Image enhancement method and image enhancement device

Abstract

This application provides an image enhancement method and an image enhancement apparatus that enhance an image and improve its display effect without changing the image noise. The method includes: removing the original noise of a target image to obtain a denoised image; enhancing the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image; and obtaining a target enhanced image based on the target noise and the initial enhanced image; wherein the target noise may be the same as or different from the original noise.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image enhancement method and an image enhancement apparatus. Background Technology

[0002] In the field of image processing, images or videos captured by cameras or downloaded from the internet often exhibit similar brightness levels in dark and bright scenes due to the shooting environment, resulting in weak contrast and affecting the display quality. Currently, local contrast enhancement techniques can be used to improve image contrast and detail in images or videos, thereby enhancing their display performance.

[0003] However, while the above methods enhance the image, they also amplify the noise intensity, making the noise more prominent in the image and resulting in poor image display and a bad user experience. Summary of the Invention

[0004] This application provides an image enhancement method and an image enhancement apparatus that enhance the image and improve its display effect without changing the image noise.

[0005] In a first aspect, an image enhancement method is provided, the method comprising: removing original noise from a target image to obtain a denoised image; enhancing the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image; and obtaining a target enhanced image based on the target noise and the initial enhanced image; wherein the target noise is the same as or different from the original noise.

[0006] In this application, noise can be temporarily removed before image enhancement, so that contrast enhancement only applies to pixels other than those with noise. In other words, this application can enhance image contrast and improve image display quality without changing image noise.

[0007] Secondly, an image enhancement apparatus is provided, comprising: a denoising module and a processing module. The denoising module is used to remove original noise from a target image to obtain a denoised image; the processing module is used to enhance the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image; and to obtain a target enhanced image based on the target noise and the initial enhanced image; wherein the target noise is the same as or different from the original noise.

[0008] Thirdly, another image enhancement apparatus is provided, comprising a processor coupled to a memory for executing instructions in the memory to implement the method in any of the possible implementations of the first aspect described above. Optionally, the image enhancement apparatus further includes a memory. Optionally, the image enhancement apparatus further includes a communication interface, to which the processor is coupled.

[0009] Fourthly, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is used to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute the method in any possible implementation of the first aspect described above.

[0010] In specific implementation, the processor can be a chip, the input circuit can be an input pin, the output circuit can be an output pin, and the processing circuit can be a transistor, gate circuit, flip-flop, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be output to, for example, but not limited to, a transmitter and transmitted by the transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0011] Fifthly, a processing apparatus is provided, including a processor and a memory. The processor is used to read instructions stored in the memory and to receive signals via a receiver and transmit signals via a transmitter to execute the method in any of the possible implementations of the first aspect described above.

[0012] Optionally, there may be one or more processors and one or more memories.

[0013] Alternatively, the memory can be integrated with the processor, or the memory can be set up separately from the processor.

[0014] In specific implementation, the memory can be a non-transitory memory, such as read-only memory (ROM), which can be integrated with the processor on the same chip or set on different chips. The embodiments of this application do not limit the type of memory or the way the memory and processor are set.

[0015] It should be understood that the relevant data interaction process, such as sending indication information, can be the process of outputting indication information from the processor, and receiving capability information can be the process of the processor receiving input capability information. Specifically, the processed output data can be output to the transmitter, and the input data received by the processor can come from the receiver. Here, the transmitter and receiver can be collectively referred to as a transceiver.

[0016] The processing device in the fifth aspect above can be a chip. The processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0017] In a sixth aspect, a computer program product is provided, comprising: a computer program (also referred to as code or instructions) that, when executed, causes a computer to perform the method in any possible implementation of the first aspect.

[0018] In a seventh aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when executed on a computer, causes the computer to perform the methods in any of the possible implementations of the first aspect described above. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an example of an image enhancement method provided in an embodiment of this application;

[0020] Figure 2 This is a flowchart of a first specific example of the image enhancement method provided in the embodiments of this application;

[0021] Figure 3 This is a flowchart of a second specific example of the image enhancement method provided in the embodiments of this application;

[0022] Figure 4 This is a flowchart of a third specific example of the image enhancement method provided in the embodiments of this application;

[0023] Figure 5 This is a structural block diagram of an example of the image enhancement device provided in the embodiments of this application;

[0024] Figure 6 This is a structural block diagram of another example of the image enhancement device provided in the embodiments of this application. Detailed Implementation

[0025] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0026] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. For example, "first instruction" and "second instruction" are used to distinguish different user instructions and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0027] It should be noted that, in this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0028] Furthermore, "at least one" refers to one or more, while "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can mean: a, or b, or c, or a and b, or a and c, or b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0029] In the field of image processing, images or videos captured by cameras or downloaded from the internet often exhibit similar brightness levels in dark and bright scenes due to the shooting environment, resulting in weak contrast and affecting the display quality. Currently, local contrast enhancement techniques can be used to improve image contrast and detail in images or videos, thereby enhancing their display performance.

[0030] However, while the above methods enhance the image, they also amplify the noise intensity, making the noise more prominent in the image and resulting in poor image display and a poor user experience.

[0031] Therefore, embodiments of this application provide an image enhancement method and an image enhancement apparatus. Before enhancing the image, noise is temporarily removed, so that contrast enhancement only applies to pixels other than noisy pixels. In other words, this application can enhance the contrast of an image and improve its display effect without changing the image noise.

[0032] To make the objectives and technical solutions of this application clearer and more intuitive, the image enhancement method and image enhancement apparatus provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0033] Figure 1 This is a schematic flowchart of an image enhancement method 100 provided in an embodiment of this application. It should be understood that this method 100 can be executed by a device with image enhancement capabilities, referred to herein as an image processing device. Figure 1 As shown, the method 100 may include the following steps:

[0034] S101, the image processing device removes the original noise from the target image to obtain a denoised image.

[0035] In one possible implementation, the image processing device can perform denoising on the target image in the HSV color model format (hue, saturation, value, HSV).

[0036] S102, the image processing device enhances the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image.

[0037] In one possible implementation, the image processing device can obtain the distribution of pixel values ​​for each pixel based on the histogram, and then enhance the contrast of the denoised image based on the histogram equalization result.

[0038] S103, the image processing device obtains a target enhanced image based on the target noise and the aforementioned initial enhanced image. The target noise may be the same as or different from the aforementioned original noise.

[0039] It should be understood that the image processing device can obtain the above-mentioned target enhancement image based on different noise levels, according to user needs.

[0040] For example, if the user does not want noise to be displayed, the target noise is different from the original noise, and the target noise does not include any noise pixels.

[0041] For example, if a user wants to enhance an image while keeping the noise unchanged, the target noise can be the same as the original noise.

[0042] In this application, the image processing device temporarily removes noise before enhancing the image, so that the contrast enhancement only applies to pixels other than the noisy pixels. In other words, this application can enhance the contrast of an image and improve its display effect without changing the image noise.

[0043] Optionally, to further improve the display effect of the image, the image processing device may also obtain the target-enhanced image based on noise (target noise) that is not exactly the same as the original noise.

[0044] In one possible scenario, the number of noise pixels included in the target noise may differ from the number of noise pixels included in the original noise.

[0045] Figure 2 This is a schematic flowchart illustrating an image enhancement method 200 provided in an embodiment of this application. Figure 2 As shown, the method 200 includes the following steps:

[0046] S201, The image processing device removes the original noise from the target image to obtain a denoised image.

[0047] In one possible implementation, the image processing device can obtain the distribution of pixel values ​​for each pixel based on the histogram, and then enhance the contrast of the denoised image based on the histogram equalization result.

[0048] It should be understood that the aforementioned original noise includes at least one of the following: Gaussian noise, Poisson noise, multiplicative noise, or salt and pepper noise.

[0049] Optionally, before denoising, the image processing device may also determine the noise in the target image and select at least one type of noise to remove.

[0050] It should be understood that, in addition to denoising in the HSV format described above, the image processing device can also perform the above denoising operation in RGB color mode (red, green, blue, RGB) or YUV format, and this application does not limit this.

[0051] S202, the image processing device enhances the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image.

[0052] In one possible implementation, the image processing device can obtain the distribution of pixel values ​​for each pixel based on the histogram, and then enhance the contrast of the denoised image based on the histogram equalization result.

[0053] S203, the image processing device determines whether there are any noise pixels in the target area among the original noise.

[0054] It should be understood that the target region includes the edge region or flat region in the aforementioned target enhancement image. The edge region may indicate the intersection area between different objects in the image, or the outline of an object, etc. The flat region may indicate the area where an object is located in the image, etc.

[0055] It should also be understood that, in addition to the edge areas and flat areas shown above, the target area can also be other areas, and there is no limitation on the area that can be touched.

[0056] For example, the image processing device can determine the region in the target image where each noise pixel in the original noise is located, and determine the region in the initial enhanced image based on the region in the target image, and then determine whether the region where each noise pixel is located is the target region.

[0057] S204, if it is determined that there are noise pixels in the target region in the original noise, the image processing device determines the target noise, which includes the noise pixels in the target region in the original noise.

[0058] S205, the image processing device obtains the target enhancement image based on the noise pixels in the target noise and the initial enhancement image.

[0059] For example, the target image includes a person, and the target area is the outline area of ​​the person, that is, the edge area of ​​the image. In order to avoid the problem that the image display effect is affected by the missing pixels at the outline of the person, the image processing device can obtain the target enhancement image based on the noise pixels (target noise) in the outline area and the initial enhancement image.

[0060] Optionally, following S203 above, if it is determined that there are no noise pixels in the target region in the original noise, the image processing device may execute S206 to determine the initial enhanced image as the target enhanced image. In other words, since all the noise pixels in the original noise are located in the non-target region, it indicates that the original noise has little impact on the image display effect. The image processing device may not add it to the initial enhanced image and may use the initial enhanced image as the target enhanced image, thus enhancing the image display effect while ensuring the image display effect.

[0061] In another possible scenario, the pixel values ​​of the noise pixels included in the target noise may also be different from the pixel values ​​of the noise pixels included in the original noise, or both the pixel values ​​and the number of noise pixels included in the target noise may be different from the pixel values ​​and the number of noise pixels included in the original noise.

[0062] The image enhancement method provided in this application is described below using the example where the pixel values ​​and number of noise pixels in the target noise are different from those in the original noise.

[0063] It should be understood that image processing devices can make the pixel values ​​of noise pixels in the target noise different from the pixel values ​​of noise pixels in the original noise based on noise adjustment parameters. In other words, noise adjustment parameters can be used to adjust the pixel values ​​of the aforementioned noise pixels.

[0064] In one possible implementation, the noise adjustment parameters mentioned above can be determined by the image processing device based on the target region.

[0065] Figure 3 This is a schematic flowchart illustrating an image enhancement method 300 provided in an embodiment of this application. Figure 3 As shown, the method 300 includes the following steps:

[0066] S301, The image processing device removes the original noise from the target image to obtain a denoised image.

[0067] In one possible implementation, the target image format is converted to HSV format; the original noise in the HSV format target image is filtered out to obtain the denoised image.

[0068] It should be understood that the aforementioned original noise includes at least one of the following: Gaussian noise, Poisson noise, multiplicative noise, or salt and pepper noise.

[0069] Optionally, before denoising, the image processing device may also determine the noise in the target image and select at least one type of noise to remove.

[0070] It should be understood that, in addition to denoising in the HSV format, the image processing device can also perform the above denoising operation in RGB or YUV formats, and this application does not limit this.

[0071] S302, the image processing device enhances the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image.

[0072] In one possible implementation, the image processing device can obtain the histogram equalization result of the pixel value of each pixel in the denoised image, wherein the histogram equalization result indicates the enhanced pixel value of each pixel in the denoised image, and the image processing device can enhance the contrast of the denoised image based on the histogram equalization result to obtain the target enhanced image.

[0073] S303, the image processing device determines whether there are any noise pixels in the target area among the original noise.

[0074] It should be understood that the aforementioned target region includes edge regions or flat regions in the target enhancement image, and this application does not limit this.

[0075] For example, the image processing device can determine the region in the target image where each noise pixel in the original noise is located, and determine the region in the initial enhanced image based on the region in the target image, and then determine whether the region where each noise pixel is located is the target region.

[0076] S304, if it is determined that there are noise pixels in the target region in the original noise, the image processing device determines noise adjustment parameters based on the target region.

[0077] In one possible implementation, the image processing device can determine the aforementioned noise adjustment parameters based on reference data and the brightness of the target area.

[0078] Table 1 shows the reference data mentioned above.

[0079] Area brightness Noise adjustment parameters Highlight Third parameter Low brightness Fourth parameter

[0080] As shown in Table 1, the noise adjustment parameter is the third adjustment parameter when the area brightness is high, and the noise adjustment parameter is the fourth adjustment parameter when the area brightness is low.

[0081] It should be understood that the noise adjustment parameters corresponding to the brightness of the different areas mentioned above can be determined by the following formula.

[0082]

[0083] Where x is the noise adjustment parameter mentioned above, coeff is the adjustment intensity, luma is the area brightness, and A, B...Y,Z are areas with different brightness.

[0084] It should also be understood that since noise is less noticeable in bright areas than in low-brightness areas, to ensure image enhancement while maintaining overall image display quality, the image processing device can adjust the noise level according to user needs. This adjustment can be greater than or equal to the adjustment level in low-brightness areas; that is, the third parameter can be less than or equal to the fourth parameter. In other words, the smaller the noise adjustment parameter, the less noise is added back.

[0085] For example, when the target area is determined to be highlighted, the image processing device can determine the noise adjustment parameter as a third parameter, such as the third parameter being greater than 0, less than or equal to 1.

[0086] For example, when the target area is determined to be low-brightness, the image processing device can determine the noise adjustment parameter as a fourth parameter, such that the fourth parameter can be greater than or equal to the third parameter, or less than or equal to 1.

[0087] In another possible implementation, the image processing device can also determine the aforementioned noise adjustment parameters based on reference data and the scene of the target area.

[0088] Table 2 shows another set of reference data.

[0089] Regional Scene Noise adjustment parameters at night Fifth parameter daytime Sixth parameter

[0090] As shown in Table 2, the noise adjustment parameter is the fifth adjustment parameter when the area scene is at night, and the noise adjustment parameter is the sixth adjustment parameter when the area scene is at night.

[0091] It should be understood that the noise adjustment parameters corresponding to the above-mentioned nighttime or daytime scenes can also be determined by the above formula. For specific details, please refer to the above embodiments. To avoid repetition, they will not be repeated here.

[0092] It should also be understood that since noise is less perceptible in daytime scenes than in nighttime scenes, in order to ensure image enhancement while maintaining the overall display effect of the image, the image processing device can adjust the noise according to the user's needs, and the adjustment can be greater than or equal to the adjustment in nighttime scenes, that is, the sixth parameter can be less than or equal to the fifth parameter.

[0093] For example, when the scene corresponding to the target area is determined to be nighttime, the image processing device determines the noise adjustment parameter as the fifth parameter, such that the fifth parameter can be greater than 0, less than or equal to 1.

[0094] For example, when the scene corresponding to the target area is determined to be daytime, the image processing device determines the noise adjustment parameter as the sixth parameter, such that the sixth parameter can be greater than 0 and less than or equal to the fifth parameter.

[0095] S305, the image processing device determines the target noise based on the noise pixels in the target area and the noise adjustment parameters.

[0096] For example, the target noise in the above embodiments is determined by the following formula Y = αX. Wherein, Y is the target noise, α is the noise adjustment parameter, and X is the original noise.

[0097] It should be understood that Y can also be understood as the pixel value of any noise pixel of the target noise, α as the noise adjustment parameter, and X as the pixel value of any noise pixel of the original noise. In this application, Y is collectively referred to as target noise and X is collectively referred to as original noise.

[0098] It should also be understood that the target noise includes noise pixels in the original noise that are located in the target region, and the pixel value of the noise pixel is different from the noise pixel value of the corresponding pixel in the original noise.

[0099] S306, the image processing device obtains a target enhancement image based on the noise pixels in the target noise and the initial enhancement image.

[0100] For example, the target image includes a person, and the target area is the outline area of ​​the person, that is, the edge area of ​​the image. In order to avoid the noise pixels in the outline area being too prominent and affecting the overall display effect of the image, the image processing device can adjust the pixel value of the noise pixels in the outline area based on the noise adjustment parameters, and then obtain the target enhancement image based on the adjusted noise pixels (target noise) and the initial enhancement image.

[0101] Optionally, following S303 above, if it is determined that there are no noise pixels in the target region in the original noise, the image processing device may execute S307 to determine the initial enhanced image as the target enhanced image.

[0102] In another possible implementation, the noise adjustment parameters can be determined by the image processing device based on the target region and the noise pixels in the target region from the original noise.

[0103] Figure 4 This is a schematic flowchart illustrating an image enhancement method 400 provided in an embodiment of this application. Figure 4 As shown, the method 400 includes the following steps:

[0104] S401, The image processing device removes the original noise from the target image to obtain a denoised image.

[0105] In one possible implementation, the target image format is converted to HSV format; the original noise in the HSV format target image is filtered out to obtain the denoised image.

[0106] It should be understood that the aforementioned original noise includes at least one of the following: Gaussian noise, Poisson noise, multiplicative noise, or salt and pepper noise.

[0107] Optionally, before denoising, the image processing device may also determine the noise in the target image and select at least one type of noise to remove.

[0108] S402, the image processing device enhances the contrast of the denoised image based on the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image.

[0109] In one possible implementation, a histogram equalization result of the pixel value of each pixel in the denoised image is obtained, wherein the histogram equalization result indicates the enhanced pixel value of each pixel in the denoised image; the contrast of the denoised image is enhanced based on the histogram equalization result to obtain the target enhanced image.

[0110] S403, the image processing device determines whether there are any noise pixels in the target area among the original noise.

[0111] It should be understood that the target region includes edge regions or flat regions in the target enhanced image.

[0112] For example, the image processing device can determine the region in the target image where each noise pixel in the original noise is located, and determine the region in the initial enhanced image based on the region in the target image, and then determine whether the region where each noise pixel is located is the target region.

[0113] S404, if it is determined that there are noise pixels in the target region in the original noise, the image processing device determines noise adjustment parameters based on the target region and the noise pixels in the target region in the original noise.

[0114] In one possible implementation, the image processing device can determine noise adjustment parameters based on reference data, the maximum and minimum values ​​of pixels in the target region, and the pixel values ​​of noise pixels in the target region from the original noise.

[0115] Table 3 shows another set of reference data.

[0116] Maximum and minimum values ​​of pixels in the target region Noise adjustment parameters First threshold First parameter Second threshold Second parameter

[0117] Table 3 shows the maximum and minimum values ​​of pixels in the target region and their corresponding noise adjustment parameters. Specifically, if the pixel value of a noise pixel is greater than the maximum value of pixels in the target region (i.e., the first threshold), the noise adjustment parameter is the first parameter; and if the pixel value of a noise pixel is less than the minimum value of pixels in the target region (i.e., the second threshold), the noise adjustment parameter is the second parameter.

[0118] It should be understood that, since the first threshold is greater than or equal to the second threshold, in order to ensure the overall display effect of the image, the first parameter is used to lower the pixel value of the noise pixel, and the second parameter is used to raise the pixel value of the noise pixel. That is, the first parameter can be less than or equal to the second parameter.

[0119] For example, if the pixel value of the noise pixel in the target area is determined to be a first pixel value and the first pixel value is greater than a first threshold of the target area, the image processing device can determine the noise adjustment parameter as a first parameter to lower the pixel value of the noise pixel and avoid the noise from affecting the overall display effect of the image.

[0120] For example, if the pixel value of the noise pixel in the target area is determined to be a second pixel value, and the pixel value is less than a second threshold of the target area, the image processing device can determine the noise adjustment parameter as a second parameter to increase the pixel value of the noise pixel, thereby improving the display effect of the image.

[0121] S405, the image processing device determines the target noise based on the noise pixels in the target area and the noise adjustment parameters.

[0122] For example, the image processing device can determine the target noise mentioned above using the following formula Y = αX.

[0123] It should be understood that Y can be interpreted as the pixel value of any noise pixel in the target noise, α can be interpreted as the noise adjustment parameter, and X can be interpreted as the pixel value of any noise pixel in the original noise. In this application, Y is collectively referred to as target noise, and X is collectively referred to as original noise.

[0124] It should also be understood that the target noise includes noise pixels in the original noise that are located in the target region, and the pixel value of the noise pixel is different from the pixel value of the corresponding noise pixel in the original noise.

[0125] S406, the image processing device obtains a target enhancement image based on the noise pixels included in the target noise and the initial enhancement image.

[0126] Similarly, if the target image includes a person, the target area is the outline area of ​​the person, i.e. the edge area of ​​the image. In order to avoid the noise pixels in the outline area being too prominent and affecting the overall display effect of the image, the image processing device can adjust the pixel value of the noise pixels in the outline area based on the noise adjustment parameters, and then obtain the target enhancement image based on the adjusted noise pixels (target noise) and the initial enhancement image.

[0127] Optionally, following S403 above, if it is determined that there are no noise pixels in the target region in the original noise, the image processing device may execute S407 to determine the initial enhanced image as the target enhanced image.

[0128] It should be understood that the above embodiments mainly use enhancing image contrast as an example to describe the image enhancement method provided by this application. In addition, this application can also enhance other image display parameters besides contrast, and this application does not limit this.

[0129] It should also be understood that the various embodiments described above can be coupled to each other, and this application does not limit this. Furthermore, the sequence number of each process does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0130] The above text combines Figures 1 to 4 The image enhancement method of the embodiments of this application is described in detail below, and will be combined with Figures 5 to 6 This application describes in detail the image enhancement apparatus according to embodiments of the present application.

[0131] Figure 5 An image enhancement apparatus 500 according to an embodiment of this application is shown. The apparatus 500 includes a noise reduction module 501 and a processing module 502.

[0132] The denoising module 501 is used to: remove the original noise of the target image to obtain a denoised image; the processing module 502 is used to: enhance the contrast of the denoised image according to the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image; and to obtain a target enhanced image according to the target noise and the initial enhanced image; wherein the target noise is the same as or different from the original noise.

[0133] Optionally, the processing module 502 is configured to: determine whether there are noise pixels in the original noise that are located in the target region, wherein the target region includes the edge region or flat region in the target enhanced image; if it is determined that there are noise pixels in the target region in the original noise, determine the target noise, wherein the target noise includes the noise pixels located in the target region; and obtain the target enhanced image based on the noise pixels in the target noise region and the initial enhanced image.

[0134] Optionally, the processing module 502 is used to: determine the noise adjustment parameters based on the target region; determine the target noise based on the noise pixels in the target region and the noise adjustment parameters, wherein the noise adjustment parameters are used to adjust the pixel values ​​of the noise pixels.

[0135] Optionally, the processing module 502 is configured to, when the target area is determined to be bright, determine the noise adjustment parameter as a third parameter; or, when the target area is determined to be dim, determine the noise adjustment parameter as a fourth parameter, wherein the fourth parameter is less than or equal to the third parameter.

[0136] Optionally, the processing module 502 is configured to determine the noise adjustment parameter as the fifth parameter when the scene corresponding to the target area is determined to be nighttime; or, when the scene corresponding to the target area is determined to be daytime, determine the noise adjustment parameter as the sixth parameter, wherein the sixth parameter is greater than or equal to the fifth parameter.

[0137] Optionally, the processing module 502 is configured to: determine the noise adjustment parameters based on the target region and the noise pixels in the target region of the original noise; and determine the target noise based on the noise pixels in the target region and the noise adjustment parameters, wherein the pixel values ​​of the noise pixels included in the target noise are indicated by the noise adjustment parameters.

[0138] Optionally, the processing module 502 is configured to: determine the noise adjustment parameter as a first parameter when the pixel value of the noise pixel in the target region is determined to be greater than a first threshold of the target region; or determine the noise adjustment parameter as a second parameter when the pixel value of the noise pixel in the target region is determined to be less than a second threshold of the target region, wherein the second parameter is greater than or equal to the first parameter; wherein the first threshold is greater than or equal to the second threshold.

[0139] Optionally, the target noise is obtained by the following formula: Y = αX, where Y is the target noise, α is the noise adjustment parameter, and X is the original noise.

[0140] Optionally, the denoising module 501 is used to convert the target image format into an HSV format target image; and to filter out the original noise in the HSV format target image to obtain the denoised image.

[0141] Optionally, the processing module 502 is used to obtain the histogram equalization result of the pixel value of each pixel in the denoised image, wherein the histogram equalization result indicates the enhanced pixel value of each pixel in the denoised image; and enhance the contrast of the denoised image based on the histogram equalization result to obtain the target enhanced image.

[0142] It should be understood that the image enhancement device 500 here is embodied in the form of a functional module. The term "module" here can refer to application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors, etc.) and memories for executing one or more software or firmware programs, integrated logic circuits, and / or other suitable components supporting the described functions. In an alternative example, those skilled in the art will understand that the image enhancement device 500 may be specifically the image processing device in the above embodiments, or the functions of the image processing device in the above embodiments may be integrated into the image enhancement device 500. The image enhancement device 500 may be used to execute the various processes and / or steps corresponding to the image processing device in the above method embodiments; to avoid repetition, these will not be described again here.

[0143] The image enhancement device 500 described above has the function of implementing the corresponding steps performed by the image processing device in the above method; the above function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above function.

[0144] In the embodiments of this application, Figure 5 The image enhancement device 500 in the image enhancement device 500 can also be a chip or a chip system, such as a system on chip (SoC).

[0145] Figure 6 Another image enhancement device 600 provided in this application embodiment is shown. The image enhancement device 600 includes a processor 601, a transceiver 602, and a memory 603. The processor 601, transceiver 602, and memory 603 communicate with each other via an internal connection path. The memory 603 stores instructions, and the processor 601 executes the instructions stored in the memory 603 to control the transceiver 602 to transmit and / or receive signals.

[0146] The processor 601 is used to: remove the original noise from the target image to obtain a denoised image; enhance the contrast of the denoised image according to the distribution of pixel values ​​of each pixel in the denoised image to obtain an initial enhanced image; and obtain a target enhanced image according to the target noise and the initial enhanced image; wherein the target noise is the same as or different from the original noise.

[0147] It should be understood that the image enhancement device 600 may specifically be the image processing device in the above embodiments. The functions of the image processing device in the above embodiments may be integrated into the image enhancement device 600. The image enhancement device 600 may be used to execute the various steps and / or processes corresponding to the image processing device in the above method embodiments.

[0148] Optionally, the memory 603 may include read-only memory and random access memory, and provide instructions and data to the processor 601. A portion of the memory 603 may also include non-volatile random access memory. For example, the memory 603 may also store device type information. The processor 601 can be used to execute instructions stored in the memory, and when the processor executes the instructions, the processor 601 can perform the various steps and / or processes corresponding to the image processing device in the above method embodiments.

[0149] It should be understood that, in the embodiments of this application, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0150] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware processor, or as a combination of hardware and software modules within the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor executes the instructions in the memory, combining them with its hardware to complete the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0154] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0156] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0157] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above claims.

Claims

1. An image enhancement method characterized by, The method comprises the following steps: removing original noise of a target image to obtain a denoised image; enhancing contrast of the denoised image according to a distribution of pixel values of each pixel point in the denoised image to obtain an initial enhanced image; obtaining a target enhanced image according to target noise and the initial enhanced image; wherein the target noise is the same as or different from the original noise; before the step of obtaining the target enhanced image according to the target noise and the initial enhanced image, the method further comprises the following steps: judging whether there is noise pixel point in a target region in the original noise, wherein the target region comprises an edge region or a flat region in the target enhanced image; in a case where it is determined that there is noise pixel point in the target region in the original noise, determining the target noise, wherein the target noise comprises the noise pixel point in the target region; the step of obtaining the target enhanced image according to the target noise and the initial enhanced image comprises the following step: obtaining the target enhanced image according to the noise pixel point in the target noise and the initial enhanced image.

2. The method of claim 1, wherein, before the step of determining the target noise, the method further comprises the following steps: determining a noise adjustment parameter based on the target region; determining the target noise based on the noise pixel point in the target region and the noise adjustment parameter, wherein the noise adjustment parameter is used to adjust the pixel value of the noise pixel point.

3. The method of claim 2, wherein, the step of determining the noise adjustment parameter based on the target region comprises the following steps: in a case where it is determined that the target region is highlighted, determining that the noise adjustment parameter is a third parameter; or in a case where it is determined that the target region is low-lighted, determining that the noise adjustment parameter is a fourth parameter, wherein the fourth parameter is less than or equal to the third parameter.

4. The method of claim 2, wherein, the step of determining the noise adjustment parameter based on the target region comprises the following steps: in a case where it is determined that a scene corresponding to the target region is night, determining that the noise adjustment parameter is a fifth parameter; or in a case where it is determined that the scene corresponding to the target region is day, determining that the noise adjustment parameter is a sixth parameter, wherein the sixth parameter is greater than or equal to the fifth parameter.

5. The method of claim 1, wherein, before the step of determining the target noise, the method further comprises the following steps: determining a noise adjustment parameter based on the target region and the noise pixel point in the target region in the original noise; determining the target noise based on the noise pixel point in the target region and the noise adjustment parameter, wherein a pixel value of the noise pixel point included in the target noise is indicated by the noise adjustment parameter.

6. The method of claim 5, wherein, the step of determining the noise adjustment parameter based on the target region and the noise pixel point in the target region in the original noise comprises the following steps: in a case where it is determined that the pixel value of the noise pixel point in the target region is greater than a first threshold value of the target region, determining that the noise adjustment parameter is a first parameter; or in a case where it is determined that the pixel value of the noise pixel point in the target region is less than a second threshold value of the target region, determining that the noise adjustment parameter is a second parameter, wherein the second parameter is greater than or equal to the first parameter; wherein the first threshold value is greater than or equal to the second threshold value.

7. The method according to any one of claims 2 to 6, characterized in that, The target noise is obtained by the following formula: Y = αX, Y is the target noise, α is the noise adjustment parameter, and X is the original noise.

8. The method of claim 1, wherein, The original noise of the target image is removed to obtain a denoised image, including: Converting the target image into an HSV format target image; Filtering the original noise in the HSV format target image to obtain the denoised image.

9. The method of claim 1, wherein, The contrast of the denoised image is enhanced according to the distribution of the pixel value of each pixel point in the denoised image to obtain an initial enhanced image, including: Obtaining a histogram equalization result of the pixel value of each pixel point in the denoised image, wherein the histogram equalization result indicates the enhanced pixel value of each pixel point in the denoised image; Enhancing the contrast of the denoised image based on the histogram equalization result to obtain the target enhanced image.

10. An image enhancement device, characterized by Including: A denoising module for removing the original noise of the target image to obtain a denoised image; A processing module for enhancing the contrast of the denoised image according to the distribution of the pixel value of each pixel point in the denoised image to obtain an initial enhanced image; And obtaining a target enhanced image according to the target noise and the initial enhanced image; Wherein, the target noise is the same as or different from the original noise; The processing module is also used to determine whether there is a noise pixel point in the target area in the original noise, and the target area includes an edge area or a flat area in the target enhanced image; in the case that it is determined that there is a noise pixel point in the target area in the original noise, the target noise is determined, and the target noise includes the noise pixel point in the target area; The target enhanced image is obtained according to the noise pixel point in the target noise and the initial enhanced image.

11. An image enhancement device, characterized by A processor and a memory are included, the memory is used to store code instructions; the processor is used to run the code instructions to execute the method in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, A computer program is used to store instructions for implementing the method in any one of claims 1 to 9.

13. A computer program product comprising computer program code in said computer program product, characterised in that, When the computer program code runs on the computer, the computer implements the method in any one of claims 1 to 9.

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