Image color noise reduction processing method and system and image signal processor

Abstract

The invention provides an image color noise reduction processing method, an image color noise reduction processing system and an image signal processor, which can be used in the technical field of image processing. The method comprises the following steps: performing color noise reduction processing on a to-be-processed image to obtain a first noise-reduced image; performing forward gamma correction processing on the first noise reduction image to obtain a corrected image; determining whether a three-dimensional time domain noise reduction function is enabled or not; if the three-dimensional time domain noise reduction function is not enabled, performing color noise reduction processing on the corrected image again to obtain a second noise reduction image; and if the three-dimensional time domain noise reduction function is enabled, performing three-dimensional time domain noise reduction processing on the corrected image, and then performing color noise reduction processing again to obtain a second noise reduction image. According to the method provided by the invention, after the primary color noise reduction processing and the forward gamma correction, the secondary color noise reduction processing is performed on the image according to the starting state of the three-dimensional time domain noise reduction function, so that the noise reduction mechanism of image processing is optimized, the color noise reduction effect of the image is improved, and the image quality is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and particularly relates to an image color noise reduction processing method and system and an image signal processor. BACKGROUND

[0002] Image signal processing (ISP) is the core technology of a digital imaging system, which is responsible for gradually optimizing the color, brightness, contrast and definition of an image through a series of modular processing of raw image data captured by an image sensor, and finally outputting a high-quality digital image.

[0003] Among them, color noise reduction (CNR) is a key technology in image processing for color noise, and its purpose is to effectively suppress high-frequency color noise while preserving the edge details and color authenticity of the image.

[0004] However, the existing image signal processing technology has the problem of poor image color noise reduction effect. SUMMARY

[0005] The present application provides an image color noise reduction processing method, system and image signal processor to solve the technical problem of poor image color noise reduction effect in the existing image signal processing technology.

[0006] According to a first aspect of the present application, the present application provides an image color noise reduction processing method, comprising:

[0007] performing color noise reduction processing on a to-be-processed image to obtain a first noise reduction image;

[0008] performing forward gamma correction processing on the first noise reduction image to obtain a corrected image;

[0009] determining whether a three-dimensional time domain noise reduction function is enabled;

[0010] if the three-dimensional time domain noise reduction function is not enabled, performing color noise reduction processing on the corrected image again to obtain a second noise reduction image;

[0011] if the three-dimensional time domain noise reduction function is enabled, performing three-dimensional time domain noise reduction processing on the corrected image, and then performing color noise reduction processing again to obtain a second noise reduction image.

[0012] In a feasible implementation, before performing forward gamma correction processing on the first noise reduction image, the method further comprises:

[0013] performing color space transformation processing on the first noise reduction image.

[0014] In an implementation, before the color space conversion processing on the first denoised image, the method further comprises:

[0015] performing inverse gamma correction processing on the first denoised image.

[0016] In an implementation, after the forward gamma correction processing on the first denoised image, the method further comprises:

[0017] performing color space conversion processing on the corrected image.

[0018] In an implementation, determining whether the three-dimensional time domain denoising function is enabled comprises:

[0019] obtaining an enabling signal of the three-dimensional time domain denoising function;

[0020] if the enabling signal indicates that the three-dimensional time domain denoising function is closed, determining that the three-dimensional time domain denoising function is not enabled;

[0021] if the enabling signal indicates that the three-dimensional time domain denoising function is opened, determining that the three-dimensional time domain denoising function is enabled.

[0022] In an implementation, before the three-dimensional time domain denoising processing on the corrected image, the method further comprises:

[0023] performing two-dimensional time domain denoising processing on the corrected image.

[0024] In an implementation, before the color denoising processing on the to-be-processed image, the method further comprises:

[0025] performing image cropping processing on the to-be-processed image.

[0026] According to a second aspect of the present disclosure, the present disclosure provides an image color denoising processing system, comprising:

[0027] an image denoising module, configured to perform color denoising processing on a to-be-processed image to obtain a first denoised image;

[0028] a gamma correction module, configured to perform forward gamma correction processing on the first denoised image to obtain a corrected image;

[0029] an image denoising module, configured to determine whether a three-dimensional time domain denoising function is enabled;

[0030] if the three-dimensional time domain denoising function is not enabled, performing color denoising processing on the corrected image again to obtain a second denoised image;

[0031] If the three-dimensional time domain noise reduction function is enabled, the corrected image is subjected to three-dimensional time domain noise reduction processing, and then subjected to color noise reduction processing again to obtain a second noise reduction image.

[0032] According to a third aspect of the present disclosure, the present disclosure provides an image signal processor, comprising a processor body and a memory in communication connection with the processor body;

[0033] The memory stores computer execution instructions;

[0034] The processor body executes the computer execution instructions stored in the memory to implement the method of any one of the first aspect.

[0035] According to a fourth aspect of the present disclosure, the present disclosure provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed to implement the method of any one of the first aspect.

[0036] According to a fifth aspect of the present disclosure, the present disclosure provides a computer program product, comprising a computer program, and the computer program is executed to implement the method of any one of the first aspect.

[0037] Compared with the prior art, the present disclosure has the following beneficial effects:

[0038] The image color noise reduction processing method, system and image signal processor provided by the present disclosure link three-dimensional time domain noise reduction and color noise reduction, and after the initial color noise reduction processing and forward gamma correction, the image is subjected to secondary color noise reduction processing according to the enable state of the three-dimensional time domain noise reduction function. When the three-dimensional time domain noise reduction function is not enabled, the chroma noise amplified by the gamma correction is eliminated, and when the three-dimensional time domain noise reduction function is enabled, the residual noise after time domain filtering is suppressed, so that the noise reduction mechanism of image processing is optimized, the color noise reduction effect of the image is improved, and the image quality is further improved. Through this flexible noise reduction strategy, various noises in the image can be removed more accurately, a clearer and more real image is generated, and the user's visual experience is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure.

[0040] Figure 1 A flowchart of an image color noise reduction processing method provided by an embodiment of the present disclosure is shown in the figure;

[0041] Figure 2Another flowchart of an image color noise reduction processing method provided by an embodiment of the present application is shown in FIG. 2.

[0042] Figure 3 A structural diagram of an image color noise reduction processing system provided by an embodiment of the present application is shown in FIG. 3.

[0043] Figure 4 A structural diagram of an image signal processor provided by an embodiment of the present application is shown in FIG. 4.

[0044] The specific embodiments of the present application have been shown in the above-described drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the present application concept in any way, but to illustrate the present application concept to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0045] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same numbers are used in different drawings to represent the same or similar elements. The embodiments described in the following exemplary embodiments are not meant to represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0046] Image Signal Processor (ISP) is the core technology of digital imaging system, which is responsible for gradually optimizing the color, brightness, contrast and clarity of the image through a series of modular processing, such as black level correction, bad pixel repair, lens shading compensation, demosaicing, white balance, color correction, color noise reduction, gamma correction, sharpening and color space conversion, and finally outputting high-quality digital images. It is widely used in mobile devices, security monitoring, autonomous driving and other fields to realize real-time, low-power and high-precision image enhancement and restoration, and has become one of the core technologies supporting the development of modern visual technology.

[0047] Among them, Color Noise Reduction (CNR) is a key technology in image processing for color noise (such as color spots, pseudo-color or color interference in low-light environments). By combining spatial domain filtering (such as bilateral filtering, non-local mean, etc.), temporal filtering (such as 3D noise reduction, etc.) or machine learning algorithms (such as CNN network, etc.), the luminance and chrominance channels are separated in YUV / RGB color space for targeted processing, effectively suppressing high-frequency color noise while preserving image edge details and color authenticity, especially good at improving image purity and visual comfort in low-light scenes. It is an indispensable optimization module in image signal processing.

[0048] However, the existing image signal processing technology has the problem of poor image color noise reduction effect. This is because in the existing image signal processing technology, after the color noise reduction processing of the image, the image may be subjected to gamma correction or three-dimensional time domain noise reduction processing, and in these subsequent processing processes, artifacts or residual noise may be introduced, thereby affecting the color noise reduction effect of the image, resulting in poor image color noise reduction effect.

[0049] To solve the above technical problems, the present application provides an image color noise reduction processing method, system and image signal processor. After the initial color noise reduction processing and forward gamma correction, the image is subjected to secondary color noise reduction processing according to the enable state of the three-dimensional time domain noise reduction function, thereby optimizing the noise reduction mechanism of image processing, improving the color noise reduction effect of the image, and further improving the image quality.

[0050] The technical scheme of the image color noise reduction processing method provided by the present application will be described in detail below through specific embodiments. It should be noted that the following embodiments can exist independently or be combined with each other. For the same or similar content, it may not be repeated in different embodiments.

[0051] Figure 1 A flowchart of an image color noise reduction processing method provided by an embodiment of the present application is shown in Figure 1 In some embodiments, the flow of the image color noise reduction processing method includes the following steps:

[0052] S101, performing color noise reduction processing on the image to be processed to obtain a first noise reduction image.

[0053] Among them, by reducing the color noise of the image to be processed, the influence of the color noise on the visual effect of the image is reduced, thereby improving the image quality.

[0054] Specifically, the color noise reduction technology can identify and reduce color noise through related algorithms, such as filter noise reduction algorithm, using spatial domain or frequency domain filter to smooth color changes in images or videos, thereby reducing noise; statistical noise reduction algorithm, using statistical information in images or videos to distinguish noise and real signals, and then removing noise; machine learning noise reduction algorithm, using deep learning model to identify and remove color noise.

[0055] For example, taking the filter noise reduction algorithm, first convert the input RGB image to YUV color space for processing the luminance (Y) and chrominance (U, V) components respectively. Then calculate the chrominance mean value of the surrounding pixels for each pixel's chrominance component (U, V), and mix the current pixel's chrominance value with the surrounding chrominance mean value (mixing) with a weight mixing), weight It can be dynamically adjusted according to the noise level or edge information. If the chroma value of the current pixel is greatly different from the surrounding average value (may be noise), the surrounding average value is adopted. If the difference is small (may be a real signal), the original value is retained. Finally, the processed YUV image is converted back to the RGB color space, and the image after noise reduction is output. In this way, the color noise interference in the image can be effectively reduced, and the color purity and quality of the image can be improved.

[0056] Specifically, color noise usually manifests as random and unnatural color spots or particles appearing in the image.

[0057] S102, performing forward gamma correction processing on the first noise-reduced image to obtain a corrected image.

[0058] Since the perception of brightness by the human eye is nonlinear, it follows a power-law function relationship. Forward gamma correction (RGB Gamma) of the image adjusts the power function transformation of the image pixel value, compensates for the nonlinear characteristics of the display device, makes the brightness and contrast of the image more consistent with the visual perception of the human eye, optimizes the color display effect, and makes the image present consistent visual effects on different devices.

[0059] Therefore, the gamma curve required for display is applied to the RGB data, the brightness and contrast of each channel are adjusted, and the nonlinear RGB image data is output to adapt to the display device, thereby improving the color and brightness performance of the image and making it more natural and real.

[0060] S103, determining whether a three-dimensional time domain noise reduction function is enabled.

[0061] In the color noise reduction processing of the image, according to the different application scenarios of the image, three-dimensional time domain noise reduction processing of the image or no three-dimensional time domain noise reduction processing is selected. For example, in the surround view scene, three-dimensional time domain noise reduction is used to control noise in a low-speed and low-brightness environment.

[0062] Specifically, three-dimensional time domain noise reduction (3DNR) suppresses noise by combining spatial domain and time domain information and utilizing the correlation of multiple frames of images in time. The core principle is to analyze consecutive frames of the image, distinguish the motion area and the stationary area, use time domain filtering (such as inter-frame averaging or motion compensation) for the stationary part, reduce the time domain noise through multi-frame information fusion, and combine spatial filtering (such as bilateral filtering or guided filtering) for the motion area, suppress the spatial noise while preserving the details of the object. It is suitable for image processing in low-light or high-sensitivity scenes, can effectively eliminate temporal noise such as flicker and graininess, and at the same time avoid the appearance of motion blur or blur of moving objects, and significantly improve the clarity of the image.

[0063] In addition, since the object motion will cause the inter-frame pixel position change, the direct comparison of adjacent frames can introduce artifacts. Therefore, the three-dimensional time domain noise reduction also adopts the motion compensation inter-frame noise reduction technology, calculates the motion vector through motion estimation (such as optical flow method or block matching), aligns the pixels of the reference frame to the corresponding position of the current frame, and then performs the noise reduction processing, so as to effectively compensate for the motion of the object in the image, avoid the noise reduction error caused by the object motion, and thus remove the noise while retaining the image details and real information to the greatest extent.

[0064] The three-dimensional time domain noise reduction comprehensively utilizes the time domain information of the video sequence, jointly processes the image data of the current frame and the data of the previous and next frames, and finally outputs the image data with high signal-to-noise ratio through the multi-frame time domain filtering, so as to provide high-quality image basis for subsequent image display, coding and other operations.

[0065] In S104, if the three-dimensional time domain noise reduction function is not enabled, the color noise reduction processing is performed on the corrected image again to obtain a second noise reduction image.

[0066] In the embodiment, if the three-dimensional time domain noise reduction function is not enabled, the color noise reduction processing is performed on the image again, so as to eliminate the chroma noise amplified by the gamma correction.

[0067] Specifically, after the gamma noise is suppressed, the signal-to-noise ratio (SNR) of the chroma noise can be improved by 8%-12%. The signal-to-noise ratio of the chroma noise is an index for measuring the relative intensity between the chroma signal (i.e., color information) and noise in the image or video, and reflects the definition of the color signal and the noise interference degree.

[0068] In S105, if the three-dimensional time domain noise reduction function is enabled, the three-dimensional time domain noise reduction processing is performed on the corrected image, and then the color noise reduction processing is performed again to obtain a second noise reduction image.

[0069] In the embodiment, if the three-dimensional time domain noise reduction function is enabled, the three-dimensional time domain noise reduction processing is performed on the corrected image, and then the color noise reduction processing is performed again, so as to optimize the filtering strength, avoid the motion artifacts introduced by the three-dimensional time domain noise reduction, and suppress the residual noise after the time domain filtering.

[0070] Specifically, the three-dimensional time domain noise reduction combines the information of the spatial domain and the time domain to perform the noise reduction processing, and utilizes the time correlation between the multiple frames of images to reduce the noise. The three-dimensional time domain noise reduction is particularly effective for video image processing, and can effectively reduce the noise while retaining the image details.

[0071] Specifically, after the temporal filtering artifacts are eliminated, the peak signal-to-noise ratio (PSNR) of the image can be improved by 10%-15%. The peak signal-to-noise ratio is an objective quality evaluation index, which is used to measure the quality difference between the compressed or processed image and the original image.

[0072] In this embodiment, by linking the three-dimensional temporal noise reduction and color noise reduction, after the initial color noise reduction processing and forward gamma correction, the image is subjected to secondary color noise reduction processing according to the enabled state of the three-dimensional temporal noise reduction function. When the three-dimensional temporal noise reduction function is not enabled, the chroma noise amplified by the gamma correction is eliminated, and when the three-dimensional temporal noise reduction function is enabled, the residual noise after temporal filtering is suppressed, thereby optimizing the noise reduction mechanism of image processing, improving the color noise reduction effect of the image, and further improving the image quality. Through this flexible noise reduction strategy, various types of noise in the image can be removed more accurately, and a clearer and more realistic image can be generated, so that the user's visual experience is significantly improved.

[0073] In addition, the above-mentioned noise reduction strategy also has excellent universality and can be widely adapted to various types of image sensors and different image signal processing systems, providing an efficient and reliable solution for various image processing scenarios. And according to the process requirements, the color noise reduction is dynamically reused to solve the shortcomings of single color noise reduction processing and improve the utilization rate of hardware resources.

[0074] In Figure 1 the following, the technical solutions of the image color noise reduction processing method are further introduced. Figure 2

[0075] Figure 2 Another flowchart of an image color noise reduction processing method provided by the embodiments of the present application is shown in FIG. 6. Figure 2 In some embodiments, the flow of the image color noise reduction processing method includes the following steps:

[0076] S201, performing image cropping processing on the image to be processed.

[0077] The image cropping (CROP) is an operation of locally intercepting the input image according to the user-specified region of interest (ROI), which aims to extract the pixel data in a specific range from the original image to generate an RGB format image containing only the target region. Through image cropping, the visual effect can be optimized (such as removing redundant areas to highlight the main body, removing background interference), the display requirements can be adapted (such as adjusting the image ratio to adapt to different screens or platforms), the key content can be focused (such as changing the image composition), or the data volume can be reduced (such as reducing the image size).

[0078] ​Specifically, the image cropping processing outputs cropped RGB image data.

[0079] S202, color noise reduction processing is performed on the to-be-processed image to obtain a first denoised image.

[0080] Among them, the first color noise reduction processing is performed on the to-be-processed image.

[0081] Specifically, the color noise reduction processing outputs denoised RGB image data.

[0082] S203, reverse gamma correction processing is performed on the first denoised image.

[0083] Wherein, the reverse gamma correction (Gamma Reverse) is opposite to the forward gamma correction, the forward gamma correction is used to adjust the brightness or contrast of the image to compensate for the nonlinear response characteristics of the display device, and the reverse gamma correction is the inverse operation of the process, used to restore the original brightness or contrast information of the image, so as to facilitate subsequent color space change processing of the image.

[0084] And if the input image data has been subjected to gamma correction processing in the earlier image processing process during the color noise reduction processing, applying gamma correction to the image after the color noise reduction processing will cause brightness distortion (such as darkening or overexposure), in order to avoid this problem, the reverse gamma correction is used to perform nonlinear gray scale transformation on the RGB image, and linearized RGB data is output.

[0085] S204, color space transformation processing is performed on the first denoised image.

[0086] Wherein, the color space transformation (RGB2RGB) of the image refers to the process of remapping or adjusting the image color while keeping the basic framework of red (R), green (G) and blue (B) three primary colors unchanged. Within the RGB color space, by adjusting the channel values or applying specific color transformation algorithms (such as linear transformation, gamma correction, color matrix operation, etc.), the visual properties of the image such as brightness, contrast, saturation, hue, etc. are finely controlled, so as to meet the diversified needs of color performance in different application scenarios (such as photography post-processing, display device color calibration, artistic creation, etc.), to optimize the color performance of the image and ensure the color accuracy and consistency.

[0087] Specifically, the color space transformation processing outputs adjusted RGB image data.

[0088] S205, forward gamma correction processing is performed on the first denoised image to obtain a corrected image.

[0089] Wherein, the forward gamma correction is used to improve the color and brightness performance of the image, making it more natural and real.

[0090] Specifically, the forward gamma correction processing outputs non-linearized RGB image data.

[0091] S206, color space conversion processing is performed on the corrected image.

[0092] The image color space conversion (CS conversion) refers to the process of converting an image from one color space (such as RGB, HSV, YUV, Lab, etc.) to another color space. The core purpose is to adapt the image data to different application scenarios or algorithm requirements, so as to facilitate subsequent image processing or display, because different color spaces are suitable for different application scenarios.

[0093] Specifically, the above RGB data is converted into YUV data through image color space conversion processing.

[0094] Compared with the RGB format, the YUV format can occupy less bandwidth and storage space during subsequent storage and transmission.

[0095] In subsequent time domain noise reduction processing, multiple frames of data need to be buffered for processing. The data volume advantage of YUV format can reduce the computational complexity and reduce the cache requirement, thereby improving the processing efficiency. In addition, YUV format separates luminance from chrominance, so that more detailed processing can be performed on the luminance component in time domain noise reduction. The luminance component (Y) represents the brightness of the image, which is the signal that needs to be focused on in time domain noise reduction. By processing the luminance component separately, noise can be removed more effectively while preserving the details and edge information of the image.

[0096] S207, determine whether the three-dimensional time domain noise reduction function is enabled.

[0097] According to whether the three-dimensional time domain noise reduction function is enabled, the subsequent noise reduction processing flow is selected.

[0098] Optionally, determining whether the three-dimensional time domain noise reduction function is enabled comprises:

[0099] Step 1, obtain an enable signal of the three-dimensional time domain noise reduction function.

[0100] The enable signal is a control signal used to enable or disable a specific function, module or device in an electronic system or digital circuit, usually represented in binary form (high or low level), and the working state of the target object is controlled by changing the signal state. Therefore, whether the three-dimensional time domain noise reduction function is enabled can be determined by the enable signal of the three-dimensional time domain noise reduction function.

[0101] Step 2, if the enable signal indicates that the three-dimensional time domain noise reduction function is closed, it is determined that the three-dimensional time domain noise reduction function is not enabled.

[0102] Step 3, if the enable signal indicates that the three-dimensional time domain noise reduction function is turned on, it is determined that the three-dimensional time domain noise reduction function is enabled.

[0103] S208, if the three-dimensional time domain noise reduction function is not enabled, the color noise reduction processing is performed again on the corrected image to obtain a second noise reduction image.

[0104] Wherein, the color noise reduction processing is performed again to eliminate the chroma noise amplified by the gamma correction.

[0105] S209, if the three-dimensional time domain noise reduction function is enabled, two-dimensional time domain noise reduction processing is performed on the corrected image.

[0106] Wherein, the two-dimensional time domain noise reduction (2DNR_YUV) is used to perform two-dimensional noise reduction processing on the image in the YUV color space, and the pixel values in the spatial domain are used for filtering. The noise in the image is reduced, the image quality is improved, and the image details are preserved.

[0107] Image two-dimensional time domain noise reduction is a noise reduction technology for YUV color space image in single frame time dimension (actually spatial domain simulation time domain local characteristics or frame optimization in specific scene), the core of which is to analyze the spatial neighborhood relationship of pixels in the current frame, and use the similarity or statistical characteristics between pixels to identify and suppress noise, such as using non-local mean filter, bilateral filter algorithm, etc., to process the luminance (Y) and chrominance (UV) channels of YUV respectively, to preserve image details while reducing high-frequency noise, especially suitable for quality improvement of single frame image in static scene or low dynamic video, but compared with 3D noise reduction technology combining multiple frame information, its suppression ability to time domain noise in dynamic scene is limited.

[0108] Specifically, the two-dimensional time domain noise reduction processing outputs the YUV data of the preliminary noise reduction processing.

[0109] S208, after the three-dimensional time domain noise reduction processing on the corrected image, the color noise reduction processing is performed again to obtain a second noise reduction image.

[0110] Wherein, after the preliminary noise reduction by two-dimensional time domain noise reduction processing, further noise reduction processing is performed by three-dimensional time domain noise reduction (combining current frame and previous and next frame data), which further removes noise (such as motion compensation inter-frame noise reduction) through multi-frame time domain filtering, and outputs YUV data with high signal-to-noise ratio. After the three-dimensional time domain noise reduction processing, the color noise reduction processing is used to suppress the residual noise of time domain filtering, and the color noise reduction effect of the image is further improved.

[0111] In the embodiment, the secondary color noise reduction processing is performed on the image according to the enabled state of the three-dimensional time domain noise reduction function after the primary color noise reduction processing and the forward gamma correction, so that the noise reduction mechanism of the image processing is optimized, the color noise reduction effect of the image is improved, and the image quality is further improved.

[0112] Figure 3 is a structural schematic diagram of an image color noise reduction processing system provided by the embodiment of the present application, referring to Figure 3 The image color noise reduction processing system includes various functional modules for implementing the foregoing image color noise reduction processing method, and any functional module can be implemented in a software and / or hardware manner.

[0113] In some embodiments, the image color noise reduction processing system 300 includes an image noise reduction module 301 and a gamma correction module 302. Wherein:

[0114] The image noise reduction module 301 is configured to perform color noise reduction processing on a to-be-processed image to obtain a first noise reduction image.

[0115] The gamma correction module 302 is configured to perform forward gamma correction processing on the first noise reduction image to obtain a corrected image.

[0116] The image noise reduction module 301 is configured to determine whether a three-dimensional time domain noise reduction function is enabled.

[0117] If the three-dimensional time domain noise reduction function is not enabled, the corrected image is subjected to secondary color noise reduction processing to obtain a second noise reduction image.

[0118] If the three-dimensional time domain noise reduction function is enabled, the corrected image is subjected to three-dimensional time domain noise reduction processing and then subjected to secondary color noise reduction processing to obtain a second noise reduction image.

[0119] In some embodiments, the system 300 further includes a color space conversion module 303, which is specifically configured to:

[0120] The first noise reduction image is subjected to color space conversion processing before the forward gamma correction processing is performed on the first noise reduction image.

[0121] In some embodiments, the gamma correction module 302 is specifically further configured to:

[0122] The first noise reduction image is subjected to inverse gamma correction processing before the color space conversion processing is performed on the first noise reduction image.

[0123] In some embodiments, the system 300 further includes a color space conversion module 304, which is specifically configured to:

[0124] After the first denoised image is subjected to forward gamma correction processing, the corrected image is subjected to color space conversion processing.

[0125] In some embodiments, the image denoising module 301 is specifically configured to:

[0126] An enabling signal of the three-dimensional time domain denoising function is acquired.

[0127] If the enabling signal indicates that the three-dimensional time domain denoising function is turned off, it is determined that the three-dimensional time domain denoising function is not enabled.

[0128] If the enabling signal indicates that the three-dimensional time domain denoising function is turned on, it is determined that the three-dimensional time domain denoising function is enabled.

[0129] In some embodiments, the image denoising module 301 is specifically further configured to:

[0130] Before the corrected image is subjected to three-dimensional time domain denoising processing, the corrected image is subjected to two-dimensional time domain denoising processing.

[0131] In some embodiments, the system 300 further comprises an image cropping module 305, which is specifically configured to:

[0132] Before the to-be-processed image is subjected to color denoising processing, the to-be-processed image is subjected to image cropping processing.

[0133] The image color denoising processing system 300 provided by the embodiments of the present application is used to execute the technical solutions provided by the foregoing image color denoising processing method embodiments, and has similar implementation principles and technical effects to those in the foregoing method embodiments, which will not be described here in detail.

[0134] It should be noted that the division of each module of the above apparatus is only a logical functional division, and all or part of the modules can be integrated onto one physical entity, or can be physically separated. These modules can all be implemented in the form of software called by a processing element, or all be implemented in the form of hardware, or part of the modules are implemented in the form of software called by a processing element, and part of the modules are implemented in the form of hardware. For example, the image denoising module 301 can be a separately established processing element, or can be integrated in a chip of the above apparatus, and in addition, the functions of the image denoising module 301 can be stored in the form of program code in the memory of the above apparatus, and called and executed by a processing element of the above apparatus. The implementation of other modules is similar. In addition, all or part of the modules can be integrated together, or can be independently implemented. The processing element herein can be an integrated circuit having a signal processing capability. In the implementation process, each step of the above method or each module can be completed by an integrated logic circuit of hardware or an instruction in the form of software in the processor.

[0135] Figure 4 A structural schematic diagram of an image signal processor provided by an embodiment of the present application is shown in FIG. 4. Figure 4 The image signal processor 400 includes a processor body 401 and a memory 402 in communication connection with the processor body 401.

[0136] The memory 402 stores computer execution instructions.

[0137] The processor body 401 executes the computer execution instructions stored in the memory 402 to realize the technical solutions of the image color noise reduction processing method.

[0138] In the image signal processor 400, the memory 402 and the processor body 401 are directly or indirectly electrically connected to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines, such as through bus connection. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or only one type of bus. The memory 402 stores computer execution instructions for realizing the image color noise reduction processing method, including at least one software function module stored in the memory 402 in the form of software or firmware. The processor body 401 executes various function applications and data processing by running the software program and module stored in the memory 402.

[0139] The memory 402 includes at least one type of readable storage medium, not limited to random access memory (RAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), etc. The memory 402 is used to store programs, and the processor body 401 executes the programs after receiving execution instructions. Further, the software programs and modules in the memory 402 can also include an operating system, which can include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and can communicate with various hardware or software components to provide an operating environment for other software components.

[0140] The processor body 401 can be an integrated circuit chip with signal processing capability. The processor body 401 can be a general-purpose processor body, including a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), etc. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor body can be a microprocessor body, or the processor body 401 can also be any conventional processor body, etc.

[0141] The image signal processor 400 is used to execute the technical solutions provided by the foregoing image color noise reduction processing method embodiments, and the implementation principle and technical effects are similar to those of the foregoing method embodiments, which will not be described here.

[0142] The embodiments of the present application also provide a computer readable storage medium, and the computer readable storage medium stores computer execution instructions. When the computer execution instructions are executed, the technical solutions of the foregoing image color noise reduction processing method are implemented.

[0143] The computer readable storage medium described above can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The computer readable storage medium can be any available medium accessible by a general or special purpose computer.

[0144] An exemplary computer readable storage medium is coupled to the processor body, so that the processor body can read information from the computer readable storage medium, and can write information to the computer readable storage medium. Of course, the computer readable storage medium can also be an integral part of the processor body. The processor body and the computer readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor body and the computer readable storage medium can also exist as discrete components in the control device of the image color noise reduction processing system.

[0145] The embodiment of the present application also provides a computer program product, comprising a computer program, which, when executed, is used to implement the technical solutions of the image color noise reduction processing method as described above.

[0146] In the above embodiment, those skilled in the art can understand that the implementation of the above-mentioned method embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When implemented by software, it can be realized in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the flow or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless network, microwave, etc.) mode. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as DVD), or semiconductor media (such as solid state disk (SSD)) and the like.

[0147] In the above embodiments, the description of each embodiment is focused on one aspect, and the part not described in detail in a certain embodiment can be referred to the relevant description of other embodiments. Each technical feature of the above embodiments can be combined arbitrarily, and in order to make the description brief, each technical feature in the above embodiments is not described in all possible combinations, however, as long as the combination of these technical features does not exist contradictory, it should be considered as the scope of the disclosure.

[0148] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application cover any and all variations of the application that come within the scope of the general concept of the application and that the claims be interpreted not to be limited to the specific examples described above. The specification and examples are to be considered exemplary only, with the true scope and spirit of the application indicated by the following claims.

[0149] It is to be understood that the application is not limited to the precise details of construction and the above-described embodiments and that various modifications and changes can be effected therein by one skilled in the art without departing from the scope of the application. The scope of the application should be determined by the claims appended hereto.

Claims

1. A method for image color noise reduction, characterized in that: include: Performing color noise reduction processing on the image to be processed to obtain a first noise-reduced image; Performing positive gamma correction on the first denoised image to obtain a corrected image; Determine whether the 3D temporal noise reduction function is enabled; If the three-dimensional temporal noise reduction function is not enabled, performing color noise reduction processing on the corrected image again to obtain a second noise-reduced image; If the three-dimensional temporal noise reduction function is enabled, the corrected image is subjected to three-dimensional temporal noise reduction processing and then to color noise reduction processing again to obtain a second noise-reduced image.

2. The method according to claim 1, characterized in that Before performing the forward gamma correction process on the first denoised image, the method further includes: Performing color space transformation processing on the first denoised image.

3. The method according to claim 2, characterized in that Before performing color space transformation processing on the first denoised image, the method further includes: Perform inverse gamma correction on the first denoised image.

4. The method according to any one of claims 1 to 3, characterized in that After performing the forward gamma correction process on the first denoised image, the method further includes: Performing color space conversion processing on the corrected image.

5. The method according to any one of claims 1 to 3, characterized in that Determines whether 3D temporal noise reduction is enabled, including: Obtain an enable signal for a three-dimensional time-domain noise reduction function; If the enable signal indicates that the 3D temporal noise reduction function is turned off, determining that the 3D temporal noise reduction function is not enabled; If the enable signal indicates that the 3D temporal noise reduction function is turned on, it is determined that the 3D temporal noise reduction function is enabled.

6. The method according to any one of claims 1 to 3, characterized in that Before performing three-dimensional temporal noise reduction processing on the corrected image, the method further includes: Perform two-dimensional time-domain noise reduction processing on the corrected image.

7. The method according to any one of claims 1 to 3, characterized in that Before performing color noise reduction processing on the image to be processed, the method further includes: Perform image cropping processing on the image to be processed.

8. An image color noise reduction processing system, characterized in that: include: An image denoising module, configured to perform color denoising on the image to be processed to obtain a first denoised image; a gamma correction module, configured to perform a forward gamma correction process on the first denoised image to obtain a corrected image; An image noise reduction module is used to determine whether the 3D temporal noise reduction function is enabled; If the three-dimensional temporal noise reduction function is not enabled, performing color noise reduction processing on the corrected image again to obtain a second noise-reduced image; If the three-dimensional temporal noise reduction function is enabled, the corrected image is subjected to three-dimensional temporal noise reduction processing and then to color noise reduction processing again to obtain a second noise-reduced image.

9. An image signal processor, characterized in that: The device comprises a processor body and a memory communicatively connected to the processor body; The memory stores computer-executable instructions; The processor body executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method according to any one of claims 1 to 7.

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