those skilled in the art can understand that the existence of Moire can interfere the detection of Mura, so the application is different from the processing of other technical schemes, and in order to improve the detection effect, the Moire in the image is removed first and then the Mura detection is carried out.
Specifically, the image of the display screen to be detected obtained directly by the shooting mode is an airspace image, and may be an image in a format of bmp, jpg, png, or the like, for example.
Since the moire is a high frequency fringe, the moire can be removed by filtering out the high frequency component in the frequency domain. In the implementation of step S102 and step S103, the spatial domain image may be converted into a frequency domain image, and filtering may be performed in the frequency domain image.
In a specific embodiment, the image is gaussian filtered, and Discrete Fourier Transform (DFT) is performed on the gaussian filtered image to obtain a frequency domain image.
In this embodiment, by performing gaussian filtering on the image, noise in the image can be removed. And the moire fringes are removed from the image after the noise points are removed, so that the moire fringes can be more comprehensively removed.
In a specific embodiment, the frequency domain image is subjected to high-frequency filtering, the high-frequency filtered image is subjected to inverse DFT conversion to obtain a spatial domain image, and the obtained spatial domain image is used as an image with moire removed.
In this embodiment, the high-frequency filtering of the frequency domain image may refer to removing a high-frequency component from the frequency domain image by using a low-pass filter to achieve the purpose of removing moire.
The DFT transform and IDFT (inverse DFT) transform involved in this process are shown as follows:
where M and N are the length and width of the image size, respectively, F (x, y) is the pixel value of each pixel (x, y) in the spatial domain image, and F (u, v) is the pixel value of each pixel (u, v) in the frequency domain image.
In a specific implementation, the low-pass filtering of the frequency domain image in the frequency domain refers to setting a specific pixel in the frequency domain image to zero. Specifically, after the original image is subjected to DFT to obtain a frequency domain image, a low frequency portion in the frequency domain image is located at a center point of the image. In this case, the center point of the frequency domain image is used as the origin, and the pixel values outside the circle having a radius of a predetermined length (for example, the length of the image width 1/4) are set to zero, and the pixel values inside the circle are kept unchanged, so that the low-pass filtered image, that is, the image from which the moire is removed, can be obtained by the inverse DFT.
Further, in the embodiment of step 104, Mura is detected in the image after the moire removal. By detecting Mura in the image with the moire fringes removed, the interference of the moire fringes on detection can be avoided, and the accuracy of Mura detection is improved.
In one non-limiting embodiment, referring to fig. 2, step 104 shown in fig. 1 may include the following steps:
step 201: extracting a difference image in the image with the moire removed, wherein the difference image is a background removed image of the image with the moire removed;
step 202: carrying out threshold segmentation on the difference image to obtain a Mura candidate region;
step 203: and determining whether the Mura candidate region comprises Mura according to the brightness of the pixels in the Mura candidate region.
The background referred to in this embodiment refers to other parts of the moire afterimage screen image relative to the Mura part, and the purpose of removing the background is to highlight the Mura part more obviously to help better detect the Mura.
In a specific embodiment of step 201, extracting a background image in the image after removing moire, where the background image does not include noise and Mura; and calculating to obtain the difference image according to the image without the moire fringes and the background image.
In this embodiment, the difference image includes only noise and Mura. Because the background image is easy to extract from the image, the background image can be extracted first, and then the image with the moire pattern and the background image are calculated to obtain a difference image only containing noise and Mura.
Further, the background image is acquired by: performing DCT (discrete cosine transformation) on the IDFT image without the Moire fringes to obtain a DCT image; carrying out high-frequency filtering on the DCT image, and carrying out IDCT transformation on the image subjected to high-frequency filtering to obtain a DCT inverse transformation image; and obtaining the background image based on the image difference value of the spatial domain image and the DCT inverse transformation image.
In one embodiment, Discrete Cosine Transform (DCT) can effectively obtain the energy distribution of an image, and the background is usually a region with higher energy in the image. Therefore, the blocks with higher energy in the DCT image are reserved by selecting an effective mode, and the background image can be obtained by IDCT transformation. In general, after the image is DCT transformed, the low frequency information is concentrated in the upper left corner of the matrix and the high frequency information is concentrated in the lower right corner. Specifically, as can be seen from the transform formula of the DCT, the low-frequency information represents a region in the spatial domain image where the pixel value change is small, and corresponds to a region in the frequency domain where the subscript is small, and since the starting point of the pixel coordinate in the frequency domain image is the top-left end point, the low-frequency information corresponds to the top-left part of the frequency domain. For example, a region where both the pixel row index and the column index are less than 5; in other words, the low frequency information corresponds to regions where both the pixel row and column indices are less than 5.
In another alternative embodiment, the block with higher energy is removed from the DCT image by selecting an effective manner, for example, filtering out the high frequency part in the DCT image (which may also be referred to as performing high frequency filtering on the DCT image), and then performing IDCT on the filtered image, so as to obtain an image with a removed background, that is, a difference image.
The DCT transform and IDCT transform involved in this process are shown by the following equations:
where D (u, v) is a pixel value of each pixel (u, v) in the DCT transform domain image, f (x, y) is a pixel value of each pixel (x, y) in the spatial domain image, and M and N are the length and width of the image size, respectively.
In the implementation, the block with higher energy in the DCT domain is removed by setting the pixels of other blocks except the specific area at the top left corner in the DCT transform domain image to zero. The size of a specific region in the upper left corner of the DCT-domain image may be preset or may be adaptively set according to the DCT image.
In a specific implementation of step 202, Mura and noise may be extracted from the difference image by thresholding the difference image. Specifically, the threshold segmentation method is to divide a pixel set by gray scale, and each obtained subset forms a region corresponding to a real scene, and each region has a consistent property inside, while adjacent regions do not have the consistent property. Such a division can be achieved by choosing one or more threshold values from the grey scale.
Specifically referring to fig. 3, in the difference image, the pixel value of the pixel with the pixel value lower than the preset threshold is set to 0, and the pixel value of the pixel with the pixel value higher than the preset threshold is set to 255, so as to implement the binarization of the image. The white region shown in fig. 3 is a region including Mura and noise, i.e., a Mura candidate region.
In particular, the number of Mura candidate regions may be one or more. For each Mura candidate region, it needs to be determined whether Mura is included separately.
In a specific embodiment, the thresholding may be performed using a Maximum Stable Extreme Regions (MSER) algorithm. The idea of the method is to binarize the image in an iterative manner, with a threshold value for binarization being 0 to 255. In the process of iterative binary segmentation, the image can go from full black to full white, and the area of some connected regions changes very little along with the rise of the threshold value, and such regions are maximum stable extremum regions. The MSER finally represents the maximum extremum stable region, i.e. the Mura candidate region in the present scheme, by using the white part (i.e. the highlight part) in the binary segmentation.
It should be noted that, other arbitrary implementable threshold algorithm may also be used to implement threshold segmentation, and the embodiment of the present invention is not limited to this.
In a specific implementation of step 203, for each Mura candidate region, calculating a Semu value of the Mura candidate region; and determining whether Mura exists in the Mura candidate area according to the relation between the Semu value of the Mura candidate area and a preset threshold. In this embodiment, the Semu value may characterize Mura. Mura can only be determined if the value of Semu reaches a preset threshold. Noise and Mura can be distinguished by calculating the Semu value in the Mura candidate region, so that the Mura in the display screen image to be detected is determined.
In one embodiment, the Semu value of the Mura candidate region is calculated using the following formula:
wherein, cjndTo a predetermined contrast value, cxIs the average contrast, I, of the Mura candidate regionBFor the block average luminance of the background image, known from the historical background removal part, IMBlock average luminance, S, for the Mura candidate region0.33S in (d) is the area of each Mura candidate region, i.e., the number of pixels contained in the region. Specifically, contrast refers to the difference in brightness between different regions. In the formula cxIs the average contrast of the Mura candidate region, specifically by the flatness of the Mura candidate regionAverage brightness IMAnd average brightness I of background imageBTo obtain cjndIs a preset contrast value, specifically obtained by the area S of the Mura candidate region.
It should be noted that the brightness and the contrast mentioned in the embodiment of the present invention may be calculated according to an existing calculation method, and in addition, taking the average brightness of the block of the Mura candidate region as an example, the average brightness may be obtained by dividing the sum of the regional brightness by the number of pixels.
For each Mura candidate region, the position information of each pixel is known, so that the average brightness I of the background image and the difference image for the block region can be obtained according to the position informationBAnd IM。
In a specific application scenario of the present invention, referring to fig. 4, the Mura detection method may include steps 401 to 409.
In step 401, image denoising is performed on the screen shot image. Specifically, the noise in the captured image can be removed by means of gaussian filtering.
In step 402, the denoised image is DFT transformed to obtain a frequency domain image.
In step 403, the frequency domain image is filtered to remove moire in the image.
In step 404, inverse DFT is performed on the frequency domain image without moire to obtain a spatial domain image without moire.
In step 405, the moir e removed image is DCT transformed to obtain a DCT image.
In step 406, the DCT image is high frequency filtered to obtain a background image in the DCT domain.
In step 407, inverse DCT transform is performed on the background image in the DCT domain to obtain a spatial domain background image.
At this point, the image without moire output in step 404 and the background image output in step 407 pass through a subtractor to obtain a difference image.
In step 408, the difference image is thresholded and Mura decided to determine whether Mura exists in the image.
In step 409, a detection result is output, and the detection result may include whether Mura exists in the screen shot image and, in the case of Mura, the position of Mura in the screen shot image.
Therefore, the position of the Mura in the display screen can be determined according to the position of the Mura in the screen shot image in the detection result.
Referring to fig. 5, an embodiment of the invention further discloses a Mura detection apparatus 50. The Mura detection apparatus 50 may include:
an obtaining module 501, configured to obtain an image of a display screen to be detected;
a frequency domain converting module 502, configured to convert the image into a frequency domain image;
a filtering module 503, configured to filter the frequency domain image according to the frequency range of the frequency domain image to remove moire in the image;
a detecting module 504, configured to detect Mura in the image after removing moire.
For more details of the operation principle and the operation mode of the Mura detection apparatus 50, reference may be made to the related descriptions in fig. 1 to 4, which are not described herein again.
In a specific implementation, the Mura detection apparatus may correspond to a Chip having a Mura detection function in a terminal device, such as a System-On-a-Chip (SOC), a baseband Chip, or the like; or the terminal equipment comprises a chip module with a Mura detection function; or to a chip module having a chip with a data processing function, or to a terminal device.
Each module/unit included in each apparatus and product described in the above embodiments may be a software module/unit, or may also be a hardware module/unit, or may also be a part of a software module/unit and a part of a hardware module/unit. For example, for each device or product applied to or integrated into a chip, each module/unit included in the device or product may be implemented by hardware such as a circuit, or at least a part of the module/unit may be implemented by a software program running on a processor integrated within the chip, and the rest (if any) part of the module/unit may be implemented by hardware such as a circuit; for each device or product applied to or integrated with the chip module, each module/unit included in the device or product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules/units may be implemented by using a software program running on a processor integrated within the chip module, and the rest (if any) of the modules/units may be implemented by using hardware such as a circuit; for each device and product applied to or integrated in the terminal, each module/unit included in the device and product may be implemented by using hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal, or at least part of the modules/units may be implemented by using a software program running on a processor integrated in the terminal, and the rest (if any) part of the modules/units may be implemented by using hardware such as a circuit.
The embodiment of the invention also discloses a storage medium, which is a computer-readable storage medium and stores a computer program thereon, and the computer program can execute the steps of the method shown in fig. 1 or fig. 2 when running.
The embodiment of the invention also discloses a terminal which can comprise a memory and a processor, wherein the memory is stored with a computer program which can run on the processor. The processor, when running the computer program, may perform the steps of the method shown in fig. 1 or fig. 2. The user equipment includes but is not limited to a mobile phone, a computer, a tablet computer and other terminal equipment.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document indicates that the former and latter related objects are in an "or" relationship.
The "plurality" appearing in the embodiments of the present application means two or more.
The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application.
The term "connect" in the embodiments of the present application refers to various connection manners, such as direct connection or indirect connection, to implement communication between devices, which is not limited in this embodiment of the present application.
It should be understood that, in the embodiment of the present application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will also be appreciated that the memory in the embodiments of the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example and not limitation, many forms of Random Access Memory (RAM) are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (enhanced SDRAM), SDRAM (SLDRAM), synchlink DRAM (SLDRAM), and direct bus RAM (DR RAM).
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed method, apparatus and system may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative; for example, the division of the unit is only a logic function division, and there may be another division manner in actual implementation; for example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be physically included alone, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute some steps of the methods according to the embodiments of the present invention.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A Mura detection method is characterized by comprising the following steps:
acquiring an image of a display screen to be detected;
converting the image into a frequency domain image;
filtering the frequency domain image according to the frequency range of the frequency domain image to remove moire in the image;
mura was detected in the image after moire removal.
2. The Mura detection method of claim 1, wherein the converting the image to a frequency domain image comprises: performing Gaussian filtering on the image, and performing DFT (discrete Fourier transform) transformation on the image after the Gaussian filtering to obtain a frequency domain image;
the filtering the frequency domain image according to the frequency range of each region in the frequency domain image comprises:
and performing high-frequency filtering on the frequency domain image, performing inverse DFT (discrete Fourier transform) on the high-frequency filtered image to obtain a space domain image, and taking the obtained space domain image as the image with the Moire removed.
3. The Mura detection method of claim 2, wherein the detecting Mura in the Moire-removed image comprises:
extracting a difference image in the image with the moire removed, wherein the difference image is a background removed image of the image with the moire removed;
carrying out threshold segmentation on the difference image to obtain a Mura candidate region;
and determining whether the Mura candidate region comprises Mura according to the brightness of the pixels in the Mura candidate region.
4. The Mura detection method of claim 3, wherein the determining whether Mura is included in the Mura candidate region according to the intensities of the pixels in the Mura candidate region comprises:
calculating a Semu value of each Mura candidate region;
and determining whether Mura exists in the Mura candidate area according to the relation between the Semu value of the Mura candidate area and a preset threshold.
5. The Mura detection method of claim 3, wherein the extracting the difference image in the Moire-removed image comprises:
extracting a background image in the image without the moire fringes, wherein the background image does not comprise noise and Mura;
and calculating to obtain the difference image according to the image without the moire fringes and the background image.
6. The Mura detection method of claim 5, wherein the extracting the background image in the Moire-removed image comprises:
performing DCT (discrete cosine transformation) on the image without the moire fringes to obtain a DCT image;
carrying out high-frequency filtering on the DCT image, and carrying out IDCT transformation on the image subjected to high-frequency filtering to obtain a DCT inverse transformation image;
and obtaining the background image based on the image difference value of the spatial domain image and the DCT inverse transformation image.
7. A Mura detection method according to claim 4, wherein the Semu value of the Mura candidate region is calculated using the following formula:
wherein, cjndTo a predetermined contrast value, cxIs the average contrast, I, of the Mura candidate regionBIs the block average brightness of the background image, IMIs the block average luminance of the Mura candidate region.
8. A Mura detection apparatus, comprising:
the acquisition module is used for acquiring an image of the display screen to be detected;
the frequency domain conversion module is used for converting the image into a frequency domain image;
the filtering module is used for filtering the frequency domain image according to the frequency range of the frequency domain image so as to remove moire in the image;
and the detection module is used for detecting Mura in the image after the Moire fringes are removed.
9. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the Mura detection method according to any of the claims 1 to 7.
10. A terminal comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, wherein the processor, when executing the computer program, performs the steps of the Mura detection method of any of claims 1-7.
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