Denoising method and device, electronic equipment, storage medium and chip

Abstract

The invention provides a denoising method and device, electronic equipment, a storage medium and a chip, and relates to the field of image processing, and the technical scheme comprises the steps: obtaining a short-frame image after color gamut conversion and a long-frame image after color gamut conversion, performing conversion processing on the short-frame image after color gamut conversion and the long-frame image after color gamut conversion through a window with a preset size respectively to obtain a conversion coefficient of the short-frame image and a conversion coefficient of the long-frame image, determining a window comprehensive coefficient according to the conversion coefficient of the short-frame image and the conversion coefficient of the long-frame image, and performing window conversion on the window comprehensive coefficient according to the window comprehensive coefficient. And performing de-noising calculation on the effective signals in the window based on the window comprehensive coefficient. According to the scheme provided by the invention, the short frame image with a low signal-to-noise ratio is de-noised through a joint de-noising mode that the long frame image guides the short frame image, so that more detail textures can be reserved while de-noising is carried out.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular to a denoising method and device, an electronic device, a storage medium and a chip. Background Art

[0002] High Dynamic Range (HDR) imaging is a major selling point for digital devices, and also one of the major technical difficulties, and has attracted much attention. The difference in exposure between long and short frames is used to obtain a high dynamic range response of brightness. In the fusion process, considering that the image information caused by the low signal-to-noise ratio of short frames is often accompanied by a large amount of noise, resulting in poor visual perception of the human eye, the traditional HDR solution performs denoising on short frames.

[0003] Considering the computational dimension in the process of denoising short frames, the original data volume of the color filter array (CFA) is one-third of the traditional one. At the same time, it has not undergone subsequent demosaicing and nonlinear operations. The part with low signal-to-noise ratio of short frames cannot be separated from the useful signal through traditional frequency domain decomposition. Summary of the invention

[0004] The present disclosure provides a denoising method and device, an electronic device, a storage medium and a chip to solve the problems in the related art. The method guides the joint denoising of short frame images through long frame images, performs denoising on short frame images with low signal-to-noise ratio, and retains more detailed textures while removing noise.

[0005] A first aspect of the present disclosure provides a denoising method, the method comprising:

[0006] Obtaining a short frame image after color gamut conversion and a long frame image after color gamut conversion;

[0007] The short frame image after the color gamut conversion and the long frame image after the color gamut conversion are respectively transformed through a window of a preset size to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image;

[0008] Determining a window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image;

[0009] A denoising calculation is performed on the effective signal in the window based on the window comprehensive coefficient.

[0010] In some embodiments, acquiring the short frame image after color gamut conversion and the long frame image after color gamut conversion includes:

[0011] Aligning the acquired original short-frame images to obtain short-frame images, wherein the frame length of the short-frame images is the same as the frame length of the long-frame images;

[0012] The color gamut conversion is performed on each channel of the short frame image and the long frame image respectively to obtain a short frame image after the color gamut conversion and a long frame image after the color gamut conversion.

[0013] In some embodiments, performing color gamut conversion on each channel of the short frame image and the long frame image to obtain the short frame image after color gamut conversion and the long frame image after color gamut conversion comprises:

[0014] Respectively obtain four-channel RGB data of the short frame image and the long frame image;

[0015] According to the preset conversion matrix and the RGB data of a single channel, the difference data of local brightness, single channel and other channels are obtained;

[0016] Generate the short frame image after the color gamut conversion according to the local brightness corresponding to the short frame image and the difference data between the single channel and other channels;

[0017] The long frame image after the color gamut conversion is generated according to the local brightness corresponding to the long frame image and the difference data between the single channel and other channels.

[0018] In some embodiments, the method further comprises:

[0019] Preprocessing operations are performed on the short frame image after color gamut conversion and the long frame image after color gamut conversion respectively, and the preprocessing operation includes any one of Gaussian convolution and dilated convolution.

[0020] In some embodiments, the transform coefficients include: discrete cosine transform DCT coefficients;

[0021] The transforming process is performed on the short frame image after the color gamut conversion and the long frame image after the color gamut conversion through a window of a preset size to obtain the transform coefficients of the short frame image and the transform coefficients of the long frame image, respectively. The process comprises:

[0022] Determining the window of the preset size;

[0023] The short frame image after the color gamut conversion and the long frame image after the color gamut conversion are respectively transformed along the vertical direction and the horizontal direction according to a window of a preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image.

[0024] In some embodiments, determining the window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image includes:

[0025] Calculating the signal-to-noise ratio in the frequency domain according to the transformation coefficient of the long frame image to obtain a signal-to-noise ratio coefficient;

[0026] The signal-to-noise ratio coefficient is attenuated based on the signal-to-noise ratio coefficient and the transformation coefficient of the short frame image to obtain a window comprehensive coefficient.

[0027] In some embodiments, performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient includes:

[0028] Accumulating the signal-to-noise ratio coefficients to obtain accumulated signal-to-noise ratio coefficients;

[0029] Performing an inverse transformation on the window comprehensive coefficient to obtain an inverse transformed window comprehensive coefficient;

[0030] The effective signal in the window is de-noised according to the inversely transformed window comprehensive coefficient and the accumulated signal-to-noise ratio coefficient.

[0031] In some embodiments, after performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient, the method further includes:

[0032] Perform inverse transform on the denoised image and output the final processed image.

[0033] A second aspect of the present disclosure provides a denoising device, the device comprising:

[0034] An acquisition unit, used for acquiring a short frame image after color gamut conversion and a long frame image after color gamut conversion;

[0035] A transformation unit, used to transform the short frame image after the color gamut conversion and the long frame image after the color gamut conversion through a window of a preset size, respectively, to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image;

[0036] A processing unit, configured to determine a window comprehensive coefficient according to a transformation coefficient of the short frame image and a transformation coefficient of the long frame image;

[0037] The denoising unit is used to perform denoising calculation on the effective signal in the window based on the window comprehensive coefficient.

[0038] In some embodiments, the acquisition unit includes:

[0039] An alignment module, used for performing alignment processing on the acquired original short frame images to obtain short frame images, wherein the frame length of the short frame images is the same as the frame length of the long frame images;

[0040] The processing module is used to perform color gamut conversion on each channel of the short frame image and the long frame image respectively to obtain the short frame image after color gamut conversion and the long frame image after color gamut conversion.

[0041] In some embodiments, the processing module is further configured to:

[0042] Respectively obtain four-channel RGB data of the short frame image and the long frame image;

[0043] According to the preset conversion matrix and the RGB data of a single channel, the difference data of local brightness, single channel and other channels are obtained;

[0044] Generate the short frame image after the color gamut conversion according to the local brightness corresponding to the short frame image and the difference data between the single channel and other channels;

[0045] The long frame image after the color gamut conversion is generated according to the local brightness corresponding to the long frame image and the difference data between the single channel and other channels.

[0046] In some embodiments, the apparatus further comprises:

[0047] The preprocessing unit is used to perform preprocessing operations on the short frame image after color gamut conversion and the long frame image after color gamut conversion respectively, and the preprocessing operation includes any one of Gaussian convolution and dilated convolution.

[0048] In some embodiments, the transform coefficients include: discrete cosine transform DCT coefficients;

[0049] The transformation unit is further configured to include:

[0050] Determining the window of the preset size;

[0051] The short frame image after the color gamut conversion and the long frame image after the color gamut conversion are respectively transformed along the vertical direction and the horizontal direction according to a window of a preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image.

[0052] In some embodiments, the processing unit is further configured to:

[0053] Calculating the signal-to-noise ratio in the frequency domain according to the transformation coefficient of the long frame image to obtain a signal-to-noise ratio coefficient;

[0054] The signal-to-noise ratio coefficient is attenuated based on the signal-to-noise ratio coefficient and the transformation coefficient of the short frame image to obtain a window comprehensive coefficient.

[0055] In some embodiments, the denoising unit is further configured to:

[0056] Accumulating the signal-to-noise ratio coefficients to obtain accumulated signal-to-noise ratio coefficients;

[0057] Performing an inverse transformation on the window comprehensive coefficient to obtain an inverse transformed window comprehensive coefficient;

[0058] The effective signal in the window is de-noised according to the inversely transformed window comprehensive coefficient and the accumulated signal-to-noise ratio coefficient.

[0059] In some embodiments, the apparatus further comprises:

[0060] The output unit is used to perform inverse transformation processing on the denoised image after the denoising unit performs denoising calculation on the effective signal in the window based on the window comprehensive coefficient, and output the final processed image.

[0061] The third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect embodiment of the present disclosure.

[0062] The fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the first aspect embodiment of the present disclosure.

[0063] The fifth aspect embodiment of the present disclosure proposes a chip, which includes one or more interfaces and one or more processors; the interface is used to receive signals from a memory of an electronic device and send signals to the processor, the signals include computer instructions stored in the memory, and when the processor executes the computer instructions, the electronic device executes the method described in the first aspect embodiment of the present disclosure.

[0064] In summary, according to the denoising method, device, electronic device, storage medium and chip proposed in the present disclosure, the method includes obtaining a short-frame image after color gamut conversion and a long-frame image after color gamut conversion, respectively transforming the short-frame image after color gamut conversion and the long-frame image after color gamut conversion through a window of preset size to obtain the transformation coefficient of the short-frame image and the transformation coefficient of the long-frame image, determining the window comprehensive coefficient according to the transformation coefficient of the short-frame image and the transformation coefficient of the long-frame image, and performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient. The solution provided in the present disclosure guides the joint denoising of short-frame images through long-frame images, denoises short-frame images with low signal-to-noise ratio, and can retain more detailed textures while removing noise.

[0065] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0067] Figure 1 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0068] Figure 2 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0069] Figure 3 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0070] Figure 4 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0071] Figure 5 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0072] Figure 6 A schematic diagram of a preset window provided in an embodiment of the present disclosure;

[0073] Figure 7 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0074] Figure 8 A flowchart of a denoising method provided by an embodiment of the present disclosure;

[0075] Fig. 9 A schematic diagram of the structure of a denoising device provided in an embodiment of the present disclosure;

[0076] Fig.10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure;

[0077] Fig.11 A schematic diagram of the structure of a chip provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0078] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0079] High Dynamic Range (HDR) imaging is a major selling point for digital devices, and also one of the major technical difficulties, and has attracted much attention. The difference in exposure between long and short frames is used to obtain a high dynamic range response of brightness. In the fusion process, considering that the image information caused by the low signal-to-noise ratio of short frames is often accompanied by a large amount of noise, resulting in poor visual perception of the human eye, the traditional HDR solution performs denoising on short frames.

[0080] Considering the computational dimension in the process of denoising short frames, the color filter array (CFA)

[0081] The original data volume is one third of the traditional one, and it has not undergone subsequent demosaicing and nonlinear operations. The short frame with low signal-to-noise ratio cannot be separated from the useful signal through traditional frequency domain decomposition.

[0082] Therefore, in order to solve the problems existing in the related art, the present disclosure proposes a denoising method, by obtaining a short frame image after color gamut conversion and a long frame image after color gamut conversion, respectively transforming the short frame image after color gamut conversion and the long frame image after color gamut conversion through a window of preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image, determining the window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image, and performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient.

[0083] This scheme uses long-frame images to guide the joint denoising of short-frame images, and denoises short-frame images with low signal-to-noise ratio. While removing noise, it can also retain more detailed textures.

[0084] The embodiments of the present disclosure are not exhaustive, but are only illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined, for example, some or all of the steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0085] In each embodiment of the present disclosure, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between the embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form a new embodiment based on their internal logical relationships.

[0086] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0087] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun after the article may be understood as a singular expression or a plural expression.

[0088] In some embodiments, terms such as "in response to ...", "in response to determining ...", "in the case of ...", "at the time of ...", "when ...", "if ...", "if ...", etc. can be used interchangeably.

[0089] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "no more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0090] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only for distinguishing different description objects and do not constitute any restrictions on the position, order, priority, quantity or content of the description objects. For the statement of the description objects, please refer to the description in the context of the claims or embodiments, and no unnecessary restrictions should be constituted due to the use of prefixes.

[0091] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0092] In the embodiments of the present disclosure, terms such as "import", "input", and "read in" can be used interchangeably.

[0093] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0094] Figure 1This is a flow chart of a denoising method provided by an embodiment of the present disclosure. The method can be applicable to application scenarios such as smart terminals, for example, executed by a terminal with an integrated image processing function or an image processor in the terminal, or executed by other devices suitable for performing denoising processing to output images, and the present disclosure is not limited thereto. Figure 1 As shown, the denoising method includes steps 101-104.

[0095] Step 101, obtaining a short frame image after color gamut conversion and a long frame image after color gamut conversion.

[0096] The short-frame image and long-frame image described in the embodiment of the present application refer to the images corresponding to the long and short exposures output alternately by the camera's sensor frame rate. The frame rate may vary from camera to camera, and the embodiment of the present application does not limit this.

[0097] In view of the data characteristics of CFA, that is, a single-channel data measurement result in which a pixel only contains one pixel, it is impossible to use the pixel-level color space to convert one by one, and it is impossible to fully denoise. The CFA data is divided into the measurement values ​​of the four positions of R, G1, G2, and B, and the four channels are processed separately, but the color correlation between the four channels must be ensured. Therefore, after obtaining the measurement values ​​of the four channels, the embodiment of the present application needs to perform the correlation operation of each channel, so as to ensure the independence of each channel and the correlation between the channels.

[0098] It should be noted that when acquiring the short frame image after color gamut conversion and the long frame image after color gamut conversion, the short frame image and the long frame image are converted respectively by a preset conversion matrix, and the measurement value of each channel is taken out.

[0099] Step 102 , transforming the short frame image after the color gamut conversion and the long frame image after the color gamut conversion respectively through a window of a preset size to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image.

[0100] The data based on CFA data is special. When determining the window size, the preset size of the window is 4*4. When performing the transformation processing, the transformation processing can be performed in the order of transforming the vertical direction first and then the horizontal direction, or the transformation processing can be performed in the order of transforming the horizontal direction first and then the vertical direction. Specifically, the embodiment of the present application does not limit the processing order of the transformation.

[0101] In some embodiments, when performing transformation processing, Discrete Cosine Transform (DCT) may be used but is not limited to it, and the embodiments of the present application do not limit this.

[0102] Step 103, determining a window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image.

[0103] According to the transformation coefficient of the long frame image, the local signal-to-noise ratio in the frequency domain is calculated, and the coherent denoising is performed by guiding the transformation coefficient of the short frame image through the signal-to-noise ratio coefficient.

[0104] Step 104: performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient.

[0105] When performing denoising calculations, the CFA data is based on the fact that it is one-third of the RGB / YUV color gamut, thus greatly reducing the denoising overhead cost.

[0106] According to the denoising method proposed in the present disclosure, the method includes obtaining a short-frame image after color gamut conversion and a long-frame image after color gamut conversion, respectively transforming the short-frame image after color gamut conversion and the long-frame image after color gamut conversion through a window of preset size to obtain the transformation coefficient of the short-frame image and the transformation coefficient of the long-frame image, determining the window comprehensive coefficient according to the transformation coefficient of the short-frame image and the transformation coefficient of the long-frame image, and performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient. The solution provided in the present disclosure guides the joint denoising of short-frame images through long-frame images, denoises short-frame images with low signal-to-noise ratio, and can retain more detailed textures while removing noise.

[0107] As a refinement of the above embodiment, when acquiring the short frame image after the color gamut conversion and the long frame image after the color gamut conversion in step 101, the following methods may be used but are not limited to: Figure 2 As shown, including:

[0108] Step 201 , aligning the acquired original short frame images to obtain short frame images, wherein the frame length of the short frame images is the same as the frame length of the long frame images.

[0109] Since there is a significant difference in exposure between short-frame images and long-frame images, aligning the frame length of the short-frame image with the frame length of the long-frame image is a necessary step for joint denoising of short and long frames. This alignment operation can make the short-frame image closer to the long-frame image in brightness and contrast, and better utilize the information between the two during the fusion process.

[0110] The alignment processing of the original short-frame image described in the embodiment of the present application refers to the alignment processing of the image, that is, only pixel alignment is required without additional exposure alignment operation. The alignment processing step helps to improve the overall joint denoising effect.

[0111] Step 202 , color gamut conversion is performed on each channel of the short frame image and the long frame image to obtain a short frame image after color gamut conversion and a long frame image after color gamut conversion.

[0112] Considering the error caused by the alignment operation in step 201, a preprocessing operation needs to be performed before the color gamut conversion is performed to eliminate the error caused by the alignment.

[0113] In the related art, a commonly used method is to divide the CFA data into measurement values ​​of four positions of R, G1, G2, and B, and use a single-channel denoising processing method for each of the four channels. However, this processing method ignores the color correlation, resulting in poor final results. Therefore, the embodiment of the present application needs to perform channel-related operations when performing color gamut conversion to ensure that the channels after conversion are related and independent.

[0114] When executing the steps, the following methods may be used but are not limited to: Figure 3 As shown, including:

[0115] Step 2021, respectively obtain the four-channel RGB data of the short frame image and the long frame image.

[0116] The measurement of RGB data may be performed by adopting but not limited to any implementation method in the related art, and the specific embodiment of the present application does not limit the implementation algorithm.

[0117] Step 2022, obtaining the difference data of local brightness, single channel and other channels according to the preset conversion matrix and the RGB data of the single channel.

[0118] The preset conversion matrix described in the embodiment of the present application is as follows:

[0119]

[0120] Step 2023, generating the short frame image after the color gamut conversion according to the local brightness corresponding to the short frame image and the difference data between the single channel and other channels.

[0121] Step 2024: Generate the color gamut-converted long-frame image according to the local brightness corresponding to the long-frame image and the difference data between the single channel and other channels.

[0122] Based on the preset conversion matrix shown in step 2022, the local brightness corresponding to the short frame image and the difference data between the single channel and other channels are calculated respectively, and the short frame image after color gamut conversion is constructed based on the local brightness corresponding to the short frame image and the difference data between the single channel and other channels. In the embodiment of the present application, the short frame image after color gamut conversion is in the form of a matrix.

[0123] The short frame image after color gamut conversion is as follows:

[0124]

[0125] Wherein, A is the preset conversion matrix, noisy_luma is the local brightness, noisy_uv0, noisy_uv11 and noisy_uv2 are the difference data between a single channel and other channels respectively.

[0126] In adopting Figure 3 After the method shown completes the color conversion of the long-frame and short-frame images, a preprocessing operation is performed on the short-frame image after color gamut conversion and the long-frame image after color gamut conversion output after the color gamut conversion. The preprocessing operation includes any one of Gaussian convolution and dilated convolution.

[0127] Taking the transformation coefficient as discrete cosine transform DCT coefficient as an example, the short frame image after color gamut conversion and the long frame image after the color gamut conversion are transformed and processed respectively through a window of preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image, which includes: determining the window of preset size, and transforming the short frame image after color gamut conversion and the long frame image after color gamut conversion along the vertical direction and the horizontal direction according to the window of preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image.

[0128] The DCT long and short frame transform is used for processing. The DCT processing window is taken as 4*4. First, 4 1*4 DCT transforms are performed in the vertical direction. Then, 4 1*4 DCT transforms are performed in the horizontal direction on this basis. Finally, the DCT coefficients after long and short frame decomposition are obtained respectively, that is, the transform coefficients of the short frame image and the transform coefficients of the long frame image are obtained.

[0129] In the embodiment of the present application, the local signal-to-noise ratio of the frequency domain is calculated based on the transformation coefficient of the long frame image, and the denoising operation is performed by guiding the long frame image through the coefficient of the signal-to-noise ratio coefficient (w). Figure 4 As shown, determining the window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image specifically includes:

[0130] Step 301, calculating the signal-to-noise ratio in the frequency domain according to the transformation coefficient of the long frame image to obtain a signal-to-noise ratio coefficient.

[0131] Calculate the local brightness of the long frame image as the index, and find the local noise variance as the part of the noise s by interpolation. Please refer to the following formula:

[0132] ShortFrame_dct_coe=DCT(ShortFrame)

[0133] LongFrame_dct_coe=DCT(LongFrame)

[0134] g(w)=LongFrame_dct_coe

[0135] Among them, ShortFrame_dct_coe is the transformation coefficient of the long frame image obtained by performing conventional DCT transformation on the short frame (ShortFrame) through a 4*4 window, and LongFrame_dct_coe represents the transformation coefficient of the short frame image obtained by performing conventional DCT transformation on the short frame (ShortFrame) through a 4*4 window.

[0136]

[0137] Among them, coefficient(w) is the signal-to-noise ratio coefficient, α is an empirical parameter, s is the calibration data obtained by interpolation, and g(w) 2 The numerical range of α*sigma 2 The numerical range of s is the same. For the implementation method of calibrating s by difference method, reference can be made to any implementation method in the related art, and the details will not be repeated here.

[0138] Step 302: performing attenuation processing on the signal-to-noise ratio coefficient based on the signal-to-noise ratio coefficient and the transform coefficient of the short frame image to obtain a window comprehensive coefficient.

[0139] When performing attenuation processing, it is achieved through the following formula:

[0140] ShortFrame_dct_coe=coefficient(w)*ShortFrame_dct_coe

[0141] The signal-to-noise ratio coefficient (w) is attenuated locally. Coefficient (w) is a number less than 1, so coefficient (w) * ShortFrame _ dct _ coe also gradually becomes smaller, thus achieving attenuation.

[0142] As a refinement, Figure 5 As shown, performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient includes:

[0143] Step 401: Accumulate the signal-to-noise ratio coefficients to obtain accumulated signal-to-noise ratio coefficients.

[0144] Presented by Figure 4The example shown is implemented by the following formula when performing accumulation processing:

[0145] Sum_coefficient(w)+=coefficient(w)

[0146] Among them, Sum_coefficient(w) is the accumulated signal-to-noise ratio coefficient.

[0147] Step 402, performing inverse transformation processing on the window comprehensive coefficient to obtain the inverse transformed window comprehensive coefficient.

[0148] When performing the inverse transformation, it is achieved through the following formula:

[0149] ShortFrame_bmp=IDCT(ShortFrame_dct_coe)

[0150] Step 403: performing denoising calculation on the effective signal in the window according to the inverse transformed window comprehensive coefficient and the accumulated signal-to-noise ratio coefficient.

[0151] The local effective signal is normalized according to the window comprehensive coefficient after inverse transformation to restore the real image:

[0152] When normalizing, it is achieved through the following formula:

[0153] X(patch)=X(patch)+ShortFrame_bmp*(1+Sum_coefficient) -1

[0154] W(patch)=W(patch)+(1+Sum_coefficient) -1

[0155]

[0156] To understand the normalization process, please refer to Figure 6 , Figure 6It is single-channel data, containing 4*4 large windows, each large window contains 4*4 small windows, and the single-channel data can be any of the following, namely the aforementioned luma, uv0, uv1 and uv2; secondly, the first small window 4*4 is to mark the DCT decomposition position of the gray pixel at the viewing angle, and the second small window is obtained by moving to the right with a step of 1. The corresponding value in the position of the box moves left by one as the box moves to the right, and so on, to achieve single-channel, single-pixel 4*4 DCT denoising processing at different positions in the 7*7 single channel. In this way, the consideration of the value at different viewing angles can be considered as much as possible to achieve the operation of protecting details and edges.

[0157] like Figure 7 As shown, the embodiment of the present application also provides a denoising method, including:

[0158] Step 501 , aligning the acquired original short frame images to obtain short frame images, wherein the frame length of the short frame images is the same as the frame length of the long frame images.

[0159] Step 502 , color gamut conversion is performed on each channel of the short frame image and the long frame image to obtain a short frame image after color gamut conversion and a long frame image after color gamut conversion.

[0160] Step 503 , transforming the short frame image after the color gamut conversion and the long frame image after the color gamut conversion respectively through a window of a preset size to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image.

[0161] Step 504: Calculate the signal-to-noise ratio in the frequency domain according to the transformation coefficient of the long frame image to obtain a signal-to-noise ratio coefficient.

[0162] Step 505: Perform attenuation processing on the signal-to-noise ratio coefficient based on the signal-to-noise ratio coefficient and the transform coefficient of the short frame image to obtain a window comprehensive coefficient.

[0163] Step 506: Perform denoising calculation on the effective signal in the window based on the window comprehensive coefficient.

[0164] For steps 501 to 506, please refer to the detailed description of the above embodiment, and the embodiment of the present application will not be described in detail here.

[0165] Step 507, performing inverse transformation processing on the denoised image, and outputting a final processed image.

[0166] The denoised image is subjected to an inverse DCT transform from the frequency domain to the time domain, followed by an inverse color domain process. The inverse process matrix (color inverse transform matrix) is shown below:

[0167]

[0168] Among them, A -1 It is the inverse matrix of the preset transformation matrix.

[0169] Therefore, this solution has the following beneficial effects:

[0170] 1. The embodiment of the present application is used for the denoising operation of short frames in the HDR mode, which can better preserve the details and weak texture parts of the image while ensuring good denoising in areas with low signal-to-noise ratio.

[0171] 2. Assisting the subsequent HDR long and short frame fusion will not introduce huge differences in clarity in the fusion transition area due to denoising.

[0172] 3. Based on the fact that CFA data is one-third of the RGB / YUV color gamut, the denoising cost is greatly reduced.

[0173] Corresponding to the above denoising method, the present invention also provides a denoising device. Since the device embodiment of the present invention corresponds to the above method embodiment, details not disclosed in the device embodiment can be referred to the above method embodiment, and will not be described in detail in the present invention.

[0174] Figure 8 A schematic diagram of the structure of a denoising device provided in an embodiment of the present disclosure, the denoising device comprising:

[0175] An acquisition unit 61 is used to acquire a short frame image after color gamut conversion and a long frame image after color gamut conversion;

[0176] A transformation unit 62, configured to perform transformation processing on the short frame image after the color gamut conversion and the long frame image after the color gamut conversion through a window of a preset size, respectively, to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image;

[0177] A processing unit 63, configured to determine a window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image;

[0178] The denoising unit 64 is used to perform denoising calculation on the effective signal in the window based on the window comprehensive coefficient.

[0179] According to the denoising device proposed in the present disclosure, a short-frame image after color gamut conversion and a long-frame image after color gamut conversion are obtained, and the short-frame image after color gamut conversion and the long-frame image after color gamut conversion are transformed through a window of preset size respectively to obtain the transformation coefficient of the short-frame image and the transformation coefficient of the long-frame image, and the window comprehensive coefficient is determined according to the transformation coefficient of the short-frame image and the transformation coefficient of the long-frame image, and the effective signal in the window is denoised based on the window comprehensive coefficient. The solution provided in the present disclosure guides the joint denoising of short-frame images through long-frame images, denoises short-frame images with low signal-to-noise ratio, and can retain more detailed textures while removing noise.

[0180] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the acquisition unit 61 includes:

[0181] An alignment module 611 is used to align the acquired original short frame images to obtain short frame images, wherein the frame length of the short frame image is the same as the frame length of the long frame image;

[0182] The processing module 612 is used to perform color gamut conversion on each channel of the short frame image and the long frame image respectively to obtain a short frame image after color gamut conversion and a long frame image after color gamut conversion.

[0183] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the processing module 612 is also used for:

[0184] Respectively obtain four-channel RGB data of the short frame image and the long frame image;

[0185] According to the preset conversion matrix and the RGB data of a single channel, the difference data of local brightness, single channel and other channels are obtained;

[0186] Generate the short frame image after the color gamut conversion according to the local brightness corresponding to the short frame image and the difference data between the single channel and other channels;

[0187] The long frame image after the color gamut conversion is generated according to the local brightness corresponding to the long frame image and the difference data between the single channel and other channels.

[0188] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the device also includes:

[0189] The preprocessing unit 65 is used to perform preprocessing operations on the short frame image after the color gamut conversion and the long frame image after the color gamut conversion respectively, and the preprocessing operation includes any one of Gaussian convolution and dilated convolution.

[0190] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the transform coefficients include: discrete cosine transform DCT coefficients;

[0191] The transformation unit 62 is further configured to include:

[0192] Determining the window of the preset size;

[0193] The short frame image after the color gamut conversion and the long frame image after the color gamut conversion are respectively transformed along the vertical direction and the horizontal direction according to a window of a preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image.

[0194] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the processing unit 63 is further used for:

[0195] Calculating the signal-to-noise ratio in the frequency domain according to the transformation coefficient of the long frame image to obtain a signal-to-noise ratio coefficient;

[0196] The signal-to-noise ratio coefficient is attenuated based on the signal-to-noise ratio coefficient and the transformation coefficient of the short frame image to obtain a window comprehensive coefficient.

[0197] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the denoising unit 64 is also used for:

[0198] Accumulating the signal-to-noise ratio coefficients to obtain accumulated signal-to-noise ratio coefficients;

[0199] Performing an inverse transformation on the window comprehensive coefficient to obtain an inverse transformed window comprehensive coefficient;

[0200] The effective signal in the window is de-noised according to the inversely transformed window comprehensive coefficient and the accumulated signal-to-noise ratio coefficient.

[0201] Furthermore, in a possible implementation of this embodiment, as Fig. 9 As shown, the device also includes:

[0202] The output unit 66 is used to perform inverse transformation processing on the denoised image after the denoising unit 64 performs denoising calculation on the effective signal in the window based on the window comprehensive coefficient, and output a final processed image.

[0203] Since the device provided in the embodiment of the present disclosure corresponds to the methods provided in the above-mentioned embodiments, the implementation of the method is also applicable to the device provided in the embodiment and will not be described in detail in this embodiment.

[0204] In the embodiments provided in the present application, the methods and devices provided in the embodiments of the present application are introduced. In order to implement the functions in the methods provided in the embodiments of the present application, the electronic device may include a hardware structure and a software module, and implement the functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. A function of the functions may be executed in the form of a hardware structure, a software module, or a hardware structure plus a software module.

[0205] Fig.10 1 is a block diagram of an electronic device 1000 for implementing the above-mentioned denoising method according to an exemplary embodiment. For example, the electronic device 1000 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0206] Reference Fig.10 , the electronic device 1000 may include one or more of the following components: a processing component 1002 , a memory 1004 , a power component 1006 , a multimedia component 1008 , an audio component 1010 , an input / output (I / O) interface 1012 , a sensor component 1014 , and a communication component 1016 .

[0207] The processing component 1002 generally controls the overall operation of the electronic device 1000, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 1002 may include one or more processors 1020 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 1002 may include one or more modules to facilitate the interaction between the processing component 1002 and other components. For example, the processing component 1002 may include a multimedia module to facilitate the interaction between the multimedia component 1008 and the processing component 1002.

[0208] The memory 1004 is configured to store various types of data to support operations on the electronic device 1000. Examples of such data include instructions for any application or method operating on the electronic device 1000, contact data, phone book data, messages, pictures, videos, etc. The memory 1004 may be implemented by any type of volatile or non-volatile storage device 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 memory, flash memory, magnetic disk or optical disk.

[0209] The power supply component 1006 provides power to various components of the electronic device 1000. The power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1000.

[0210] The multimedia component 1008 includes a screen that provides an output interface between the electronic device 1000 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1008 includes a front camera and / or a rear camera. When the electronic device 1000 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0211] The audio component 1010 is configured to output and / or input audio signals. For example, the audio component 1010 includes a microphone (MIC), and when the electronic device 1000 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 1004 or sent via the communication component 1016. In some embodiments, the audio component 1010 also includes a speaker for outputting audio signals.

[0212] I / O interface 1012 provides an interface between processing component 1002 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0213] The sensor assembly 1014 includes one or more sensors for providing various aspects of status assessment for the electronic device 1000. For example, the sensor assembly 1014 can detect the open / closed state of the electronic device 1000, the relative positioning of components, such as the display and keypad of the electronic device 1000, and the sensor assembly 1014 can also detect the position change of the electronic device 1000 or a component of the electronic device 1000, the presence or absence of user contact with the electronic device 1000, the orientation or acceleration / deceleration of the electronic device 1000, and the temperature change of the electronic device 1000. The sensor assembly 1014 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 1014 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1014 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0214] The communication component 1016 is configured to facilitate wired or wireless communication between the electronic device 1000 and other devices. The electronic device 1000 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, 4G LTE, 5G NR (NewRadio) or a combination thereof. In an exemplary embodiment, the communication component 1016 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1016 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0215] In an exemplary embodiment, the electronic device 1000 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0216] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1004 including instructions, and the instructions can be executed by the processor 1020 of the electronic device 1000 to perform the above method for image processing. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0217] The embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the above embodiments of the present disclosure.

[0218] For electronic devices that may be chips or chip systems, see Fig.11 Schematic diagram of the chip structure shown. Fig.11 The chip shown includes a processor 1101 and an interface 1102. The number of the processor 1101 may be one or more, and the number of the interface 1102 may be multiple.

[0219] Optionally, the chip further includes a memory 1103, and the memory 1103 is used to store necessary computer programs and data.

[0220] Those skilled in the art may also understand that the various illustrative logical blocks and steps listed in the embodiments of the present application may be implemented by electronic hardware, computer software, or a combination of the two. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the functions for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present application.

[0221] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0222] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0223] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0224] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processing module, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (control method), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0225] It should be understood that the various parts of the embodiments of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0226] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0227] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0228] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A denoising method, characterized in that: The method comprises: Obtaining a short frame image after color gamut conversion and a long frame image after color gamut conversion; The short frame image after the color gamut conversion and the long frame image after the color gamut conversion are respectively transformed through a window of a preset size to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image; Determining a window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image; A denoising calculation is performed on the effective signal within the window based on the window comprehensive coefficient.

2. The method according to claim 1, characterized in that The obtaining of the short frame image after the color gamut conversion and the long frame image after the color gamut conversion comprises: Performing alignment processing on the original short frame images to obtain short frame images, wherein the frame length of the short frame image is the same as the frame length of the long frame image; The color gamut conversion is performed on each channel of the short frame image and the long frame image respectively to obtain a short frame image after the color gamut conversion and a long frame image after the color gamut conversion.

3. The method according to claim 2, characterized in that The color gamut conversion is performed on each channel of the short frame image and the long frame image to obtain the short frame image after the color gamut conversion and the long frame image after the color gamut conversion, including: Respectively obtain four-channel RGB data of the short frame image and the long frame image; According to the preset conversion matrix and the RGB data of a single channel, the difference data of local brightness, single channel and other channels are obtained; Generate the short frame image after the color gamut conversion according to the local brightness corresponding to the short frame image and the difference data between the single channel and other channels; The long frame image after the color gamut conversion is generated according to the local brightness corresponding to the long frame image and the difference data between the single channel and other channels.

4. The method according to claim 3, characterized in that The method further comprises: Preprocessing operations are performed on the short frame image after color gamut conversion and the long frame image after color gamut conversion respectively, and the preprocessing operation includes any one of Gaussian convolution and dilated convolution.

5. The method according to claim 1, characterized in that The transform coefficients include: discrete cosine transform DCT coefficients; The transforming process is performed on the short frame image after the color gamut conversion and the long frame image after the color gamut conversion respectively through a window of a preset size to obtain the transform coefficient of the short frame image and the transform coefficient of the long frame image, including: Determining the window of the preset size; The short frame image after the color gamut conversion and the long frame image after the color gamut conversion are respectively transformed along the vertical direction and the horizontal direction according to a window of a preset size to obtain the transformation coefficient of the short frame image and the transformation coefficient of the long frame image.

6. The method according to claim 5, characterized in that Determining the window comprehensive coefficient according to the transformation coefficient of the short frame image and the transformation coefficient of the long frame image includes: Calculating the signal-to-noise ratio in the frequency domain according to the transformation coefficient of the long frame image to obtain a signal-to-noise ratio coefficient; The signal-to-noise ratio coefficient is attenuated based on the signal-to-noise ratio coefficient and the transformation coefficient of the short frame image to obtain a window comprehensive coefficient.

7. The method according to claim 1, characterized in that The performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient comprises: Accumulating the signal-to-noise ratio coefficients to obtain accumulated signal-to-noise ratio coefficients; Performing an inverse transformation on the window comprehensive coefficient to obtain an inverse transformed window comprehensive coefficient; The effective signal in the window is de-noised according to the inversely transformed window comprehensive coefficient and the accumulated signal-to-noise ratio coefficient.

8. The method according to any one of claims 1 to 7, characterized in that After performing denoising calculation on the effective signal in the window based on the window comprehensive coefficient, the method further includes: Perform inverse transform on the denoised image and output the final processed image.

9. A denoising device, characterized in that: The device comprises: An acquisition unit, used for acquiring a short frame image after color gamut conversion and a long frame image after color gamut conversion; A transformation unit, used to transform the short frame image after the color gamut conversion and the long frame image after the color gamut conversion through a window of a preset size, respectively, to obtain a transformation coefficient of the short frame image and a transformation coefficient of the long frame image; A processing unit, configured to determine a window comprehensive coefficient according to a transformation coefficient of the short frame image and a transformation coefficient of the long frame image; The denoising unit is used to perform denoising calculation on the effective signal in the window based on the window comprehensive coefficient.

10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

12. A chip, characterized in that: It comprises one or more interfaces and one or more processors; the interface is used to receive a signal from a memory of an electronic device and send the signal to the processor, the signal includes a computer instruction stored in the memory, and when the processor executes the computer instruction, the electronic device executes the method described in any one of claims 1 to 8.

Images