A curve sharpening method, system, device, and medium for peak picking

Abstract

The application discloses a curve sharpening method, system, device and medium for peak extraction, and relates to the field of data processing. The method comprises the following steps: acquiring a pixel value curve; updating the pixel value of each pixel point in the pixel value curve based on a preset calculation formula, as a sharpened pixel value curve. Based on the above processing, the application realizes the sharpening processing of the pixel value in the pixel value curve by subtracting the absolute value of the first-order gradient value multiplied by λ from the original pixel value. Therefore, the application can not only reduce the peak width radius and achieve the sharpening purpose, but also will not increase the center pixel value of the light bar, avoid the saturation of the signal center, and improve the accuracy of the peak calculation result.

Description

Technical Field

[0001] This invention belongs to the field of data processing, and in particular relates to a curve sharpening method, system, device and medium for peak extraction. Background Technology

[0002] When using spectral confocal or 3D line laser to detect transparent objects, in order to identify the height information of different layers in the transparent object, it is necessary to extract multiple peaks in the pixel value curve formed by the multi-layer reflection signals.

[0003] Due to the superposition of multiple reflection signals, the overlap of reflection signals from some adjacent layers is relatively high. In the process of peak value extraction or light stripe center extraction, it is not possible to directly locate and extract based on the judgment condition of rising edge-falling edge, making it difficult to accurately extract the peak signals of different layers in the transparent test object.

[0004] To address the above issues, existing technologies can use non-mask sharpening methods based on convolutional kernels to further process pixel value curves and achieve peak signal separation for different layers. However, these non-mask sharpening methods increase pixel values ​​near the peaks, making the signal center prone to saturation and affecting the accuracy of peak calculation results.

[0005] Therefore, there is an urgent need for a curve sharpening method that can achieve non-mask sharpening of peak signals while avoiding saturation at the signal center. Summary of the Invention

[0006] This application proposes a curve sharpening method, system, device, and medium for peak extraction, which avoids signal center saturation while achieving non-mask sharpening of peak signals.

[0007] To achieve the above objectives, this application proposes the following technical solutions: In a first aspect of this application, a curve sharpening method for peak extraction is provided, comprising: Obtain a pixel value curve; wherein, the pixel value curve is formed by a row or column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layer transparent object by a spectral confocal sensor, or a light stripe image reflected from a multi-layer transparent object in online structured light measurement; Based on a preset calculation formula, the pixel values ​​of each pixel in the pixel value curve are updated to form the sharpened pixel value curve. The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

[0008] Optionally, the acquisition of pixel value curves includes: Obtain a column or a row of pixels from the detection image; the selection of the pixel column or pixel row is determined based on the dispersion direction in the dispersion image and the distribution direction of the light stripes in the light stripe image. Remove pixels below the image background threshold from the column or row of pixels, and perform filtering and noise reduction processing to obtain the pixel value curve.

[0009] Optionally, updating the pixel values ​​of each pixel in the pixel value curve based on a preset calculation formula to serve as the sharpening curve includes: Calculate the absolute value of the first-order gradient value for each pixel in the pixel value curve; Determine whether the absolute value is lower than a first preset threshold; if yes, set the absolute value to 0; if no, keep the absolute value unchanged. Based on a preset calculation formula, the pixel values ​​of each pixel in the pixel value curve are updated; The updated pixel value curve is used as the sharpening curve.

[0010] Optionally, after calculating the absolute value of the first-order gradient value of each pixel in the pixel value curve, if the absolute value is not lower than a first preset threshold, the curve sharpening method further includes: Determine whether the absolute value exceeds a second preset threshold; if yes, set the absolute value to the second preset threshold; if no, keep the absolute value unchanged. The second preset threshold is not lower than the first preset threshold.

[0011] Optionally, before using the updated pixel value curve as the sharpening curve, the curve sharpening method further includes: Determine whether the updated pixel value is lower than the third preset threshold; if yes, set the updated pixel value to the third preset threshold; otherwise, keep the updated pixel value unchanged.

[0012] Optionally, for a preset calculation formula, the curve sharpening method further includes: The value of the multiplier λ is based on the absolute value of the corresponding pixel. It decreases as it increases.

[0013] Optionally, for a preset calculation formula, the curve sharpening method further includes: The value of the multiplier λ increases as the pixel value P1 of the corresponding pixel increases.

[0014] In a second aspect of this application, a curve sharpening system for peak extraction is provided, the system comprising: The curve acquisition module is used to acquire pixel value curves; wherein, the pixel value curves are formed by a row or a column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layer transparent test object acquired by a spectral confocal sensor, or a light stripe image reflected from a multi-layer transparent test object in online structured light measurement; The pixel value update module is used to update the pixel value of each pixel in the pixel value curve based on a preset calculation formula, so as to obtain the sharpened pixel value curve. The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

[0015] In a third aspect of this application, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the curve sharpening method described in any of the first aspects.

[0016] In a fourth aspect of this application, 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 curve sharpening method described in any of the first aspects.

[0017] The beneficial effects of this application are as follows: This application provides a curve sharpening method for peak extraction, including: Obtain a pixel value curve; wherein the pixel value curve is formed by a row or column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layer transparent test object acquired by a spectral confocal sensor, or a light stripe image reflected from a multi-layer transparent test object in online structured light measurement.

[0018] Based on a preset calculation formula, the pixel values ​​of each pixel in the pixel value curve are updated to obtain the sharpened pixel value curve. The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

[0019] Based on the above processing, this application achieves pixel value sharpening in the pixel value curve by subtracting λ times the absolute value of the first-order gradient from the original pixel value. This not only reduces the peak radius, achieving the sharpening effect, but also avoids increasing the pixel value at the center of the light stripe, preventing signal center saturation and improving the accuracy of peak value calculation results. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a pixel value curve provided in this application; Figure 2 This is a schematic diagram of another pixel value curve provided in this application; Figure 3 This is a flowchart illustrating a curve sharpening method provided in this application; Figure 4 This is a sharpened light bar pattern provided in this application; Figure 5 This is a schematic diagram of the structure of a curve sharpening system provided in this application; Figure 6 This is a structural diagram of an electronic device provided in this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments.

[0022] Spectral confocal measurement and line structured light measurement, as important non-contact measurement methods, are widely used in machine vision inspection scenarios. The basic principle of line structured light measurement is to project a series of parallel, narrow light stripes onto the surface of the object being measured, and to obtain the surface morphology and height information by measuring the degree of distortion of the light stripes. Spectral confocal measurement, on the other hand, is based on the principle of spectral dispersion. After passing through a dispersive objective lens, light forms a series of continuously distributed focused spots with different spectral peak values ​​along the optical axis. There is a one-to-one quantitative mapping relationship between the spectral peak values ​​and the focusing distance. By decoding this mapping relationship, the corresponding focusing distance can be obtained after obtaining the spectral peak values.

[0023] In spectral confocal measurement, spectral peaks need to be extracted to accurately locate the spectral wavelength to be reflected from the side. In online structured light measurement, the peak points of the brightness distribution on the cross-section of the light stripe (corresponding to the brightest pixels) need to be extracted as the center of the light stripe. Therefore, both spectral confocal measurement and online structured light measurement require accurate location of pixel peaks.

[0024] Taking the line center extraction algorithm as an example, in spectral confocal applications, the gray value distribution of the light stripe cross section captured by the sensor approximately exhibits a Gaussian curve shape, such as... Figure 1 As shown in the enlarged area on the right, the line center extraction algorithm extracts the coordinates of the center point of the energy concentration based on the gray value distribution.

[0025] During the detection of transparent objects, due to the presence of multiple layers of reflected light, when the peak width and radius of the light stripe are large, the overlap between the reflected signals of adjacent layers is high. Weaker signals may lack a falling edge, making it impossible to extract the line center. Only one layer of line center information can be extracted from the multi-layer signal, resulting in the loss of multi-layer information. Figure 2 As shown.

[0026] To address the above issues, existing technologies can use non-mask sharpening methods based on convolutional kernels to further process pixel value curves and achieve peak signal separation for different layers. However, these non-mask sharpening methods increase pixel values ​​near the peaks, making the signal center prone to saturation and affecting the accuracy of peak calculation results.

[0027] To address the aforementioned problems, this application provides a curve sharpening method for peak extraction, such as... Figure 3 As shown, it includes the following steps: S1. Obtain the pixel value curve.

[0028] The pixel value curve is formed by a row or column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layered transparent test object by a spectral confocal sensor, or a light stripe image reflected from a multi-layered transparent test object in online structured light measurement.

[0029] S2. Based on a preset calculation formula, update the pixel values ​​of each pixel in the pixel value curve to obtain the sharpened pixel value curve.

[0030] The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

[0031] Based on the above processing, this application achieves pixel value sharpening in the pixel value curve by subtracting λ times the absolute value of the first-order gradient from the original pixel value. This not only reduces the peak radius, achieving the sharpening effect, but also avoids increasing the pixel value at the center of the light stripe, preventing signal center saturation and improving the accuracy of peak value calculation results.

[0032] Regarding step S1, the dispersive image obtained by the spectral confocal sensor during the detection of the transparent test object can be obtained by projecting a point spectrum or a line spectrum onto the transparent test object and then reflecting it. No specific limitation is made here, but it can be referred to the description in Chinese Patent CN116989664A.

[0033] In some embodiments, step S1 includes the following: S101. Obtain a column or a row of pixels from the detection image. The selection of the pixel column or row is determined based on the dispersion direction in the dispersion image and the distribution direction of the light stripes in the light stripe image.

[0034] S102. Remove pixels below the image background threshold from the column or row of pixels, and perform filtering and denoising processing to obtain the pixel value curve. Pixels below the image background threshold in the column or row of pixels are directly discarded. In this application, all pixels within the pixel value curve are the remaining pixels after removing pixels below the image background threshold and filtering and denoising.

[0035] Regarding step S101, when acquiring a dispersive image at the CCD receiver of the spectral confocal displacement sensor, a row or column of pixels parallel to the dispersion direction is selected to form a pixel value curve. Taking Chinese Patent CN116989664A as an example, its dispersion direction coincides laterally with the CCD at the sensor end; therefore, a row of pixels is selected as the pixel value curve for this application. For light bar images, a column or row of pixels parallel to the cross-section of the light bar is selected to form a pixel value curve, as shown in Chinese Patent CN118587272A. The lines are distributed along the row direction of the original image data, and a column of pixels is selected to extract the line center.

[0036] In most image processing software and programming libraries, image data is stored in the form of a two-dimensional array, corresponding to an image data matrix. To reduce the impact of noise on the pixel value curve, referring to CN116989664A, the image data matrix can be denoised. The denoised image data is then filtered, and the row of data in the image data matrix that is closest to the effective peak curve is selected as the pixel value curve of this application.

[0037] Alternatively, for the detected image, a noise threshold can be set. Pixels exceeding the threshold retain their original pixel values, while pixels below the threshold are set to 0, thus eliminating image noise interference. Furthermore, filtering methods can be used for noise reduction, including median filtering, low-pass filtering, etc., which are not limited here.

[0038] Furthermore, if the number of remaining pixels after removing pixels below the image background threshold is insufficient to meet the requirements for subsequent peak calculation, it indicates that an error occurred during the acquisition of the detection image, and the detection image needs to be replaced or reacquired.

[0039] In some embodiments, step S2 includes the following: S201. Calculate the absolute value of the first-order gradient value of each pixel in the pixel value curve.

[0040] S202. Determine whether the first-order gradient value is lower than a first preset threshold; if yes, set the first-order gradient value to 0; if no, keep the first-order gradient value unchanged.

[0041] S203. Based on a preset calculation formula, update the pixel values ​​of each pixel in the pixel value curve.

[0042] S204. Use the updated pixel value curve as the sharpening curve.

[0043] For step S201, the gradient value is calculated as follows: d i =P 1（i+1） -P 1（i-1） Where i represents the sequence number of the pixel in the pixel value curve, 2≤i≤n-1, and n represents the total number of pixels in the pixel value curve; d i P represents the gradient value of the i-th pixel in the pixel value curve; 1（i） P1 represents the pixel value of the i-th pixel in the pixel value curve.

[0044] Assuming the pixel value curve corresponds to a column of pixels in the detected image, the first-order gradient value can be calculated as follows: .

[0045] It should be noted that, in the aforementioned gradient value calculation method, since the gradient value cannot be calculated for the first and last pixels, the grayscale values ​​of the first and last pixels remain unchanged in the curve sharpening scheme provided in this application and are directly used as the grayscale values ​​after sharpening.

[0046] In some embodiments, for the first-order gradient value Alternatively, this application can also perform curve fitting on the pixel value curve, and then calculate the first-order gradient value of each pixel by taking the first derivative of the function obtained by curve fitting.

[0047] For step S202, when the absolute value When the value is less than the first preset threshold, the absolute value of the gradient is set to 0, thereby avoiding the absolute value caused by noise and thus not performing sharpening processing.

[0048] For the preset calculation formula, the preset multiplier λ is not lower than 0, and the preferred value range is [0, 3], usually set to 1.

[0049] If the value of λ is in the range of [0, 3] and the precision is 0.1, the change of the line center position with the parameter is shown in Table 1 below.

[0050] Table 1 Peak Positioning Table Center position of line 326.54 326.57 326.59 326.60 326.61 326.61 326.58 Peak grayscale 253 249 245 241 237 233 229 In the curve sharpening scheme provided in this application, when the sharpening degree is high (e.g., when the multiplier λ is too large), the sharpened curve may exhibit overshoot. Figure 4 For example, an image of light stripes formed by double-layer reflection may be separated into three layers of signals after sharpening, such as... Figure 4 A third layer of signals appeared below.

[0051] To further avoid overshoot, the curve sharpening method provided in this application also includes the following after step S202: S205. Determine whether the absolute value exceeds a second preset threshold; if yes, set the absolute value to the second preset threshold; if no, keep the absolute value unchanged. The second preset threshold is not lower than the first preset threshold.

[0052] Regarding step S205, the curve sharpening scheme provided in this application sets a second preset threshold as the maximum threshold of the absolute value of the first-order gradient. When the value is greater than the second preset threshold, it is truncated to the second preset threshold.

[0053] Based on the foregoing processing, this application restricts... Too high a value prevents the sharpened pixel value from being too low, thus avoiding overshoot.

[0054] Alternatively, prior to step S203, this application may also include the following: S206. Determine whether the updated pixel value is lower than the third preset threshold; if yes, set the updated pixel value to the third preset threshold; if no, keep the updated pixel value unchanged.

[0055] In step S206, a grayscale value threshold (i.e., a third preset threshold) is set. When the sharpened pixel value is less than this grayscale value threshold, it is directly set to this grayscale value threshold. This prevents the sharpened pixel value from being too low, thereby avoiding overshoot.

[0056] In some embodiments, this application may also avoid overshoot by adjusting a preset multiple λ.

[0057] Specifically, for the preset calculation formula, the curve sharpening method further includes: The value of the multiplier λ is based on the absolute value of the corresponding pixel. It decreases as it increases.

[0058] Based on the foregoing, this application does not use a globally fixed λ, but rather uses a λ based on the pixel. The λ value is dynamically adjusted to match the sharpening intensity with the degree of signal change, based on the absolute value of the corresponding pixel. It decreases as it increases.

[0059] Wherein, the multiple λ increases with the absolute value The function that increases and decreases can be a linear decay function, an exponential decay function, or an S-shaped decay function, etc., and there are no specific restrictions here.

[0060] Based on the aforementioned processing, this application automatically reduces λ in edge regions with large gradient values ​​to avoid excessive compression at the edges; while in flat regions with small gradient values, λ remains at a relatively high level, still achieving the effect of narrowing the peak width. This preserves sufficient signal amplitude at the edges, preventing excessive compression on both sides from causing a relative bulge in the central region and forming a split peak.

[0061] In some embodiments, to avoid overshoot, the curve sharpening method further includes, for the preset calculation formula: The value of the multiplier λ increases as the pixel value P1 of the corresponding pixel increases.

[0062] Taking a pixel depth of 8 bits as an example, the formula for calculating the multiple λ is as follows: The range of the multiplier λ is limited to 0 to 1, and the multiplier λ decreases as the pixel value decreases, which avoids further reduction of the pixel value after sharpening and can reduce the probability of overshoot.

[0063] In some embodiments, this application also provides a curve sharpening system for peak extraction, such as... Figure 5 As shown, the system includes: The curve acquisition module 501 is used to acquire pixel value curves; wherein, the pixel value curves are formed by a row or a column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layer transparent test object acquired by a spectral confocal sensor, or a light stripe image reflected from a multi-layer transparent test object in online structured light measurement.

[0064] The pixel value update module 502 is used to update the pixel value of each pixel in the pixel value curve based on a preset calculation formula, so as to obtain the sharpened pixel value curve.

[0065] The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

[0066] This application also provides an electronic device, such as... Figure 6 As shown, it includes a processor 601, a communication interface 602, a memory 603, and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0067] Memory 603 is used to store computer programs; The processor 601, when executing the program stored in the memory 603, implements any of the above curve sharpening methods.

[0068] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0069] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0070] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0071] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0072] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements any of the curve sharpening method steps described above.

[0073] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the curve sharpening method steps in the above embodiments.

[0074] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, 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. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A curve sharpening method for peak extraction, characterized in that, include: Obtain a pixel value curve; wherein, the pixel value curve is formed by a row or column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layer transparent object by a spectral confocal sensor, or a light stripe image reflected from a multi-layer transparent object in online structured light measurement; Based on a preset calculation formula, the pixel values ​​of each pixel in the pixel value curve are updated to form the sharpened pixel value curve. The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

2. The curve sharpening method according to claim 1, characterized in that, The obtained pixel value curve includes: Obtain a column or a row of pixels from the detection image; the selection of the pixel column or pixel row is determined based on the dispersion direction in the dispersion image and the distribution direction of the light stripes in the light stripe image. Remove pixels below the image background threshold from the column or row of pixels, and perform filtering and noise reduction processing to obtain the pixel value curve.

3. The curve sharpening method according to claim 1, characterized in that, The step of updating the pixel values ​​of each pixel in the pixel value curve based on a preset calculation formula, as a sharpening curve, includes: Calculate the absolute value of the first-order gradient value for each pixel in the pixel value curve; Determine whether the absolute value is lower than a first preset threshold; if yes, set the absolute value to 0; if no, keep the absolute value unchanged. Based on a preset calculation formula, the pixel values ​​of each pixel in the pixel value curve are updated; The updated pixel value curve is used as the sharpening curve.

4. The curve sharpening method according to claim 3, characterized in that, After calculating the absolute value of the first-order gradient value of each pixel in the pixel value curve, if the absolute value is not lower than a first preset threshold, the curve sharpening method further includes: Determine whether the absolute value exceeds a second preset threshold; if yes, set the absolute value to the second preset threshold; if no, keep the absolute value unchanged. The second preset threshold is not lower than the first preset threshold.

5. The curve sharpening method according to claim 3, characterized in that, Before using the updated pixel value curve as the sharpening curve, the curve sharpening method further includes: Determine whether the updated pixel value is lower than the third preset threshold; if yes, set the updated pixel value to the third preset threshold; otherwise, keep the updated pixel value unchanged.

6. The curve sharpening method according to claim 1, characterized in that, For the preset calculation formula, the curve sharpening method further includes: The value of the multiplier λ is based on the absolute value of the corresponding pixel. It decreases as it increases.

7. The curve sharpening method according to claim 1, characterized in that, For the preset calculation formula, the curve sharpening method further includes: The value of the multiplier λ is increased according to the pixel value P1 of the corresponding pixel point according to a preset calculation formula. The curve sharpening method also includes increasing the multiplier λ.

8. A curve sharpening system for peak extraction, characterized in that, The system includes: The curve acquisition module is used to acquire pixel value curves; wherein, the pixel value curves are formed by a row or a column of pixels in the detection image; the detection image represents a dispersive image reflected from a multi-layer transparent test object acquired by a spectral confocal sensor, or a light stripe image reflected from a multi-layer transparent test object in online structured light measurement; The pixel value update module is used to update the pixel value of each pixel in the pixel value curve based on a preset calculation formula, so as to obtain the sharpened pixel value curve. The preset calculation formula is as follows: P2 represents the pixel value of a pixel in the sharpening curve, and P1 represents the pixel value of a pixel in the original pixel value curve. This represents the first-order gradient value of a pixel in the original pixel value curve; λ represents the absolute value of the first-order gradient; λ represents a preset multiple and is not less than 0.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the curve sharpening method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, implements the curve sharpening method according to any one of claims 1-7.

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