Image processing method and apparatus therefor, display panel

Abstract

This invention provides an image processing method and apparatus, and a display panel. The method includes: acquiring initial image data of the display panel, the initial image data including background data, contour data, edge data, and noise data; processing the initial image data according to a first frequency to generate first image data, the first frequency being the upper limit of the frequencies of the background data and contour data in the frequency domain; processing the initial image data according to a second frequency to generate second image data, the second frequency being the lower limit of the frequencies of the edge data and noise data in the frequency domain; and generating corresponding target image data based on the first image data and the second image data, thereby balancing image uniformity and edge clarity.

Description

Technical Field

[0001] This invention relates to the field of display technology, and more particularly to the manufacture of display devices, specifically to image processing methods and equipment, and display panels. Background Technology

[0002] High-quality display can enhance the user experience, and image quality can be improved through software, namely image enhancement algorithms.

[0003] Among them, the background and outline of an image are low-frequency components, while the edges and noise are high-frequency components. When there are more low-frequency components in an image, the image will appear uniform and soft, but at the same time it will be more blurry. When there are more high-frequency components in an image, the edges of the image are clear, but relatively speaking, there are fewer low-frequency components, which cannot well represent the background and outline of the image.

[0004] Therefore, existing image enhancement algorithms cannot simultaneously achieve both image uniformity and edge sharpness, and urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide an image processing method, device, and display panel to improve the technical problem that existing image enhancement algorithms cannot simultaneously achieve image uniformity and edge clarity.

[0006] This invention provides an image processing method, comprising:

[0007] Acquire initial image data of the display panel, the initial image data including background data, contour data, edge data and noise data;

[0008] The initial image data is processed according to a first frequency to generate first image data, wherein the first frequency is the upper limit of the frequency of both the background data and the contour data in the frequency domain;

[0009] The initial image data is processed according to a second frequency to generate second image data, wherein the second frequency is the lower limit of the frequencies of the edge data and the noise data in the frequency domain;

[0010] Based on the first image data and the second image data, corresponding target image data is generated.

[0011] In one embodiment, the step of processing the initial image data according to the second frequency to generate second image data includes:

[0012] The initial image data is processed according to the first and second sub-frequency of the second frequency to generate the second image data.

[0013] In one embodiment, the display panel includes a plurality of pixel units, each pixel unit including a plurality of sub-pixels, and the step of acquiring initial image data of the display panel includes:

[0014] Obtain multiple sub-initial parameters corresponding to each pixel unit and multiple sub-pixels;

[0015] Sub-initial image data corresponding to the pixel unit is generated according to multiple sub-initial parameters, and the multiple sub-initial image data corresponding to the multiple pixel units constitute the initial image data;

[0016] The first image data includes multiple first sub-image data corresponding to multiple pixel units, the second image data includes multiple second sub-image data corresponding to multiple pixel units, and the target image data includes multiple sub-target image data corresponding to multiple pixel units.

[0017] In one embodiment, it further includes:

[0018] A proportional parameter corresponding to the pixel unit is generated based on multiple sub-initial parameters;

[0019] The step of generating corresponding target image data based on the first image data and the second image data includes:

[0020] Based on the multiple first sub-image data and the multiple second sub-image data, multiple sub-image data to be assigned corresponding to the multiple pixel units are generated;

[0021] The sub-image data to be allocated for the corresponding pixel unit is processed according to each of the ratio parameters to generate multiple sub-target parameters for the multiple sub-pixels, and the multiple sub-target parameters for each pixel unit constitute the corresponding sub-target image data.

[0022] In one embodiment, the step of generating multiple sub-image data to be assigned corresponding to multiple pixel units based on multiple first sub-image data and multiple second sub-image data includes:

[0023] The first image data and the second image data are linearly weighted to generate corresponding image data to be assigned. The sum of the weight coefficients of the first image data and the weight coefficients of the second image data is equal to 1. The image data to be assigned includes multiple sub-image data to be assigned.

[0024] In one embodiment, the first frequency is less than the first sub-frequency and the second sub-frequency, and the first sub-frequency is less than the second sub-frequency.

[0025] In one embodiment, the step of processing the initial image data according to a first frequency to generate first image data includes:

[0026] Perform a Fourier transform on the initial image data to generate the corresponding first image spectrum;

[0027] The first image spectrum is processed according to the first frequency to generate a second image spectrum, wherein the frequency of the second image spectrum is less than or equal to the first frequency;

[0028] Perform an inverse Fourier transform on the spectrum of the second image to generate the first image data.

[0029] In one embodiment, the step of processing the initial image data according to the first sub-frequency and the second sub-frequency in the second frequency to generate the second image data includes:

[0030] Perform a Fourier transform on the initial image data to generate the corresponding first image spectrum;

[0031] The first image spectrum is processed based on the first sub-frequency and the second sub-frequency to generate a third image spectrum, wherein the frequency of the third image spectrum is less than or equal to the first sub-frequency and greater than or equal to the second sub-frequency;

[0032] The third image spectrum is subjected to inverse Fourier transform to generate the second image data.

[0033] The present invention also provides a display panel including a controller for executing program instructions to implement the image processing method as described above.

[0034] The present invention also provides an apparatus for performing an image processing method as described in any of the above descriptions.

[0035] This invention provides an image processing method and apparatus, and a display panel. The method generates first image data by processing initial image data according to a first frequency, and generates second image data by processing the initial image data according to a second frequency. Finally, it generates corresponding target image data based on the first image data and the second image data. The initial image data includes background data, contour data, edge data, and noise data. The first frequency is the upper limit of the frequencies of the background data and the contour data in the frequency domain, and the second frequency is the lower limit of the frequencies of the edge data and the noise data in the frequency domain. This allows the target image data generated by this invention to fuse low-frequency information and edge information of the image for image enhancement, resulting in a uniform image while maintaining clear edges. Attached Figure Description

[0036] The present invention will be further described below with reference to the accompanying drawings. It should be noted that the accompanying drawings described below are merely for illustrating some embodiments of the present invention. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0037] Figures 1 to 6 These are six flowcharts illustrating the image processing method provided in the embodiments of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] In the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first" or "second" may explicitly or implicitly include one or more of the stated features. Furthermore, it should be noted that the accompanying drawings only provide structures closely related to the invention, omitting some details less relevant to the invention. The purpose is to simplify the drawings and make the inventive points clear at a glance, not to indicate that the actual device is identical to the accompanying drawings. Figure 1 It is identical to the actual device and is not set as a limitation.

[0040] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase at various points in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] The present invention provides an image processing method, which includes, but is not limited to, the following embodiments and combinations thereof.

[0042] In one embodiment, such as Figure 1 As shown, the image processing method may include, but is not limited to, the following steps and combinations thereof.

[0043] S1, acquire initial image data of the display panel, the initial image data including background data, contour data, edge data and noise data.

[0044] In this embodiment, the display panel may include, but is not limited to, at least one of a liquid crystal display panel, an organic self-emissive display panel, and an inorganic self-emissive display panel. It is understood that the display panel can display an image based on image data, and the initial image data here can be understood as image data that has not yet been processed. The display panel includes multiple pixel units, each pixel unit includes multiple sub-pixels, and the initial image data may include multiple data corresponding to the multiple sub-pixels in the multiple pixel units. This data may include, but is not limited to, grayscale information or other optical information.

[0045] Specifically, the initial image data contains signals in the time domain, and the corresponding signals in the frequency domain can include high-frequency components and low-frequency components. The background and contours of the image are low-frequency components, while the edges and noise are high-frequency components. When there are more low-frequency components in the image, the image will appear uniform and soft, but at the same time it will be more blurry. When there are more high-frequency components in the image, the edges of the image are clear, but relatively few low-frequency components, which cannot well represent the background and contours of the image.

[0046] Among them, such as Figure 2 As shown, step S1 may include, but is not limited to, the following steps and combinations thereof.

[0047] S11, obtain multiple sub-initial parameters corresponding to multiple sub-pixels for each pixel unit.

[0048] Specifically, each sub-pixel of each sub-pixel unit has a corresponding sub-initial parameter, which can be the optical parameter of the sub-pixel, such as, but not limited to, the grayscale value or stimulus value of the sub-pixel. Here, we will take the grayscale value as an example for explanation.

[0049] S12, generate sub-initial image data corresponding to the pixel unit according to the multiple sub-initial parameters, and the multiple sub-initial image data corresponding to the multiple pixel units constitute the initial image data.

[0050] As discussed above, each pixel unit can have multiple corresponding sub-initial parameters, and each sub-initial parameter can only represent the illumination of the corresponding sub-pixel. In particular, the multiple sub-initial parameters of the pixel unit can be processed to obtain a corresponding sub-initial image data. For example, the sub-initial image data can be understood as the average value of the multiple corresponding sub-initial parameters, that is, the sub-initial image data can reflect the average illumination of the pixel unit at the initial stage.

[0051] Therefore, the initial image data includes multiple sub-initial image numbers corresponding to multiple pixel units, and the number of sub-initial image numbers is calculated from multiple sub-initial parameters corresponding to multiple sub-pixels.

[0052] S2, process the initial image data according to the first frequency to generate first image data, wherein the first frequency is the upper limit of the frequency of both the background data and the contour data in the frequency domain.

[0053] As discussed above, in the signal in the frequency domain corresponding to the initial image data, the background and contour of the image are low-frequency components, while the edges and noise are high-frequency components. When there are more low-frequency components in the image, the image will appear uniform and soft, but at the same time it will be more blurry. When there are more high-frequency components in the image, the edges of the image are clear, but relatively speaking, there are fewer low-frequency components, which cannot well represent the background and contour of the image.

[0054] Understandably, the first frequency in step S2 is the upper limit of the frequency of the data corresponding to the background and contour in the frequency domain. That is, the first image data generated based on the first frequency can be used to represent the background and contour of the image.

[0055] Among them, such as Figure 3 As shown, step S2 may include, but is not limited to, the following steps and combinations thereof.

[0056] S21, Perform a Fourier transform on the initial image data to generate the corresponding first image spectrum.

[0057] Specifically, assuming the display panel has a resolution of N×M, where M and N are both positive integers, the display panel can include M rows and N columns of pixel units, which can be represented by an M×N matrix I. Each element in matrix I can be filled with the sub-initial image data of the corresponding pixel unit. For example, element I(x,y) can represent the sub-initial image data of the (x-1) row and (y-1) column pixel unit. Furthermore, performing a Fourier transform on matrix I using the following expression can convert it to the frequency domain, obtaining the two-dimensional first image spectrum:

[0058]

[0059] Here, x can take any positive integer between 0 and (M-1), y can take any positive integer between 0 and (N-1), and each point in the spectrum can represent a plane wave. That is, FI(u,v) can represent the amplitude of a plane wave with frequency u in the x direction and frequency v in the y direction. The above frequencies can be the rate of brightness change of multiple pixel units in the corresponding directions.

[0060] Since the initial image data includes background data, contour data, edge data and noise data, and the first frequency can be understood as the upper limit of the frequency of the data corresponding to the background and contour in the frequency domain, it is necessary to convert the initial image data into the first image spectrum in the frequency domain through step S21.

[0061] S22, process the first image spectrum according to the first frequency to generate a second image spectrum, wherein the frequency of the second image spectrum is less than or equal to the first frequency.

[0062] This can be understood as performing low-pass filtering on the two-dimensional first image spectrum to obtain a second image spectrum with a lower frequency. The processing is based on the first frequency, meaning that the frequency of the second image spectrum is less than or equal to the first frequency.

[0063] Specifically, the expression for the low-pass filter function H(u,v) can be:

[0064]

[0065] Where D0 is the cutoff frequency of the low-pass filter, and D(u,v) can be understood as the Euclidean distance from the point in the u-th row and v-th column of the power spectrum to the center point, which can be expressed as:

[0066]

[0067] Therefore, the low-pass filter function H(u,v) can be understood as taking a value of 1 when the Euclidean distance is less than or equal to D0, and taking a value of 0 when the Euclidean distance is greater than D0. The final H(u,v) is also an M×N matrix, and each element in the matrix takes a value of 1 or 0 according to the above requirements.

[0068] Furthermore, by applying a low-pass filter function H(u,v) to the two-dimensional first image spectrum, a low-frequency second image spectrum can be obtained. The expression for the second image spectrum can be:

[0069]

[0070] Here, represents the Hadamard product, which is a type of matrix operation. If A = (aij) and B = (bij) are two matrices of the same order, and cij = aij × bij, then matrix C = (cij) is called the Hadamard product of A and B, or the basic product, where i and j are both positive integers.

[0071] Therefore, the second image spectrum obtained through the above calculations can also be an M×N matrix, FI L The value of each element in the matrix (u,v) is determined by the position of the element with a value of 1 in the matrix H(u,v). If the element at a certain position in the matrix H(u,v) has a value of 1, then FI L The value of the element at a certain position in the matrix (u,v) can be any value of the element at that position in the matrix FI(u,v). If the value of an element at a certain position in the matrix H(u,v) is 0, then FI... LIn the matrix (u,v), the element at that position takes the value of 0. This process can be considered equivalent to filtering out amplitudes smaller than the first frequency from the matrix FI(u,v), arranging them in their original positions to form FI. L A matrix of (u,v).

[0072] S23, perform an inverse Fourier transform on the spectrum of the second image to generate the first image data.

[0073] Specifically, the following expression can be used to analyze the low-pass spectrum FI of the second image. L Perform an inverse Fourier transform on (u,v) to convert it into the first image data, I. L The expression can be:

[0074]

[0075] Where u can take any positive integer between 0 and (M-1), v can take any positive integer between 0 and (N-1), and the first image data I(x,y) is a signal in the time domain, that is, I(x,y) can be represented as the gray level value of the pixel unit in the (x-1)th row and (y-1)th column after the above processing. In other words, the first image data includes multiple first sub-image data corresponding to multiple pixel units, and each first sub-image data can be the corresponding gray level value.

[0076] S3, process the initial image data according to the second frequency to generate second image data, wherein the second frequency is the lower limit of the frequency of both the edge data and the noise data in the frequency domain.

[0077] Specifically, in the initial image data of the display panel, the background and outline are low-frequency components, while the edges and noise are high-frequency components. When there are more low-frequency components in the image, the image will appear uniform and soft, but at the same time it will be more blurry. When there are more high-frequency components in the image, the edges of the image are clear, but relatively speaking, there are fewer low-frequency components, which cannot better represent the background and outline of the image.

[0078] The first frequency can be understood as the upper limit of the frequency of the data corresponding to the background and the outline. Therefore, the frequency of the first image data obtained through step S2 is less than or equal to the first frequency, that is, the first image data can include the background and the outline of the image.

[0079] Furthermore, the first frequency is less than both the first sub-frequency and the second sub-frequency, and the first sub-frequency is less than the second sub-frequency. The second frequency in step S3 can be understood as the lower limit of the frequency of the data corresponding to edges and noise. Therefore, the frequency of the second image data obtained through step S3 is greater than or equal to the second frequency, meaning that the second image data can include the edges and noise of the image.

[0080] Furthermore, such as Figure 4 As shown, step S3 may include, but is not limited to, the following steps:

[0081] S31, process the initial image data according to the first sub-frequency and the second sub-frequency in the second frequency to generate the second image data.

[0082] It is important to note that both edges and noise are high-frequency components. When there are more high-frequency components in an image, the edges of the image are clearer, but at the same time, the noise will also be more obvious.

[0083] The first sub-frequency can be understood as the lower limit of the frequency of the data corresponding to the edge, and the second sub-frequency can be understood as the lower limit of the frequency of the data corresponding to the noise. Therefore, the second image data obtained based on the first and second sub-frequencys can include only the edges and not the noise.

[0084] In one embodiment, step S31 may include, but is not limited to, the following steps and combinations thereof.

[0085] S32, Perform a Fourier transform on the initial image data to generate the corresponding first image spectrum;

[0086] Step S32 can be the same as step S21 described above.

[0087] Similarly, since the initial image data includes background data, contour data, edge data and noise data, and the second frequency can be understood as the lower limit of the frequency of the data corresponding to the edge and noise in the frequency domain, it is also necessary to convert the initial image data into the first image spectrum in the frequency domain through step S21.

[0088] S33, process the first image spectrum according to the first sub-frequency and the second sub-frequency to generate a third image spectrum, wherein the frequency of the third image spectrum is less than or equal to the first sub-frequency and greater than or equal to the second sub-frequency.

[0089] This can be understood as performing bandpass filtering on the two-dimensional first image spectrum to obtain a third image spectrum with higher frequencies. The processing is based on the first sub-frequency and the second sub-frequency, meaning that the frequency of the third image spectrum is between the first sub-frequency and the second sub-frequency.

[0090] Specifically, a third image spectrum can be generated by processing the first image spectrum based on the first and second sub-frequencys using Gaussian difference. Gaussian difference is equivalent to the difference between the Gaussian filtering results of two different parameters (e.g., the first and second sub-frequencys in this case). For example, if the first sub-frequency is lower than the second sub-frequency, Gaussian filtering of the first image spectrum based on the second sub-frequency can yield an image spectrum including noise and edges, while Gaussian filtering of the first image spectrum based on the first sub-frequency can yield an image spectrum containing only noise. Both can be used together to extract edge information from the image. Therefore, based on these two image spectra, a third image spectrum with frequencies between the first and second sub-frequency can be obtained.

[0091] The Gaussian difference function can be expressed as:

[0092]

[0093] Here, σ1 and σ2 are the two standard deviations of the corresponding two Gaussian distributions. σ1 can be determined by the first sub-frequency, and σ2 can be determined by the second sub-frequency. The difference of Gaussians function can also be used.

[0094] Therefore, the Gaussian difference function G(u,v) can be understood as setting the value of 1 for elements whose frequency is between the first and second sub-frequencys, and setting the value of 0 for elements outside the first and second sub-frequencys. The resulting G(u,v) is also an M×N matrix, where each element takes the value of 1 or 0 according to the above requirements.

[0095] Furthermore, by applying the Gaussian difference function G(u,v) to the two-dimensional first image spectrum using bandpass filtering, a third image spectrum (containing only edge information) with higher frequencies (between the first and second sub-frequencies) can be obtained. The expression for the third image spectrum can be:

[0096] FE(u,v)=G(u,v)○FI(u,v);

[0097] S34, perform an inverse Fourier transform on the spectrum of the third image to generate the second image data.

[0098] Specifically, please refer to the relevant discussion in step S23. The expression for the second image data E can be:

[0099]

[0100] Similarly, the second image data includes multiple second sub-image data corresponding to multiple pixel units, and each second sub-image data can be a corresponding grayscale value.

[0101] S4, Generate corresponding target image data based on the first image data and the second image data.

[0102] Similarly, the target image data includes multiple sub-target image data corresponding to multiple pixel units. That is, processing each sub-initial image data according to a first frequency yields the first sub-image data of the corresponding pixel unit, processing each sub-initial image data according to a second frequency yields the second sub-image data of the corresponding pixel unit, and then using each first sub-image data and the corresponding second sub-image data, the sub-target image data of the corresponding pixel unit can be obtained.

[0103] In one embodiment, such as Figure 5 As shown, the image processing method may include, but is not limited to, the following steps and combinations thereof.

[0104] S5, generate a scaling parameter corresponding to the pixel unit based on the multiple sub-initial parameters.

[0105] Step S5 can be placed before step S2. That is, before processing the initial image data, a proportional parameter corresponding to the pixel unit can be generated based on the ratio of multiple sub-initial parameters corresponding to multiple sub-pixels in each pixel unit. For example, if the three sub-initial parameters of multiple sub-pixels (e.g., the number is 3) in a pixel unit are g1, g2, and g3 (all three are positive integers), then the proportional parameter of the pixel unit can be h1:h2:h3 (all three are positive integers). Then it should satisfy g1 / h1=g2 / h2=g3 / h3=k, where k is the greatest common factor of g1, g2, and g3.

[0106] Step S4 may include, but is not limited to, the following steps and combinations thereof.

[0107] S41, based on the plurality of first sub-image data and the plurality of second sub-image data, generate a plurality of sub-image data to be assigned corresponding to the plurality of pixel units.

[0108] As can be seen from the above analysis, both the first sub-image data and the second sub-image data can characterize the optical parameters (e.g., grayscale values) of the corresponding pixel units determined by the first frequency and the second frequency, respectively. Therefore, what is determined by each first sub-image data and the corresponding second sub-image data here is also the optical parameter (e.g., grayscale value) of the corresponding pixel unit. This is called the sub-image data to be assigned. That is, the sub-image data to be assigned can be understood as representing the theoretical average light emission of the pixel unit after steps S2 to S3, but it cannot accurately represent the theoretical light emission of each sub-pixel.

[0109] In one embodiment, step S41 may include, but is not limited to, the following steps:

[0110] S411, linearly weight the first image data and the second image data to generate corresponding image data to be allocated, wherein the sum of the weight coefficients of the first image data and the weight coefficients of the second image data is equal to 1, and the image data to be allocated includes multiple sub-image data to be allocated.

[0111] Referring to the discussion of step S12 above, the sub-initial image data can reflect the average light emission of the pixel unit at the initial stage (multiple sub-initial image data constitute the initial image data of the display panel); referring to the discussion of step S2 above, the first sub-image data can reflect the average light emission of the pixel unit after step S2 (multiple first sub-image data constitute the first image data of the display panel); referring to the discussion of step S3 above, the second sub-image data can reflect the average light emission of the pixel unit after step S3 (multiple second sub-image data constitute the second image data of the display panel).

[0112] Furthermore, this embodiment further specifies that "the first image data and the second image data are linearly weighted to generate corresponding image data to be allocated." That is, the same linear weighting rule is applied to each first sub-image data and the corresponding second sub-image data to obtain the sub-image data to be allocated for the corresponding pixel unit. Here, the weighting coefficients of the first image data and the second image data are not limited, but their sum can be 1, to ensure that the initial sub-image data is obtained by using the first sub-image data, which is partially weighted with the first frequency information, and the second sub-image data, which is also partially weighted with the second frequency information, to obtain sub-image data to be allocated based on both the first and second sub-image data.

[0113] Specifically, for the low-frequency first image data I L The target image data I' (corresponding to the enhanced image) is obtained by linearly weighting (x,y) and the second image data E(x,y) which only has edge information, and then expressing it as follows:

[0114] I′(x,y)=αI L (x,y)+βE(x,y);

[0115] α+β=1;

[0116] Wherein, α and β are the weighting coefficients of the first image data and the second image data, respectively. Referring to the discussion of step S4 above, the sub-image data to be allocated can be understood as representing the theoretical average light emission of the pixel unit after steps S2 to S3 (multiple sub-image data to be allocated constitute the image data to be allocated on the display panel).

[0117] In other words, in this embodiment, image enhancement is performed by fusing low-frequency information and edge information of the image, so that the image is uniform while maintaining clear edges.

[0118] S42, the sub-image data to be allocated of the corresponding pixel unit is processed according to each of the ratio parameters to generate multiple sub-target parameters of the multiple sub-pixels, and the multiple sub-target parameters of each pixel unit constitute the corresponding sub-target image data.

[0119] As analyzed above, the proportional parameters of a pixel unit can characterize the proportion of optical parameters of multiple sub-pixels. Therefore, based on the sub-image data to be allocated in a pixel unit, the sub-target parameters of each sub-pixel can be allocated according to this proportion. For example, if the sub-image data to be allocated in a pixel unit is P, and the proportional parameters of the pixel unit are h1:h2:h3, then the sub-target parameters of the three sub-pixels can be 3P*h1 / (h1+h2+h3), 3P*h1 / (h1+h2+h3), and 3P*h3 / (h1+h2+h3). Here, 3P can be understood as the total optical parameters of the pixel unit that theoretically emit light, while h1 / (h1+h2+h3), h1 / (h1+h2+h3), and h3 / (h1+h2+h3) can be the proportions of the theoretically emitted optical parameters of the three sub-pixels to the aforementioned "total optical parameters".

[0120] Image processing methods may also be referenced, but are not limited to, those mentioned above. Figure 6 The flowchart shown is executed as follows. Here, "Image Acquisition" corresponds to step S1 above, "Image Averaging" corresponds to steps S11 to S12 above, "Fourier Transform" corresponds to steps S21 and S32 above, "Low-Pass Filtering" corresponds to step S22 above, "Inverse Fourier Transform" after "Low-Pass Filtering" corresponds to step S23 above, "Inverse Fourier Transform" after "Gaussian Distribution" S34, "Linear Weighting" corresponds to step S411 above, "RGB Ratio" corresponds to step S5 above, and "Color Restoration" corresponds to steps S41 to S42 above.

[0121] The present invention also provides a display panel including a controller for executing program instructions to implement the image processing method as described above.

[0122] The present invention also provides an apparatus for performing an image processing method as described above.

[0123] This invention provides an image processing method and apparatus, and a display panel. The method generates first image data by processing initial image data according to a first frequency, and generates second image data by processing the initial image data according to a second frequency. Finally, it generates corresponding target image data based on the first image data and the second image data. The initial image data includes background data, contour data, edge data, and noise data. The first frequency is the upper limit of the frequencies of the background data and the contour data in the frequency domain, and the second frequency is the lower limit of the frequencies of the edge data and the noise data in the frequency domain. This allows the target image data generated by this invention to fuse low-frequency information and edge information of the image for image enhancement, resulting in a uniform image while maintaining clear edges.

[0124] The image processing method, device, and display panel provided in the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of the present invention. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An image processing method, characterized in that, include: Acquire initial image data of the display panel, the initial image data including background data, contour data, edge data and noise data, the display panel including multiple pixel units; The initial image data is processed according to a first frequency to generate first image data, wherein the first frequency is the upper limit of the frequency of both the background data and the contour data in the frequency domain; The initial image data is processed according to a second frequency to generate second image data, wherein the second frequency is the lower limit of the frequencies of the edge data and the noise data in the frequency domain; Based on the first image data and the second image data, generate corresponding target image data; The step of generating corresponding target image data based on the first image data and the second image data includes: The first image data and the second image data are linearly weighted to generate corresponding image data to be allocated. The sum of the weight coefficients of the first image data and the weight coefficients of the second image data is equal to 1. The image data to be allocated includes multiple sub-image data to be allocated corresponding to multiple pixel units. Based on each of the sub-image data to be allocated, a plurality of sub-target image data corresponding to the pixel unit are determined, wherein the target image data includes a plurality of sub-target image data corresponding to the plurality of pixel units.

2. The image processing method as described in claim 1, characterized in that, The step of processing the initial image data according to the second frequency to generate the second image data includes: The initial image data is processed according to the first and second sub-frequency of the second frequency to generate the second image data.

3. The image processing method as described in claim 1 or 2, characterized in that, Each pixel unit includes multiple sub-pixels, and the step of acquiring initial image data of the display panel includes: Obtain multiple sub-initial parameters corresponding to each pixel unit and multiple sub-pixels; Sub-initial image data corresponding to the pixel unit is generated according to multiple sub-initial parameters, and the multiple sub-initial image data corresponding to the multiple pixel units constitute the initial image data; The first image data includes a plurality of first sub-image data corresponding to a plurality of pixel units, and the second image data includes a plurality of second sub-image data corresponding to a plurality of pixel units.

4. The image processing method as described in claim 3, characterized in that, Also includes: A proportional parameter corresponding to the pixel unit is generated based on multiple sub-initial parameters; The step of linearly weighting the first image data and the second image data to generate corresponding image data to be allocated includes: Based on the plurality of first sub-image data and the plurality of second sub-image data, a plurality of sub-image data to be assigned corresponding to the plurality of pixel units are generated; The step of determining multiple sub-target image data corresponding to each sub-image data to be allocated includes: The sub-image data to be allocated for the corresponding pixel unit is processed according to each of the ratio parameters to generate a plurality of sub-target parameters corresponding to the plurality of sub-pixels, wherein the plurality of sub-target parameters of each pixel unit constitute the corresponding sub-target image data.

5. The image processing method as described in claim 2, characterized in that, The first frequency is less than the first sub-frequency and the second sub-frequency, and the first sub-frequency is less than the second sub-frequency.

6. The image processing method as described in claim 5, characterized in that, The step of processing the initial image data according to the first frequency to generate the first image data includes: Perform a Fourier transform on the initial image data to generate the corresponding first image spectrum; The first image spectrum is processed according to the first frequency to generate a second image spectrum, wherein the frequency of the second image spectrum is less than or equal to the first frequency; Perform an inverse Fourier transform on the spectrum of the second image to generate the first image data.

7. The image processing method as described in claim 5, characterized in that, The step of processing the initial image data according to the first sub-frequency and the second sub-frequency in the second frequency to generate the second image data includes: Perform a Fourier transform on the initial image data to generate the corresponding first image spectrum; The first image spectrum is processed based on the first sub-frequency and the second sub-frequency to generate a third image spectrum, wherein the frequency of the third image spectrum is less than or equal to the first sub-frequency and greater than or equal to the second sub-frequency; The third image spectrum is subjected to inverse Fourier transform to generate the second image data.

8. A display panel, characterized in that, Includes a controller for executing program instructions to implement the image processing method as described in any one of claims 1 to 7.

9. An apparatus for an image processing method, characterized in that, Used to perform the image processing method as described in any one of claims 1 to 7.

Images