Image processing device, method, display chip, computer device and storage medium

Abstract

The application discloses an image processing device, method, display chip, computer equipment and storage medium, and belongs to the technical field of image processing. The device comprises a low-pass filtering module configured to perform low-pass filtering on original brightness values of neighborhood pixels of a target pixel by using a first filtering window and a second filtering window; an edge detail determination module configured to perform weighted summation on a high-frequency edge value and a medium-frequency detail value of the target pixel; an effective edge determination module configured to determine an effective edge value of the target pixel; and an edge superposition module configured to superpose the effective edge value on the original brightness value of the target pixel to obtain a brightness sharpening result of the target pixel. The application performs low-pass filtering on the original brightness value of the target pixel by using two filtering windows of different sizes to obtain the high-frequency edge value and the medium-frequency detail value of the target pixel, and adjusts the weight of the high-frequency edge value and the medium-frequency detail value, so that the sharpening radius is adjustable at low cost.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image processing apparatus, method, display chip, computer device and storage medium. Background Technology

[0002] In the image acquisition and processing workflow, image quality is often limited by sensor resolution. Furthermore, in the Image Signal Processing (ISP) stage, smoothing operations such as noise reduction and interpolation inevitably cause image edge blurring, severely impacting visual perception. Therefore, image sharpening technology has become an indispensable part of the image processing workflow.

[0003] Most existing image sharpening techniques are based on the Unsharp Mask (USM) algorithm. This algorithm controls the sharpening effect through three key parameters: radius, sharpening intensity, and threshold. Among these, the radius, as one of the core parameters, directly determines the range of edge enhancement. However, at the hardware implementation level, in order to achieve flexible adjustment of the radius, it is usually necessary to construct multi-scale filter kernels, which significantly increases the chip area, power consumption, and manufacturing cost. Summary of the Invention

[0004] This application provides an image processing apparatus, method, display chip, computer device, and storage medium. It determines the high-frequency edge values ​​and mid-frequency detail values ​​of a target pixel by using two filtering windows of different sizes to perform low-pass filtering on the original brightness value of the target pixel. Furthermore, by adjusting the weights of the high-frequency edge values ​​and mid-frequency detail values, it achieves adjustable sharpening radius at low cost. The technical solution is as follows: In a first aspect, an image processing apparatus is provided, the apparatus comprising: The low-pass filtering module is configured to perform low-pass filtering on the original brightness values ​​of the neighboring pixels of the target pixel in the target image using a first filtering window and a second filtering window, respectively, to obtain a first filtered value and a second filtered value of the target pixel, wherein the size of the first filtering window is larger than the size of the second filtering window. The edge detail determination module is configured to perform a weighted summation of the high-frequency edge value and the mid-frequency detail value of the target pixel to obtain the edge detail value of the target pixel. The high-frequency edge value is a parameter characterizing the high-frequency component of the brightness of the target pixel and the difference between the original brightness value of the target pixel and the second filter value. The mid-frequency detail value is a parameter characterizing the mid-frequency component of the brightness of the target pixel and the difference between the second filter value and the first filter value. The effective edge determination module is configured to determine the effective edge value of the target pixel based on the edge detail value; The edge overlay module is configured to overlay the effective edge value onto the original brightness value of the target pixel according to a first sharpening intensity, so as to obtain the brightness sharpening result of the target pixel.

[0005] In one possible implementation, the device further includes: The effective edge adjustment module is configured to obtain the corresponding sharpening intensity from the correspondence between pixel brightness and sharpening intensity based on the original brightness value of the target pixel, and obtain a second sharpening intensity; adjust the effective edge value of the target pixel according to the second sharpening intensity, and the adjusted effective edge value is used by the edge overlay module for brightness sharpening.

[0006] In one possible implementation, in the correspondence, the sharpening intensity corresponding to pixel brightness greater than or equal to a first brightness threshold and less than or equal to a second brightness threshold is equal to a fixed value, and the sharpening intensity corresponding to pixel brightness less than the first brightness threshold and pixel brightness greater than the second brightness threshold is less than the fixed value.

[0007] In one possible implementation, pixel brightness below the first brightness threshold is positively correlated with the corresponding sharpening intensity, while pixel brightness above the second brightness threshold is negatively correlated with the corresponding sharpening intensity.

[0008] In one possible implementation, the effective edge adjustment module is further configured as follows: Based on the bit width of the target image, the correspondence is obtained, wherein the bit width is used to indicate the range of the original brightness values ​​of the pixels in the target image.

[0009] In one possible implementation, the device further includes: The sharpening result adjustment module is configured to, in response to the brightness sharpening result being greater than the maximum brightness value, decrease the brightness sharpening result so that the decreased brightness sharpening result is between the maximum brightness value and the brightness sharpening result before the decrease, wherein the maximum brightness value refers to the maximum value among the original brightness values ​​of all neighboring pixels within the first filtering window centered on the target pixel; in response to the brightness sharpening result being less than or equal to the maximum brightness value and greater than or equal to the minimum brightness value, keep the brightness sharpening result unchanged, wherein the minimum brightness value refers to the minimum value among the original brightness values ​​of all neighboring pixels within the first filtering window centered on the target pixel; and in response to the brightness sharpening result being less than the minimum brightness value, increase the brightness sharpening result so that the increased brightness sharpening result is between the brightness sharpening result before the increase and the minimum brightness value.

[0010] In one possible implementation, the effective edge determination module is specifically configured as follows: In response to the edge detail value being greater than a first threshold, the edge detail value is taken as the valid edge value; In response to the edge detail value being less than or equal to the first threshold and greater than or equal to the second threshold, the edge detail value is reduced to obtain the effective edge value, wherein the second threshold is less than the first threshold; In response to the edge detail value being less than the second threshold, the effective edge value is determined to be 0.

[0011] Secondly, an image processing method is provided, the method comprising: The original brightness values ​​of the neighboring pixels of the target pixel in the target image are low-pass filtered by a first filtering window and a second filtering window respectively to obtain the first filtered value and the second filtered value of the target pixel. The size of the first filtering window is larger than the size of the second filtering window. The high-frequency edge value and the mid-frequency detail value of the target pixel are weighted and summed to obtain the edge detail value of the target pixel. The high-frequency edge value is a parameter characterizing the high-frequency component of the brightness of the target pixel and the difference between the original brightness value of the target pixel and the second filter value. The mid-frequency detail value is a parameter characterizing the mid-frequency component of the brightness of the target pixel and the difference between the second filter value and the first filter value. Based on the edge detail values, the effective edge values ​​of the target pixel are determined; According to the first sharpening intensity, the effective edge value is superimposed on the original brightness value of the target pixel to obtain the brightness sharpening result of the target pixel.

[0012] In one possible implementation, before superimposing the effective edge value onto the original brightness value of the target pixel according to a first sharpening intensity, the method further includes: Based on the original brightness value of the target pixel, the corresponding sharpening intensity is obtained from the correspondence between pixel brightness and sharpening intensity to obtain the second sharpening intensity; According to the second sharpening intensity, the effective edge value of the target pixel is adjusted, and the adjusted effective edge value is superimposed on the original brightness value of the target pixel for brightness sharpening.

[0013] In one possible implementation, in the correspondence, the sharpening intensity corresponding to pixel brightness greater than or equal to a first brightness threshold and less than or equal to a second brightness threshold is equal to a fixed value, and the sharpening intensity corresponding to pixel brightness less than the first brightness threshold and pixel brightness greater than the second brightness threshold is less than the fixed value.

[0014] In one possible implementation, pixel brightness below the first brightness threshold is positively correlated with the corresponding sharpening intensity, while pixel brightness above the second brightness threshold is negatively correlated with the corresponding sharpening intensity.

[0015] In one possible implementation, before obtaining the corresponding sharpening intensity from the correspondence between pixel brightness and sharpening intensity based on the original brightness value of the target pixel, the method further includes: Based on the bit width of the target image, the correspondence is obtained, wherein the bit width is used to indicate the range of the original brightness values ​​of the pixels in the target image.

[0016] In one possible implementation, after superimposing the effective edge value onto the original brightness value of the target pixel according to a first sharpening intensity to obtain the brightness sharpening result of the target pixel, the method further includes: In response to the brightness sharpening result being greater than the maximum brightness value, the brightness sharpening result is reduced so that the reduced brightness sharpening result is between the maximum brightness value and the brightness sharpening result before reduction. The maximum brightness value refers to the maximum value among the original brightness values ​​of each neighboring pixel in the first filtering window centered on the target pixel. In response to the brightness sharpening result being less than or equal to the maximum brightness value and greater than or equal to the minimum brightness value, the brightness sharpening result remains unchanged. The minimum brightness value refers to the minimum value among the original brightness values ​​of each neighboring pixel within the first filtering window centered on the target pixel. In response to the brightness sharpening result being less than the minimum brightness value, the brightness sharpening result is increased so that the increased brightness sharpening result is between the original brightness sharpening result and the minimum brightness value.

[0017] In one possible implementation, determining the effective edge value of the target pixel based on the edge detail value includes: In response to the edge detail value being greater than a first threshold, the edge detail value is taken as the valid edge value; In response to the edge detail value being less than or equal to the first threshold and greater than or equal to the second threshold, the edge detail value is reduced to obtain the effective edge value, wherein the second threshold is less than the first threshold; In response to the edge detail value being less than the second threshold, the effective edge value is determined to be 0.

[0018] Thirdly, a display chip is provided, the display chip including the image processing apparatus as provided in the first aspect.

[0019] Fourthly, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the image processing method provided in the second aspect.

[0020] Fifthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the image processing method provided in the second aspect.

[0021] In this embodiment, a first filtering window and a second filtering window are used to perform low-pass filtering on the original brightness value of the target pixel in the target image. Based on the result of the low-pass filtering and the original brightness value of the target pixel, the high-frequency edge value and the mid-frequency detail value of the target pixel are obtained. Weights are set for the high-frequency edge value and the mid-frequency detail value respectively. By adjusting the weights of the two, the sharpening radius can be adjusted at low cost. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the structure of an image processing device provided in an embodiment of this application; Figure 2 This is a schematic diagram of an edge detail value provided in an embodiment of this application; Figure 3 This is a schematic diagram of an effective edge value determination process provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the mapping relationship between pixel brightness and sharpening intensity provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating an adjustment of a sharpening result provided in an embodiment of this application; Figure 6 This is a flowchart of an image processing method provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an image processing device provided in an embodiment of this application; Figure 8 This is a schematic diagram of another image processing device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0025] In the field of image processing, image quality is limited by multiple objective factors, among which sensor resolution is a key hardware factor restricting the information carrying capacity of an image. Currently, although high-resolution image sensors are widely used in various imaging devices, simply increasing resolution is often difficult to achieve in resource-constrained scenarios due to limitations in device manufacturing costs and system power consumption. Simultaneously, in the image signal processing stage, smoothing operations such as noise reduction, interpolation, and color correction performed to suppress noise, compensate for pixel defects, and improve the image signal-to-noise ratio inevitably weaken high-frequency detail components in the image, causing varying degrees of blurring at image edges and texture areas, affecting the image's visual clarity and detail representation.

[0026] Image sharpening, as an image processing technique, reduces blur in an image by enhancing its edges and details, making the image clearer. Among current image sharpening techniques, the traditional Unsharp Mask (USM) algorithm has become the mainstream implementation due to its simple structure and high computational efficiency. This algorithm typically controls the sharpening effect through three key parameters: radius, sharpening intensity, and threshold.

[0027] However, in practical engineering applications, the above-mentioned sharpening methods face several technical limitations. For example, at the hardware implementation level, in order to achieve flexible adjustment of the radius parameter, it is usually necessary to integrate multiple filter kernels of different scales, which not only significantly increases the hardware area and power consumption, but also increases the manufacturing cost of the system.

[0028] Based on this, embodiments of this application provide an image processing apparatus for sharpening images. The apparatus uses a first filtering window and a second filtering window to perform low-pass filtering on the original brightness values ​​of target pixels in the target image. Based on the low-pass filtering result and the original brightness values ​​of the target pixels, high-frequency edge values ​​and mid-frequency detail values ​​of the target pixels are obtained. Weights are assigned to the high-frequency edge values ​​and the mid-frequency detail values ​​respectively. By adjusting these weights, the sharpening radius can be adjusted at low cost.

[0029] The image processing apparatus provided in the embodiments of this application will now be explained in detail.

[0030] Figure 1 This is a schematic diagram of the structure of an image processing device provided in an embodiment of this application, as shown below. Figure 1As shown, the device includes a low-pass filtering module 101, an edge detail determination module 102, an effective edge determination module 103, and an edge overlay module 104. The low-pass filtering module 101 is configured to perform low-pass filtering on the original brightness values ​​of neighboring pixels of a target pixel in the target image using a first filtering window and a second filtering window, respectively, to obtain a first filtered value and a second filtered value for the target pixel. The size of the first filtering window is larger than the size of the second filtering window. The edge detail determination module 102 is configured to perform a weighted summation of the high-frequency edge value and the mid-frequency detail value of the target pixel to obtain the edge detail value of the target pixel. The high-frequency edge value is a parameter characterizing the high-frequency component of the target pixel's brightness, and is the difference between the original brightness value and the second filtered value. The mid-frequency detail value is a parameter characterizing the mid-frequency component of the target pixel's brightness, and is the difference between the second filtered value and the first filtered value. The effective edge determination module 103 is configured to determine the effective edge value of the target pixel based on the edge detail value. The edge overlay module 104 is configured to overlay the effective edge value onto the original brightness value of the target pixel according to the first sharpening intensity, so as to obtain the brightness sharpening result of the target pixel.

[0031] The first filtering window, also known as the first filtering kernel or the first convolution kernel, is used to perform low-pass filtering on the original brightness values ​​of the neighboring pixels of the target pixel, thereby obtaining the first filtered value of the target pixel. Similarly, the second filtering window, also known as the second filtering kernel or the second convolution kernel, is used to perform low-pass filtering on the original brightness values ​​of the neighboring pixels of the target pixel, thereby obtaining the second filtered value of the target pixel.

[0032] Low-pass filtering can take many forms. For example, the low-pass filter can be a Gaussian low-pass filter (also known as Gaussian blurring), in which case the two filtering windows mentioned above can also be called Gaussian kernels.

[0033] The first and second filter windows are typically odd-sized; for example, the first filter window is 5×5 and the second filter window is 3×3.

[0034] When using the first window and the second window to determine the filtering result of the original brightness value of the target pixel, the target pixel is taken as the center of the filtering window. By processing the original brightness values ​​of the pixels covered by the filtering window, low-pass filtering of the target pixel is achieved.

[0035] In some embodiments, the target pixel may be located in the boundary region of the image. In this case, when the target pixel is used as the center of the filtering window, a portion of the area covered by the filtering window will extend beyond the effective area of ​​the image; that is, part of the filtering window covers the area outside the image boundary. In other words, this portion of the image boundary covered by the filtering window contains no data, and therefore, it is necessary to fill in this portion of the image boundary.

[0036] There are various ways to implement filling. For example, it can be fixed value filling, such as filling the entire area outside the image boundary with 0 or 255; or it can be mirror filling, such as mapping the data in the effective area of ​​the image to the area outside the image boundary in a mirror-symmetric manner. This application does not limit this.

[0037] The first filtered value is obtained by low-pass filtering the original brightness values ​​of the neighboring pixels of the target pixel using the first filtering window. Since the size of the first filtering window is larger than that of the second filtering window, and the larger filtering window covers a wider range of pixels, when using the first filtering window for filtering, a weighted calculation is performed on pixels in a larger range around the target pixel. This weighted calculation of pixels in a large range will smooth out the mid-frequency and high-frequency values ​​in the image, so the first filtered value includes low-frequency values.

[0038] The aforementioned low-frequency first filter value corresponds to a slowly changing, flat area in the image, specifically manifested as large, uniform color blocks or smooth brightness gradients. For example, low-frequency values ​​in an image could be the skin tone areas of a face, the background of the sky, or the overall color of a wall.

[0039] The second filtered value is obtained by low-pass filtering the original brightness values ​​of the neighboring pixels of the target pixel using the second filtering window. Since the second filtering window is a small window, and the pixel range covered by the small filtering window is small, when using the second filtering window for filtering processing, only a small number of pixels around the target pixel are weighted. This weighted calculation of pixels within a small range cannot smooth out the mid-frequency values ​​in the image, but can only suppress high-frequency values. Therefore, the second filtered value includes both low-frequency and mid-frequency values.

[0040] The mid-frequency values ​​in the second filter value mentioned above correspond to the regions in the image between sharp edges and gentle areas, specifically representing the fine structure and texture of an object's surface. For example, the mid-frequency values ​​in an image could be the texture of skin, the hair-like appearance of eyebrows, and the subtle wrinkles of lips. Low-frequency values ​​have been described above and will not be repeated here.

[0041] It should be noted that the above embodiments are all described using a single pixel as an example. When performing low-pass filtering on the entire image, the above operation needs to be repeated for each pixel in the entire image. In some embodiments, a sliding window mechanism can be used to traverse the entire image, that is, two fixed-size filtering windows (such as a first filtering window and a second filtering window) are used respectively. Starting from the first pixel in the upper left corner of the image, the window slides from top to bottom and from left to right, pixel by pixel, thereby performing filtering processing on each pixel in the image. That is, after the filtering operation of the current pixel is completed, the filtering window is moved one pixel to the right, that is, the filtering window slides to the next pixel position; when the filtering window slides to the end of the line, the filtering window slides to the beginning of the next line, and the above process is repeated until all pixels are processed.

[0042] High-frequency edge values ​​are the difference between the original brightness value of a target pixel and the second filtered value. They typically correspond to the sharpest abrupt changes and boundaries in an image, such as object outlines, lip contours, and pupil boundaries. These areas exhibit drastic pixel value changes.

[0043] In some embodiments, the process of obtaining the edge detail value of a target pixel by weighted summation of its high-frequency edge values ​​and mid-frequency detail values ​​includes: obtaining a first weight and a second weight, wherein the first weight is the weight corresponding to the high-frequency edge value of the target pixel, and the second weight is the weight corresponding to the mid-frequency detail value of the target pixel; multiplying the first weight by the high-frequency edge value to obtain a first value, and multiplying the second weight by the mid-frequency detail value to obtain a second value; and adding the first value and the second value to obtain the edge detail value of the target pixel.

[0044] Based on the above description, the first weight controls the proportion of high-frequency edge values ​​in the final edge detail value, while the second weight controls the proportion of mid-frequency detail values. In other words, by adjusting the values ​​of the first and second weights, the effect of controlling edges and details can be achieved. This is equivalent to achieving adjustable sharpening radius at a low cost by adjusting their weights, avoiding the high hardware costs associated with using multiple filter kernels of different scales to adjust the sharpening radius, and significantly reducing hardware area, power consumption, and manufacturing costs.

[0045] The first and second weights are a set of weights that users can flexibly configure according to their needs. For example, if you need the outline of objects in the image to be more obvious, you can set the first weight to be larger; if you need the details in the image to be more obvious, you can set the second weight to be larger.

[0046] In some embodiments, both the first weight and the second weight can be located in the range [0, 1]. That is, the first weight can be any number in the range [0, 1], and the second weight can also be any number in the range [0, 1].

[0047] For example, please refer to Figure 2 Cur_y is the original brightness value of the target pixel; smooth_v0 is the second filtered value obtained by low-pass filtering the target pixel using the second filtering window; smooth_v1 is the first filtered value obtained by low-pass filtering the target pixel using the first filtering window; high-frequency edge value is the region between the original brightness value and the second filtered value of the target pixel, that is, the difference between the original brightness value and the second filtered value of the target pixel; mid-frequency detail value is the region between the second filtered value and the first filtered value, that is, the difference between the second filtered value and the first filtered value.

[0048] In some embodiments, the effective edge determination module 103 is specifically configured to: in response to an edge detail value being greater than a first threshold, use the edge detail value as an effective edge value; in response to an edge detail value being less than or equal to the first threshold and greater than or equal to a second threshold, decrease the edge detail value to obtain an effective edge value, wherein the second threshold is less than the first threshold; and in response to an edge detail value being less than the second threshold, determine that the effective edge value is 0.

[0049] In some embodiments, when the edge detail value is less than or equal to a first threshold and greater than or equal to a second threshold, the edge detail value and the effective edge value satisfy a linear relationship, and the effective edge value of the target pixel can be calculated by the following formula (1): highpass_coring_v = coring_k (highpass_v - coring_thr0) (1) Wherein, highpass_coring_v is the effective edge value of the target pixel, coring_k is the first slope, highpass_v is the edge detail value of the target pixel, and coring_thr0 is the second threshold. The first slope can be calculated using the following formula (2): coring_k = (coring_thr) / (coring_thr - coring_thr0) (2) Here, coring_thr is the first threshold.

[0050] In some embodiments, the values ​​of the first threshold and the second threshold are determined based on experience or actual needs, and this application embodiment does not impose any limitations on this. For example, for a YUV domain image that uses an 8-bit width to quantize the luminance component, the maximum value of the luminance component is 255. In this case, the first threshold can be set to be less than or equal to 30, and the second threshold can be located in the range of [5, 10].

[0051] Based on the above, the process of determining the effective edge values ​​of a target pixel can be divided into three cases, which can also be achieved through... Figure 3 To represent. For example... Figure 3 As shown, coring_thr is the first threshold, coring_thr0 is the second threshold, coring_k is the first slope, the horizontal axis highpass_v is the edge detail value of the target pixel, and the vertical axis highpass_coring_v is the effective edge value of the target pixel. When highpass_v is less than coring_thr0, highpass_coring_v is 0; when highpass_v is between coring_thr0 and coring_thr, highpass_coring_v is calculated based on the above formulas (1) and (2); when highpass_v is greater than coring_thr, highpass_coring_v is equal to highpass_v.

[0052] Based on the above description, the effective edge determination module 103 divides the edge detail values ​​into three intervals using two thresholds of different sizes (such as a first threshold and a second threshold). Different processing methods are applied to different intervals to determine the effective edge values. This three-segment processing method enables differentiated processing of edge detail values, effectively preserving weak edges and texture details while suppressing noise. It avoids visual abrupt changes and detail loss caused by a single threshold, thereby significantly improving the sharpening effect and visual quality.

[0053] In some embodiments, the process of superimposing the effective edge value onto the original brightness value of the target pixel according to the first sharpening intensity to obtain the brightness sharpening result of the target pixel includes: multiplying the first sharpening intensity by the effective edge value to obtain a third value, and adding the third value to the original brightness value of the target pixel to obtain the brightness sharpening result of the target pixel.

[0054] In some embodiments, the first sharpening intensity is a value that the user can flexibly configure as needed. For example, if more distinct edge details of objects in an image are required, the first sharpening intensity can be set to a larger value.

[0055] In some embodiments, the image processing apparatus further includes: an effective edge adjustment module, configured to obtain a second sharpening intensity based on the original brightness value of the target pixel from the correspondence between pixel brightness and sharpening intensity; and to adjust the effective edge value of the target pixel according to the second sharpening intensity, wherein the adjusted effective edge value is used by the edge overlay module for brightness sharpening. That is, the adjusted effective edge value can be overlaid onto the original brightness value of the target pixel according to the first sharpening intensity to obtain the brightness sharpening result of the target pixel.

[0056] In some embodiments, the effective edge adjustment module is further configured to: obtain the correspondence between pixel brightness and sharpening intensity based on the bit width of the target image, wherein the bit width is used to indicate the range of the original brightness values ​​of pixels in the target image.

[0057] The bit width (bit), also known as bit depth, of an image refers to the number of binary bits occupied by each color component or luminance component of a single pixel. This parameter determines the representation range of a single color component or luminance component. In this embodiment, the bit width of the target image indicates the representation range of the luminance component of a pixel. Since the representation range of the luminance component differs with different bit widths, and consequently the correspondence between pixel luminance and sharpening intensity also differs, the effective edge adjustment module can obtain the corresponding correspondence between pixel luminance and sharpening intensity based on the bit width of the target pixel.

[0058] In some embodiments, in the above correspondence between pixel brightness and sharpening intensity, the sharpening intensity corresponding to pixel brightness greater than or equal to a first brightness threshold and less than or equal to a second brightness threshold is equal to a fixed value. The sharpening intensity corresponding to pixel brightness less than the first brightness threshold and pixel brightness greater than the second brightness threshold is less than the fixed value. The first brightness threshold is less than the second brightness threshold.

[0059] Since darker areas in an image tend to have more noise, the sharpening intensity can be reduced in these areas. In other words, a lower sharpening intensity is applied to darker areas to effectively suppress noise. Specifically, if the brightness of a pixel is below a first brightness threshold, it indicates that the pixel is located in a darker area of ​​the image. In this case, setting the corresponding sharpening intensity below a fixed value can effectively suppress noise in dark areas.

[0060] Additionally, brighter areas in an image are prone to halo effects. Therefore, the sharpening intensity can be reduced in brighter areas by setting a lower sharpening intensity, thus effectively suppressing the halo effect. In other words, if the brightness of a pixel exceeds a second brightness threshold, it indicates that the pixel is located in a brighter area of ​​the image. In this case, setting the corresponding sharpening intensity below a fixed value can effectively reduce the halo effect in bright areas.

[0061] In some embodiments, pixel brightness less than a first brightness threshold is positively correlated with the corresponding sharpening intensity, and pixel brightness greater than a second brightness threshold is negatively correlated with the corresponding sharpening intensity.

[0062] Because darker areas in an image tend to have higher noise levels, and within a certain range, noise decreases as brightness increases, the positive correlation between pixel brightness and sharpening intensity can be used to linearly reduce sharpening intensity in these darker areas. In other words, the darker the area, the lower the sharpening intensity, thus effectively suppressing noise. Specifically, if the brightness of a pixel is below a first brightness threshold, it indicates that the pixel is located in a darker area of ​​the image. In this case, the positive correlation between pixel brightness and sharpening intensity is established, and the sharpening intensity is linearly reduced based on this correlation, thereby effectively suppressing noise in dark areas.

[0063] In some embodiments, the pixel brightness of the dark area and the corresponding sharpening intensity satisfy the following formula (3): str_y = dark_k cur_y(3) Where str_y is the sharpening intensity, dark_k is the first brightness slope, and cur_y is the pixel brightness. The first brightness slope satisfies the following formula (4): dark_k = 1 / dark_thr (4) Where dark_thr is the first brightness threshold.

[0064] Because brighter areas in an image are prone to halo effects, and within a certain range, the halo effect intensifies with increasing brightness, the negative correlation between pixel brightness and sharpening intensity can be used to linearly reduce sharpening intensity in brighter areas. That is, a lower sharpening intensity is set for brighter areas, effectively suppressing the halo effect. In other words, when the brightness of a pixel exceeds a second brightness threshold, it indicates that the pixel is located in a brighter area of ​​the image. In this case, the pixel brightness is set to have a negative correlation with the corresponding sharpening intensity. Based on this negative correlation, the sharpening intensity is linearly reduced, effectively minimizing the halo effect in bright areas.

[0065] In some embodiments, the pixel brightness of the bright area and the corresponding sharpening intensity satisfy the following formula (5): str_y = bright_k cur_y + b(5) Where str_y is the sharpening intensity, bright_k is the second brightness slope, cur_y is the pixel brightness, and b is the first intercept. The second brightness slope and the first intercept satisfy the following formulas (6) and (7): bright_k = 1 / (bright_thr - max_v) (6) b = max_v / (max_v - bright_thr) (7) Where, bright_thr is the second brightness threshold, and max_v is the maximum pixel brightness, which can be determined according to the bit width of the image. For example, assuming the bit width is N, the maximum pixel brightness can be calculated using the following formula (8): max_v = 2^N – 1(8) In this embodiment, max_v represents the maximum value of the luminance component of the target image, that is, the range of the luminance component is 0 to 2^N – 1. For example, if the target image is an 8-bit wide image, the maximum luminance of a single pixel in the target image is 255, and the minimum luminance is 0.

[0066] In some embodiments, the values ​​of the first brightness threshold and the second brightness threshold can be determined based on experience or actual needs, and this application embodiment does not impose any limitations on this. For example, if the maximum brightness value in an 8-bit wide image is 255, then the first brightness threshold can be set to 50 and the second brightness threshold to 200.

[0067] In some embodiments, the correspondence between pixel brightness and sharpening intensity is characterized by the above formulas (3) to (8). In other embodiments, the correspondence between pixel brightness and sharpening intensity can also be characterized in other ways. For example, it can be characterized by a mapping table between pixel brightness and sharpening intensity, or by a curve relationship between pixel brightness and sharpening intensity. This application does not limit this.

[0068] For example, the correspondence between pixel brightness and sharpening intensity can also be derived through... Figure 4 To characterize. For example... Figure 4 As shown, dark_thr is the first brightness threshold, bright_thr is the second brightness threshold, dark_k is the first brightness slope, bright_k is the second brightness slope, max_v is the maximum brightness value, the horizontal axis cur_y is the original brightness of the pixel, and the vertical axis str_y is the sharpening intensity. When cur_y is less than dark_thr, the sharpening intensity satisfies the positive correlation shown in formula (3) above; when cur_y is between dark_thr and bright_thr, str_y is 1; when cur_y is greater than bright_thr and less than max_v, the sharpening intensity satisfies the negative correlation shown in formula (5) above.

[0069] Based on the above, the effective edge adjustment module divides pixel brightness into three intervals using two different thresholds (e.g., a first brightness threshold and a second brightness threshold). Different processing methods are applied to each interval to determine the corresponding sharpening intensity. This three-segment processing linearly reduces sharpening intensity in darker areas of the image to suppress noise, and linearly reduces sharpening intensity in brighter areas to eliminate halo effects. In other words, by adaptively adjusting the sharpening intensity based on brightness values, noise in dark areas and halo effects in bright areas are effectively controlled, thereby improving image quality.

[0070] In some embodiments, by combining the above-mentioned correspondence between pixel brightness and sharpening intensity, the sharpening intensity corresponding to the original brightness value of the target pixel can be obtained from the correspondence. The obtained sharpening intensity is used as the second sharpening intensity, and then the second sharpening intensity is multiplied by the effective edge value of the target pixel to obtain the adjusted effective edge value.

[0071] In some embodiments, the image processing apparatus further includes: a sharpening result adjustment module, configured to: decrease the brightness sharpening result in response to a target pixel having a brightness sharpening result greater than a maximum brightness value, such that the reduced brightness sharpening result is between the maximum brightness value and the original brightness sharpening result, where the maximum brightness value refers to the maximum value among the original brightness values ​​of all neighboring pixels within a first filtering window centered on the target pixel; maintain the brightness sharpening result unchanged in response to a target pixel having a brightness sharpening result less than or equal to the maximum brightness value and greater than or equal to the minimum brightness value, where the minimum brightness value refers to the minimum value among the original brightness values ​​of all neighboring pixels within the first filtering window centered on the target pixel; and increase the brightness sharpening result in response to a target pixel having a brightness sharpening result less than the minimum brightness value, such that the increased brightness sharpening result is between the original brightness sharpening result and the minimum brightness value.

[0072] After obtaining the brightness sharpening result of the target pixel using the above method, the brightness sharpening result may exhibit overshoot (also known as overshoot) and undershoot artifacts. Generally, overshoot and undershoot need to be controlled to ensure image quality, but sometimes the presence of overshoot and undershoot can actually be beneficial. For example, around light sources, increasing undershoot can counteract diffusion, resulting in better convergence at the light source. Therefore, overshoot and undershoot can be flexibly controlled according to the user's needs.

[0073] In some embodiments, if the brightness sharpening result of the target pixel is greater than the maximum brightness value, it indicates that the brightness sharpening result of the target pixel has overshoot. In this case, the brightness sharpening result can be reduced by the following formula (9): out = max_win + over_alpha (sharpened_v - max_win) (9) Where out is the brightness sharpening result after reduction, max_win is the maximum brightness value, over_alpha is the overshoot weight, and sharpened_v is the brightness sharpening result of the target pixel, that is, the brightness sharpening result before reduction.

[0074] If the brightness sharpening result of the target pixel is less than the minimum brightness value, it indicates that the brightness sharpening result of the target pixel has undershoot. In this case, the brightness sharpening result can be increased by the following formula (10): out = min_win - under_alpha (min_win - sharpened_v) (10) Where out is the brightness sharpening result after the increase, min_win is the minimum brightness value, under_alpha is the undershoot weight, and sharpened_v is the brightness sharpening result of the target pixel, that is, the brightness sharpening result before the increase.

[0075] Overshoot weight and undershoot weight are a set of weights that users can flexibly configure according to their needs. For example, if a more obvious overshoot control effect is required, the overshoot weight can be set larger; if a more obvious undershoot control effect is required, the undershoot weight can be set larger.

[0076] In some embodiments, both overshoot weight and undershoot weight can be located in the range [0, 1]. For example, to completely suppress overshoot, over_alpha can be set to 0; to completely suppress undershoot, under_alpha can be set to 0.

[0077] For example, please refer to Figure 5 `min_win` represents the minimum brightness value, `max_win` represents the maximum brightness value, `y_under` represents the minimum value of the undershoot control (i.e., the brightness sharpening result before increasing the value), and `y_over` represents the maximum value of the overshoot control (i.e., the brightness sharpening result before decreasing the value). The horizontal axis `sharpened_v` represents the brightness sharpening result, i.e., the brightness sharpening result without overshoot or undershoot control, and the vertical axis `out` represents the brightness sharpening result after overshoot or undershoot control. When the brightness sharpening result of the target pixel is less than `min_win`, the undershoot control result lies between `y_under` and `min_win`; when the brightness sharpening result of the target pixel is less than or equal to `max_win` and greater than or equal to `min_win`, the brightness sharpening result remains unchanged; when the brightness sharpening result of the target pixel is greater than `max_win`, the overshoot control result lies between `max_win` and `y_over`.

[0078] In summary, the sharpening result adjustment module controls the overshoot and undershoot effects by adjusting two weights (such as overshoot weight and undershoot weight), thereby achieving the desired effect for the user.

[0079] In this embodiment, a first filtering window and a second filtering window are used to perform low-pass filtering on the original brightness values ​​of the target pixels in the target image. Based on the result of the low-pass filtering and the original brightness values ​​of the target pixels, the high-frequency edge values ​​and mid-frequency detail values ​​of the target pixels are obtained. A first weight and a second weight are set for the high-frequency edge values ​​and the mid-frequency detail values, respectively. By adjusting the weights of the two, the sharpening radius can be adjusted at low cost, avoiding the high hardware cost caused by using multiple filter kernels of different scales to adjust the sharpening radius, and significantly reducing hardware area, power consumption and manufacturing cost.

[0080] This application provides an image processing method, which can be executed by a processing device. The flowchart of this method is shown below. Figure 6 As shown, this includes, but is not limited to, steps 601 to 604.

[0081] Step 601: Use a first filtering window and a second filtering window to perform low-pass filtering on the original brightness values ​​of the neighboring pixels of the target pixel in the target image, respectively, to obtain the first filtered value and the second filtered value of the target pixel. The size of the first filtering window is larger than the size of the second filtering window.

[0082] Step 602: Perform a weighted summation of the high-frequency edge value and the mid-frequency detail value of the target pixel to obtain the edge detail value of the target pixel. The high-frequency edge value is a parameter that characterizes the high-frequency component of the brightness of the target pixel. The high-frequency edge value is the difference between the original brightness value of the target pixel and the second filter value. The mid-frequency detail value is a parameter that characterizes the mid-frequency component of the brightness of the target pixel. The mid-frequency detail value is the difference between the second filter value and the first filter value.

[0083] Step 603: Determine the effective edge value of the target pixel based on the edge detail value.

[0084] In some embodiments, determining the effective edge value of a target pixel based on the edge detail value includes: in response to the edge detail value being greater than a first threshold, using the edge detail value as the effective edge value; in response to the edge detail value being less than or equal to the first threshold and greater than or equal to a second threshold, reducing the edge detail value to obtain the effective edge value, wherein the second threshold is less than the first threshold; and in response to the edge detail value being less than the second threshold, determining the effective edge value to be 0.

[0085] Step 604: According to the first sharpening intensity, the effective edge value is superimposed on the original brightness value of the target pixel to obtain the brightness sharpening result of the target pixel.

[0086] In some embodiments, before superimposing the effective edge value onto the original brightness value of the target pixel according to the first sharpening intensity, the method further includes: obtaining a corresponding sharpening intensity from the correspondence between pixel brightness and sharpening intensity based on the original brightness value of the target pixel to obtain a second sharpening intensity; adjusting the effective edge value of the target pixel according to the second sharpening intensity, and using the adjusted effective edge value to superimpose onto the original brightness value of the target pixel for brightness sharpening.

[0087] In some embodiments, before obtaining the corresponding sharpening intensity from the correspondence between pixel brightness and sharpening intensity based on the original brightness value of the target pixel, the method further includes: obtaining the correspondence between pixel brightness and sharpening intensity based on the bit width of the target image, wherein the bit width is used to indicate the range of the original brightness value of the pixel in the target image.

[0088] In some embodiments, in the above correspondence between pixel brightness and sharpening intensity, the sharpening intensity corresponding to pixel brightness greater than or equal to a first brightness threshold and less than or equal to a second brightness threshold is equal to a fixed value, and the sharpening intensity corresponding to pixel brightness less than the first brightness threshold and pixel brightness greater than the second brightness threshold is less than the fixed value. Wherein, the first brightness threshold is less than the second brightness threshold.

[0089] In some embodiments, pixel brightness less than a first brightness threshold is positively correlated with the corresponding sharpening intensity, and pixel brightness greater than a second brightness threshold is negatively correlated with the corresponding sharpening intensity.

[0090] In some embodiments, after superimposing effective edge values ​​onto the original brightness value of the target pixel according to a first sharpening intensity to obtain a brightness sharpening result for the target pixel, the method further includes: in response to the target pixel's brightness sharpening result being greater than a maximum brightness value, reducing the brightness sharpening result so that the reduced brightness sharpening result is located between the maximum brightness value and the brightness sharpening result before reduction, wherein the maximum brightness value refers to the maximum value among the original brightness values ​​of each neighboring pixel within a first filtering window centered on the target pixel; in response to the target pixel's brightness sharpening result being less than or equal to the maximum brightness value and greater than or equal to the minimum brightness value, keeping the brightness sharpening result unchanged, wherein the minimum brightness value refers to the minimum value among the original brightness values ​​of each neighboring pixel within a first filtering window centered on the target pixel; and in response to the target pixel's brightness sharpening result being less than the minimum brightness value, increasing the brightness sharpening result so that the increased brightness sharpening result is located between the brightness sharpening result before increase and the minimum brightness value.

[0091] In the image processing method provided in this application embodiment, a first filtering window and a second filtering window are used to perform low-pass filtering on the original brightness value of the target pixel in the target image. Based on the result of the low-pass filtering and the original brightness value of the target pixel, the high-frequency edge value and the mid-frequency detail value of the target pixel are obtained. A first weight and a second weight are set for the high-frequency edge value and the mid-frequency detail value, respectively. By adjusting the weights of the two, the sharpening radius can be adjusted at low cost, avoiding the high hardware cost caused by using multiple filtering kernels of different scales to adjust the sharpening radius, and significantly reducing the hardware area, power consumption and manufacturing cost.

[0092] It should be noted that the above embodiments of the device are only illustrated by the division of the above functional modules when implementing their functions. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the methods and device embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the device embodiments, which will not be repeated here.

[0093] Figure 7 This is a schematic diagram of the structure of an image processing device provided in an embodiment of this application. The device can vary significantly due to differences in configuration or performance. It may include one or more processors 701 and one or more memories 702. The one or more memories 702 store at least one computer program, which is loaded and executed by the one or more processors 701 to enable the device to implement the image processing methods provided in the various method embodiments described above. Of course, the device may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The device may also include other components for implementing device functions, which will not be elaborated upon here. Figure 7 The image processing device shown can be a computer device.

[0094] In an exemplary embodiment, a computer device is also provided, which is capable of implementing the image processing methods provided in the various method embodiments described above. This computer device may be, for example, a smartphone, tablet computer, media player, laptop computer, or desktop computer. The terminal may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names. The computer device may also be a server or any other device capable of implementing the image processing methods described above.

[0095] See Figure 8 This illustration shows a schematic diagram of the structure of an image processing device according to an embodiment of this application. The image processing device 800 includes an image processing unit 801, and the image processing unit 801 includes various units for executing instructions. Figure 8In this process, the image processing device 801 is coupled to the memory 802, and it should be understood that the image processing device 800 also supports other memory configurations known in the art.

[0096] The memory 802 may include one or more computer-readable storage media, which may be non-transitory, and the computer-readable storage media stores at least one computer program, which is loaded and executed by the image processing apparatus 801 to enable the computer device 800 to implement any of the above-described image processing methods.

[0097] The memory 802 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one instruction, which is executed by the processing device 801 to cause the computer device 800 to implement the image processing method provided in the above method embodiments.

[0098] In one possible implementation, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0099] Figure 8 A display 806 coupled to the image processing apparatus 801 is also shown. For example, the display 806 is connected to a display controller 804, which is also connected to the image processing apparatus 801, thus coupling the display 806 to the image processing apparatus 801. In some cases, the image processing apparatus 800 can be used for wireless communication. Figure 8 Also shown are a speaker 809 and a microphone 810 coupled to the image processing device 801 via an encoder / decoder 811; and a wireless antenna 808 coupled to the wireless controller 805.

[0100] Display 806 is used to display a UI (User Interface). This UI may include graphics, text, icons, video, and any combination thereof. When display 806 is a touch screen, it also has the ability to collect touch signals on or above its surface. In this case, display 806 can also provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display 806 may be a single display, located on the front panel of the terminal; in other embodiments, display 806 may be at least two, respectively located on different surfaces of the terminal or in a folded design; in still other embodiments, display 806 may be a flexible display, located on a curved or folded surface of the terminal. Furthermore, display 806 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display 806 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0101] Microphone 810 is used to collect sound waves from the user and the environment, and input the sound waves to image processing device 801 for processing. Multiple microphones 810 can be used for stereo sound acquisition or noise reduction, each positioned at a different location on the terminal. Microphone 810 can also be an array microphone or an omnidirectional microphone. Speaker 809 is used to convert electrical signals from image processing device 801 into sound waves. Speaker 809 can be a traditional thin-film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement.

[0102] The image processing device 801 and the memory 802 may be contained in a system-in-package or system-on-a-chip device.

[0103] Input device 807 and power supply 803 are coupled to system-on-chip device 812. Optionally, such as Figure 8 As shown, when one or more optional boxes are present, the display 806, input device 807, speaker 809, microphone 810, wireless antenna 808, and power supply 803 are external to the system-on-chip device 812. However, each of the display 806, input device 807, speaker 809, microphone 810, wireless antenna 808, and power supply 803 can be coupled to components of the system-on-chip device 812, such as interfaces or controllers.

[0104] Power supply 803 is used to power the various components in the terminal. Power supply 803 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 803 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0105] In one possible implementation, the image processing device 801 and the memory 802 may be integrated into a set-top box, server, music player, video player, entertainment unit, navigation device, personal digital assistant (PDA), fixed location data unit, computer, laptop computer, tablet computer, communication device, mobile phone or other similar device.

[0106] The methods described in conjunction with the embodiments of this application can be directly implemented in software modules executed by the image processing apparatus. The software modules may reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, compact disc read-only memory (CDROM), or any other form of storage medium known in the art. The storage medium is coupled to the processing apparatus, thereby enabling the image processing apparatus to read information from and write information to the storage medium. Optionally, the storage medium may be integral with the image processing apparatus.

[0107] Those skilled in the art will understand that Figure 8 The structure shown does not constitute a limitation on the image processing device. The image processing device may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0108] In an exemplary embodiment, a chip is also provided, comprising a processor and a memory storing at least one computer program. The at least one computer program is loaded and executed by one or more processors to enable a computer device equipped with the chip to implement any of the aforementioned image processing methods. In an exemplary embodiment, a chip is also provided, comprising any of the aforementioned image processing apparatuses.

[0109] In an exemplary embodiment, a computer device is also provided, comprising a processor and a memory storing at least one computer program. The at least one computer program is loaded and executed by one or more processors to enable the computer device to implement any of the image processing methods described above.

[0110] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one computer program that is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-described image processing methods.

[0111] In one possible implementation, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0112] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the image processing methods described above.

[0113] It should be understood that "at least one" as mentioned herein refers to one or more, and "multiple" refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, in order to clearly describe the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., are not necessarily different.

[0114] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0115] The above descriptions are embodiments provided in this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An image processing apparatus, characterized in that, The device includes: The low-pass filtering module is configured to perform low-pass filtering on the original brightness values ​​of the neighboring pixels of the target pixel in the target image using a first filtering window and a second filtering window, respectively, to obtain a first filtered value and a second filtered value of the target pixel, wherein the size of the first filtering window is larger than the size of the second filtering window. The edge detail determination module is configured to perform a weighted summation of the high-frequency edge value and the mid-frequency detail value of the target pixel to obtain the edge detail value of the target pixel. The high-frequency edge value is a parameter characterizing the high-frequency component of the brightness of the target pixel and the difference between the original brightness value of the target pixel and the second filter value. The mid-frequency detail value is a parameter characterizing the mid-frequency component of the brightness of the target pixel and the difference between the second filter value and the first filter value. The effective edge determination module is configured to determine the effective edge value of the target pixel based on the edge detail value; The edge overlay module is configured to overlay the effective edge value onto the original brightness value of the target pixel according to a first sharpening intensity, so as to obtain the brightness sharpening result of the target pixel.

2. The apparatus as claimed in claim 1, characterized in that, The device further includes: The effective edge adjustment module is configured to obtain the corresponding sharpening intensity from the correspondence between pixel brightness and sharpening intensity based on the original brightness value of the target pixel, and obtain a second sharpening intensity; adjust the effective edge value of the target pixel according to the second sharpening intensity, and the adjusted effective edge value is used by the edge overlay module for brightness sharpening.

3. The apparatus as described in claim 2, characterized in that, In the correspondence, the sharpening intensity corresponding to the pixel brightness that is greater than or equal to the first brightness threshold and less than or equal to the second brightness threshold is equal to a fixed value, and the sharpening intensity corresponding to the pixel brightness that is less than the first brightness threshold and the pixel brightness that is greater than the second brightness threshold is less than the fixed value.

4. The apparatus as described in claim 3, characterized in that, Pixel brightness below the first brightness threshold is positively correlated with the corresponding sharpening intensity, while pixel brightness above the second brightness threshold is negatively correlated with the corresponding sharpening intensity.

5. The apparatus according to any one of claims 2-4, characterized in that, The effective edge adjustment module is also configured to: Based on the bit width of the target image, the correspondence is obtained, wherein the bit width is used to indicate the range of the original brightness values ​​of the pixels in the target image.

6. The apparatus as claimed in claim 1, characterized in that, The device further includes: The sharpening result adjustment module is configured to, in response to the brightness sharpening result being greater than the maximum brightness value, decrease the brightness sharpening result so that the decreased brightness sharpening result is between the maximum brightness value and the brightness sharpening result before the decrease, wherein the maximum brightness value refers to the maximum value among the original brightness values ​​of all neighboring pixels within the first filtering window centered on the target pixel; in response to the brightness sharpening result being less than or equal to the maximum brightness value and greater than or equal to the minimum brightness value, keep the brightness sharpening result unchanged, wherein the minimum brightness value refers to the minimum value among the original brightness values ​​of all neighboring pixels within the first filtering window centered on the target pixel; and in response to the brightness sharpening result being less than the minimum brightness value, increase the brightness sharpening result so that the increased brightness sharpening result is between the brightness sharpening result before the increase and the minimum brightness value.

7. The apparatus as claimed in claim 1, characterized in that, The effective edge determination module is specifically configured as follows: In response to the edge detail value being greater than a first threshold, the edge detail value is taken as the valid edge value; In response to the edge detail value being less than or equal to the first threshold and greater than or equal to the second threshold, the edge detail value is reduced to obtain the effective edge value, wherein the second threshold is less than the first threshold; In response to the edge detail value being less than the second threshold, the effective edge value is determined to be 0.

8. An image processing method, characterized in that, The method includes: The original brightness values ​​of the neighboring pixels of the target pixel in the target image are low-pass filtered by a first filtering window and a second filtering window respectively to obtain the first filtered value and the second filtered value of the target pixel. The size of the first filtering window is larger than the size of the second filtering window. The high-frequency edge value and the mid-frequency detail value of the target pixel are weighted and summed to obtain the edge detail value of the target pixel. The high-frequency edge value is a parameter characterizing the high-frequency component of the brightness of the target pixel and the difference between the original brightness value of the target pixel and the second filter value. The mid-frequency detail value is a parameter characterizing the mid-frequency component of the brightness of the target pixel and the difference between the second filter value and the first filter value. Based on the edge detail values, the effective edge values ​​of the target pixel are determined; According to the first sharpening intensity, the effective edge value is superimposed on the original brightness value of the target pixel to obtain the brightness sharpening result of the target pixel.

9. The method as described in claim 8, characterized in that, Before superimposing the effective edge value onto the original brightness value of the target pixel according to the first sharpening intensity, the method further includes: Based on the original brightness value of the target pixel, the corresponding sharpening intensity is obtained from the correspondence between pixel brightness and sharpening intensity to obtain the second sharpening intensity; According to the second sharpening intensity, the effective edge value of the target pixel is adjusted, and the adjusted effective edge value is superimposed on the original brightness value of the target pixel for brightness sharpening.

10. The method as described in claim 9, characterized in that, In the correspondence, the sharpening intensity corresponding to the pixel brightness that is greater than or equal to the first brightness threshold and less than or equal to the second brightness threshold is equal to a fixed value, and the sharpening intensity corresponding to the pixel brightness that is less than the first brightness threshold and the pixel brightness that is greater than the second brightness threshold is less than the fixed value.

11. The method as described in claim 10, characterized in that, Pixel brightness below the first brightness threshold is positively correlated with the corresponding sharpening intensity, while pixel brightness above the second brightness threshold is negatively correlated with the corresponding sharpening intensity.

12. The method according to any one of claims 9-11, characterized in that, Before obtaining the corresponding sharpening intensity from the correspondence between pixel brightness and sharpening intensity based on the original brightness value of the target pixel, the method further includes: Based on the bit width of the target image, the correspondence is obtained, wherein the bit width is used to indicate the range of the original brightness values ​​of the pixels in the target image.

13. The method as described in claim 8, characterized in that, After superimposing the effective edge value onto the original brightness value of the target pixel according to the first sharpening intensity to obtain the brightness sharpening result of the target pixel, the method further includes: In response to the brightness sharpening result being greater than the maximum brightness value, the brightness sharpening result is reduced so that the reduced brightness sharpening result is between the maximum brightness value and the brightness sharpening result before reduction. The maximum brightness value refers to the maximum value among the original brightness values ​​of each neighboring pixel in the first filtering window centered on the target pixel. In response to the brightness sharpening result being less than or equal to the maximum brightness value and greater than or equal to the minimum brightness value, the brightness sharpening result remains unchanged. The minimum brightness value refers to the minimum value among the original brightness values ​​of each neighboring pixel within the first filtering window centered on the target pixel. In response to the brightness sharpening result being less than the minimum brightness value, the brightness sharpening result is increased so that the increased brightness sharpening result is between the original brightness sharpening result and the minimum brightness value.

14. The method as described in claim 8, characterized in that, Determining the effective edge value of the target pixel based on the edge detail value includes: In response to the edge detail value being greater than a first threshold, the edge detail value is taken as the valid edge value; In response to the edge detail value being less than or equal to the first threshold and greater than or equal to the second threshold, the edge detail value is reduced to obtain the effective edge value, wherein the second threshold is less than the first threshold; In response to the edge detail value being less than the second threshold, the effective edge value is determined to be 0.

15. A display chip, characterized in that, The display chip includes the image processing apparatus as described in any one of claims 1-7.

16. A computer device, characterized in that, The computer device includes a memory and a processor, the memory being used to store computer programs, and the processor being used to execute the computer programs stored in the memory to implement the method according to any one of claims 8-14.

17. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 8-14.

Images