Image processing method, device, electronic device and storage medium

Abstract

The present disclosure relates to an image processing method, apparatus, electronic device, and storage medium. The image processing method includes: obtaining an image to be processed; denoising the image to be processed according to a preset denoising method to determine a denoised image; filtering a preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels; superimposing the denoised image according to the at least two frequency information components and preset adjustment parameters to obtain a sharpened image; and determining a target image based on the sharpened image. This image processing method, while filtering out image noise, integrates image details into the sharpened image, enriching the image's detailed information, optimizing the denoising effect and image resolution during image processing, and improving the quality of the processed image.

Description

Technical Field

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

[0002] Image resolution and clarity enhancement technology, as the final step in imaging technology, has a significant impact on the final image presentation. Related technologies often enhance image resolution but often result in loss of detail. Furthermore, when extracting high-frequency information from an image, it's easy to mistake high-frequency noise for useful high-frequency information, resulting in excessive noise in the processed image, negatively impacting image quality. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides an image processing method, apparatus, electronic device and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, there is provided an image processing method, applied to an electronic device, the image processing method comprising:

[0005] Get the image to be processed;

[0006] Performing denoising on the image to be processed according to a preset denoising method to determine a denoised image;

[0007] performing filtering processing on a preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels respectively;

[0008] Performing a superposition process on the denoised image according to the at least two frequency information components and the preset adjustment parameters to obtain a sharpened image;

[0009] A target image is determined according to the sharpened image.

[0010] Optionally, the filtering process is performed on the preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, including:

[0011] The preset area image of the denoised image is filtered using at least two filters selected from a high-pass filter, a medium-high-pass filter, and a band-pass filter to obtain at least two of the high-frequency information components, the medium-high-frequency information components, and the medium-frequency information components of the denoised image corresponding to the at least two filters.

[0012] Optionally, the preset area image includes an edge image and / or a detail area image of the denoised image.

[0013] Optionally, performing superposition processing on the denoised image according to the at least two frequency information components and preset adjustment parameters to obtain a sharpened image includes:

[0014] For the at least two frequency information components, respectively adjust the gain parameter of each frequency information component in the preset adjustment parameter, determine the frequency optimization information component corresponding to each frequency information component, and obtain at least two frequency optimization information components;

[0015] The at least two frequency optimization information components are superimposed on the denoised image to obtain the sharpened image.

[0016] Optionally, superimposing the at least two frequency optimization information components onto the denoised image to obtain the sharpened image includes:

[0017] Determining a component threshold corresponding to each frequency optimization information component;

[0018] Within the range of the component threshold, the frequency optimization information components corresponding to the component threshold in the at least two frequency optimization information components are sequentially superimposed on the denoised image to determine a sharpened image.

[0019] Optionally, the preset adjustment parameters include at least one of the following adjustment parameters: a brightness parameter, a spatial frequency parameter, a light-dark contrast parameter, a local histogram parameter, a local pixel method parameter, and a local pixel uniformity parameter.

[0020] Optionally, the method further includes:

[0021] Determining a differential image based on the image to be processed and the denoised image; wherein the differential image includes image detail information;

[0022] Determining a target image according to the sharpened image includes:

[0023] Image processing is performed based on the sharpened image, noise information and differential image to determine the target image.

[0024] Optionally, determining image detail information according to the image to be processed and the denoised image includes:

[0025] The pixel value of each pixel in the image to be processed is subtracted from the pixel value of the corresponding pixel in the denoised image to obtain a differential image.

[0026] Optionally, performing image processing according to the sharpened image, the noise information, and the differential image to determine the target image includes:

[0027] determining the image detail information according to the differential image and the noise information;

[0028] Determining the weight of each pixel in the image detail information;

[0029] Determining supplementary pixel parameters based on the pixel parameters of each pixel in the image detail information and the weight corresponding to the pixel;

[0030] According to the supplementary pixel parameters, the supplementary pixel points are superimposed on the sharpened image to obtain the target image.

[0031] According to a second aspect of an embodiment of the present disclosure, there is provided an image processing apparatus, applied to an electronic device, the image processing apparatus comprising:

[0032] An image acquisition module, used for acquiring an image to be processed;

[0033] An image denoising module is used to perform denoising on the image to be processed according to a preset denoising method to determine a denoised image;

[0034] an image filtering module, configured to perform filtering processing on a preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels respectively;

[0035] an image sharpening module, configured to perform a superposition process on the denoised image according to the at least two frequency information components and a preset adjustment parameter to obtain a sharpened image;

[0036] The image determination module is used to determine a target image based on the sharpened image.

[0037] Optionally, the image filtering module is specifically configured to:

[0038] The preset area image of the denoised image is filtered using at least two filters selected from a high-pass filter, a medium-high-pass filter, and a band-pass filter to obtain at least two of the high-frequency information components, the medium-high-frequency information components, and the medium-frequency information components of the denoised image corresponding to the at least two filters.

[0039] Optionally, the preset area image includes an edge image and / or a detail area image of the denoised image.

[0040] Optionally, the image sharpening module is specifically configured to:

[0041] For the at least two frequency information components, respectively adjust the gain parameter of each frequency information component in the preset adjustment parameter, determine the frequency optimization information component corresponding to each frequency information component, and obtain at least two frequency optimization information components;

[0042] The at least two frequency optimization information components are superimposed on the denoised image to obtain the sharpened image.

[0043] Optionally, the image sharpening module is further configured to:

[0044] Determining a component threshold corresponding to each frequency optimization information component;

[0045] Within the range of the component threshold, the frequency optimization information components corresponding to the component threshold in the at least two frequency optimization information components are sequentially superimposed on the denoised image to determine a sharpened image.

[0046] Optionally, the preset adjustment parameters include at least one of the following adjustment parameters: a brightness parameter, a spatial frequency parameter, a light-dark contrast parameter, a local histogram parameter, a local pixel method parameter, and a local pixel uniformity parameter.

[0047] Optionally, the image denoising module is further configured to determine a differential image based on the image to be processed and the denoised image; wherein the differential image includes image detail information;

[0048] The image determination module is specifically used to perform image processing based on the sharpened image, noise information and differential image to determine the target image.

[0049] Optionally, the image denoising module is further configured to:

[0050] The pixel value of each pixel in the image to be processed is subtracted from the pixel value of the corresponding pixel in the denoised image to obtain a differential image.

[0051] Optionally, the image determination module is specifically configured to:

[0052] determining the image detail information according to the differential image and the noise information;

[0053] Determining the weight of each pixel in the image detail information;

[0054] Determining supplementary pixel parameters based on the pixel parameters of each pixel in the image detail information and the weight corresponding to the pixel;

[0055] According to the supplementary pixel parameters, the supplementary pixel points are superimposed on the sharpened image to obtain the target image.

[0056] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0057] processor;

[0058] a memory for storing processor-executable instructions;

[0059] Wherein, the processor is configured to execute the image processing method as described above.

[0060] According to a fourth aspect of an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the image processing method as described above.

[0061] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: when performing image processing, the image to be processed is first denoised to obtain a denoised image, and then the denoised image is sharpened. This can avoid the problem of increasing the intensity of noise while sharpening the image, thereby achieving effective control and suppression of noise.

[0062] During the sharpening process, at least two frequency components are processed separately, fully and richly restoring the details and texture of the processed image and enhancing image resolution. This image processing method not only filters out image noise but also sharpens the denoised image at at least two preset frequency levels, enriching the image's detail information, optimizing the denoising effect and image resolution during the image processing process, and improving the quality of the processed image.

[0063] 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 disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0065] Figure 1 The figure is a flowchart of an image processing method according to an exemplary embodiment.

[0066] Figure 2 The figure is a flowchart of an image processing method according to an exemplary embodiment.

[0067] Figure 3 The figure is a flowchart of an image processing method according to an exemplary embodiment.

[0068] Figure 4 The figure is a flowchart of an image processing method according to an exemplary embodiment.

[0069] Figure 5 is a block diagram of an image processing apparatus according to an exemplary embodiment.

[0070] Figure 6 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0071] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0072] In response to the problems of detail loss, excessive noise, and reduced resolution during image processing in related technologies, the present disclosure provides an image processing method for use in electronic devices capable of image processing. This image processing method, when performing image processing, first performs denoising on the image to be processed to obtain a denoised image. The denoised image is then sharpened, which can avoid the enhancement of noise during sharpening and achieve effective control and suppression of noise. During the sharpening process, multiple frequency information components are processed separately, which can fully and richly restore the details and texture of the image to be processed, enhance the image resolution, and then obtain a target image with rich detail expression and a soft picture based on the sharpened image. The image processing method of the present disclosure filters the denoised image at at least two frequency levels, and then optimizes and adjusts the frequency information components at different frequency levels to obtain a sharpened image, and ultimately obtains the target image. Since the frequency information components at different frequency levels are adjusted using corresponding preset adjustment parameters, the image's detail information and image resolution are enriched, the image is softer, and the quality of the processed image is improved.

[0073] In an exemplary embodiment, an image processing method is provided, referring to Figure 1 As shown, the image processing method includes:

[0074] S110: Acquire an image to be processed.

[0075] An image processing device for implementing the image processing method obtains an image to be processed sent by another device, including: the image processing device sends a request to obtain the image to be processed, and then the other device sends the image to be processed to the image processing device in response to the request, and the image processing device receives the image to be processed sent by the other device; or, the other device to be processed directly sends the image to be processed to the image processing device, the image processing device receives the image to be processed, and performs subsequent image processing.

[0076] S120 , performing denoising on the image to be processed according to a preset denoising method to determine a denoised image.

[0077] The goal of image sharpening is essentially to enhance the high-frequency components of the processed image. Common methods can be categorized as spatial domain image sharpening and frequency domain image sharpening. Spatial domain image sharpening uses spatial filtering to extract high-frequency information parameters from the image, which are used to identify image features. By controlling the intensity of these parameters, this high-frequency information is superimposed on the processed image. Frequency domain image sharpening transforms the processed image into the frequency domain, extracts high-frequency information within a certain range, and superimposes it on the frequency domain image, ultimately restoring the spatial image. Both methods suffer from the drawback that they can easily misinterpret high-frequency noise as useful high-frequency information and superimpose it on the processed image, thereby amplifying and intensifying the noise and negatively impacting image quality. Therefore, to improve the display quality of the processed image, while controlling the intensity of image sharpening, it is still necessary to appropriately suppress and control noise to enhance the fineness of image edges and details.

[0078] In this step of the present disclosure, denoising is performed before sharpening the image to reduce the impact of noise on the image. The preset denoising method includes at least one of the following methods: NLM denoising method, bilateral filtering denoising method, Gaussian filtering denoising method, and guided filtering denoising method.

[0079] Different denoising methods produce different denoising effects on different images. When the preset denoising methods include two or more of the above methods, the appropriate denoising method can be automatically selected based on the denoising results of the different denoising methods. Of course, users can also pre-select the appropriate denoising method based on the type of image to be processed.

[0080] In one example, the preset denoising method is: NLM denoising method (non-local mean denoising method), which filters out the noise of the original image in combination with the image spatial distribution. After denoising processing, noise information, image detail information and denoised image are finally obtained.

[0081] The image spatial distribution refers to the two-dimensional distribution of the image. When performing denoising, the spatial and intensity information of the noise in the image to be processed is first determined. Then, based on this information, the corresponding image detail information is separated from the image to be processed. Specifically, different image detail information is separated based on the different spatial locations of the noise in the image to be processed and the different noise intensities.

[0082] S130. Perform filtering processing on the preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels respectively.

[0083] The above-mentioned image enhancement technology based on the USM method is an algorithm for adjusting the sharpening intensity of the overall image. It cannot effectively adjust the local image and the algorithm is not flexible enough. Moreover, while enhancing the details, it is easy to cause excessive enhancement of the high-frequency information area, resulting in halo at the edges, affecting the sharpening effect of the image.

[0084] In view of this, in the image processing method disclosed herein, filtering processing is performed on the preset area images of the denoised image at at least two preset frequency levels, and then sharpening processing is performed respectively.

[0085] In image processing technology, the greater the gradient of adjacent pixel points in an image, the greater the difference between the two adjacent pixel points, and the higher the image frequency of the latter pixel point relative to the previous pixel point. Based on this, the preset area image of the denoised image can be filtered at at least two preset frequency levels to obtain at least two frequency information components corresponding to the above-mentioned at least two frequency levels.

[0086] In one example, a predetermined region of a denoised image can be filtered at two frequency levels: a first frequency level and a second frequency level, where the frequencies of the two frequency levels decrease in sequence. By filtering the denoised image at the two frequency levels, information components at the first frequency level and information components at the second frequency level are obtained.

[0087] It should be noted that the first frequency level and the second frequency level may both belong to any frequency band of high frequency, medium high frequency and medium frequency, or may respectively belong to different frequency bands.

[0088] The filtering process can be implemented by using existing filters, such as a high-pass filter for high-frequency filtering, a medium-high-pass filter for medium-high-frequency filtering, and a medium-pass filter for medium-frequency filtering.

[0089] Here, it should be noted that the pixel points are represented by a matrix, and the brightness value, RGB and other parameters are all included in the matrix. If the gradient between the two matrices is larger, the frequency at that position is higher. At the same time, the frequency between two adjacent pixel points is determined according to a preset rule, such as from top to bottom, from left to right, etc. in the two-dimensional plane of the image. Assume that two pixel points A and pixel point B have the same parameters except for the brightness parameter. According to the preset rule, pixel point A is ranked before pixel point B, and the brightness value of pixel point A is greater than the brightness value of pixel point B. Then the frequency of pixel point B relative to A is positive, and the frequency of pixel point A relative to pixel point B is negative.

[0090] The preset area image includes an edge image and / or a detail area image of the denoised image, so as to obtain the edge and / or detail information components in the denoised image through filtering, and then optimize the edge and detail images of the denoised image. For example, the detail area image is a feature image of a preset ratio threshold of the target object in the image; wherein the preset ratio threshold can be adaptively adjusted according to the content in the image. In one example, when shooting a landscape, the target object in the area with a ratio of 1:1000 in the landscape image can be used as a detail area image, such as a tree or a leaf in the landscape. The preset ratio threshold is 1:1000, which can be understood as dividing an entire image into 1000 parts, and the image in the area where one of the parts is located can be considered as a detail area image; it should be noted that the setting of the ratio parameter in this example is for the convenience of more intuitive explanation, and is not a restriction on the actual setting of the ratio parameter.

[0091] In another example, when the image to be processed includes a portrait, the target object in the area with a ratio of 1:50 can be used as the detail area image. The detail area is, for example, the area where the eyes, teeth, eyelashes and other detail parts are located, and the image of this area is the detail area image.

[0092] In another example, when the image to be processed includes a flower, the preset ratio threshold may be 1:10000, and the detail area may be an area of ​​detail parts such as the stamens, petal patterns, and petal shadows, and the image of this area is the detail area image.

[0093] In one example, filtering the image of a predetermined region of the denoised image at at least two frequency levels to determine multiple frequency information components of the denoised image includes:

[0094] The preset area image of the denoised image is filtered using at least two filters selected from a high-pass filter, a medium-high-pass filter, and a band-pass filter to obtain at least two of a high-frequency information component, a medium-high-frequency information component, and a medium-frequency information component of the denoised image.

[0095] For example, three Laplace filters with different frequency levels are designed for the denoised image to extract the high-frequency information components, medium-high-frequency information components and medium-frequency information components at the edges and details of the denoised image respectively, forming a multi-frequency level information component coverage, and then achieving the enhancement and improvement of the image quality of the details and edges at different frequency levels.

[0096] S140 . Perform superposition processing on the denoised image according to at least two frequency information components and preset adjustment parameters to obtain a sharpened image.

[0097] Image resolution enhancement, also known as image sharpening, is a method for sharpening blurred image edges and enhancing image details. In situations such as inaccurate focus or zooming, the captured image will have varying degrees of blur. Image sharpening is necessary to compensate for the outlines of objects in the image, highlighting edges and details for a clearer image.

[0098] In this step, after determining at least two frequency information components, each frequency information component is adjusted using preset adjustment parameters. Different frequency information components can be adjusted differently, which can not only achieve the effect of enhancing image display quality, but also effectively control and suppress noise, thereby improving image resolution.

[0099] Among them, the preset adjustment parameters include at least one of the following adjustment parameters: brightness parameter, spatial frequency parameter, light and dark contrast parameter, local histogram parameter, local pixel method parameter and local pixel uniformity parameter. Different adjustment effects can be achieved through the above-mentioned different adjustment parameters and different combinations of adjustment parameters. When the preset adjustment parameters include a variety of the above-mentioned adjustment parameters, appropriate adjustment parameters or combinations of adjustment parameters can be selected as needed to achieve the corresponding adjustment purpose. The selection of the adjustment parameters in the preset adjustment parameters can be selected through a preset algorithm or by the user as needed. When the adjustment parameters set in the image processing device include a brightness parameter, the preset algorithm is, for example, when it is determined that the noise of the brightness parameter in the image to be processed or the denoised image is large, the brightness parameter is used as the preset adjustment parameter to adjust the image.

[0100] For example, when at least two frequency information components include a high-frequency information component, when the high-frequency information component is adjusted by preset adjustment parameters, the enhancement adjustment of the high-frequency information component can be appropriately reduced, or the high-frequency information component can be not enhanced to reduce the impact of high-frequency noise on image quality and avoid the generation of edge halo.

[0101] In one example, the at least two frequency information components include a high-frequency information component, a mid-high-frequency information component, and a mid-frequency information component, and the preset adjustment parameters include a brightness parameter, a spatial frequency parameter, and a light-dark contrast parameter.

[0102] In this example, during image processing, the luminance information, spatial frequency information, and light-dark contrast information of the denoised image in the spatial domain are determined. Based on the differences in these three horizontal dimensions, the multiple frequency information components at multiple frequency levels are comprehensively adjusted. For example, the high-frequency information components are basically not enhanced to avoid edge halos, the mid- and high-frequency information components are moderately enhanced, and the mid-frequency information components are enhanced proportionally. The use of multi-dimensional adjustment parameters to adjust the frequency information components at each level has high control accuracy and more flexible control methods. It can fully restore the details and texture of the image to be processed, enhance the image's resolution, and better avoid edge halos.

[0103] The user can adjust the enhancement ratio for different frequency components as needed. The user can also select an appropriate ratio based on the gradient between pixels. For example, when adjusting based on brightness parameters, the brightness parameter can be adjusted based on the gradient between pixels.

[0104] S150: Determine a target image based on the sharpened image.

[0105] After determining the sharpened image, the image quality of the sharpened image can be used to determine whether additional processing is required. If the image quality of the sharpened image meets the requirements, the sharpened image is determined as the target image. If the image quality of the sharpened image still does not meet the requirements, further processing is performed on the sharpened image.

[0106] For example, when a user has requirements for the size of an image, the sharpened image can be cropped to obtain a target image of a corresponding size to meet the user's needs.

[0107] In one example, when performing denoising on the image to be processed according to a preset denoising method, image detail information is also obtained. Determining the target image based on the sharpened image includes: performing image processing on the sharpened image and the image detail information, further improving the detail information of the sharpened image, and ultimately obtaining a target image with complete noise suppression and enhanced resolution.

[0108] It's important to note that noise information refers to interference that affects the overall image quality, such as excessive or dim brightness in certain areas of the image. Another example is blurry image information. These interfere with the overall image quality and detail, and are all considered noise information. Image detail information, in contrast to noise information, is information that enhances image detail and overall quality. Image detail information is useful image information that is removed along with noise during denoising of the image being processed. Useful image detail enhances the fineness of image detail and contributes to the expression of image detail; this is non-interference information.

[0109] In addition, when determining image information, a boundary threshold can be used, such as a brightness threshold or pixel threshold. When denoising an image, if the brightness of a region in the image exceeds the brightness threshold, the image information in that region is determined to be noise information. If all image information in the image falls within the corresponding boundary threshold, that portion of image information is considered image detail information or information in the sharpened image.

[0110] In another example, when denoising the image to be processed according to a preset denoising method, image detail information and noise information are also obtained. Determining the target image based on the sharpened image includes: performing image fusion processing on the sharpened image based on the noise information and image detail information obtained during the denoising process. That is, based on the noise information, image detail information originally separated from the image to be processed is restored to the sharpened image, further improving the detail information of the sharpened image, and ultimately obtaining a complete target image with noise suppression and enhanced resolution.

[0111] This image processing method suppresses noise during image processing through image denoising and image fusion, while further enhancing image details and edges. Specifically, it uses preset adjustment parameters to adjust the frequency information components at each level. This method offers high precision and flexibility, fully restoring the details and textures of the processed image and enhancing its resolution. This image processing method can effectively improve the overall image processing effect, resulting in higher-quality target images and a better user experience.

[0112] In an exemplary embodiment, an image processing method is provided, referring to Figure 2 As shown, the image processing method is a further optimization of the above step S140. Specifically, the denoised image is superimposed based on multiple frequency information components and preset adjustment parameters to determine the sharpened image, including:

[0113] S210: For at least two frequency information components, adjust the gain parameter of each frequency information component in the preset adjustment parameters respectively, determine the frequency optimization information component corresponding to each of the frequency information components, and obtain at least two frequency optimization information components.

[0114] The gain parameters of different preset adjustment parameters for each frequency information component are adjusted respectively to determine the frequency optimization information component of the preset adjustment parameter corresponding to the frequency information component.

[0115] In one example, the at least two frequency information components include a high-frequency information component, a mid-high-frequency information component, and a mid-frequency information component, and the preset adjustment parameters include a brightness parameter, a spatial frequency parameter, and a light-dark contrast parameter.

[0116] In the image processing method, the gain parameter of the brightness parameter of the high-frequency information component, the gain parameter of the spatial frequency parameter of the high-frequency information component, the gain parameter of the light-dark contrast parameter of the high-frequency information component, the gain parameter of the brightness parameter of the mid-high-frequency information component, the gain parameter of the spatial frequency parameter of the mid-high-frequency information component, the gain parameter of the light-dark contrast parameter of the mid-high-frequency information component, the gain parameter of the brightness parameter of the intermediate-frequency information component, the gain parameter of the spatial frequency parameter of the intermediate-frequency information component, and the gain parameter of the light-dark contrast parameter of the intermediate-frequency information component are adjusted respectively, thereby determining the high-frequency optimized information component corresponding to the brightness parameter of the high-frequency information component, the high-frequency optimized information component corresponding to the spatial frequency parameter of the high-frequency information component, the high-frequency optimized information component corresponding to the light-dark contrast parameter of the high-frequency information component, the mid-high-frequency optimized information component corresponding to the brightness parameter of the mid-high-frequency information component, the mid-high-frequency optimized information component corresponding to the spatial frequency parameter of the mid-high-frequency information component, the mid-high-frequency optimized information component corresponding to the light-dark contrast parameter of the mid-high-frequency information component, the intermediate-frequency optimized information component corresponding to the brightness parameter of the intermediate-frequency information component, the intermediate-frequency optimized information component corresponding to the spatial frequency parameter of the intermediate-frequency information component, and the intermediate-frequency optimized information component corresponding to the light-dark contrast parameter of the intermediate-frequency information component.

[0117] For the same adjustment parameter, different frequencies are optimized differently. For example, for brightness, the ratio of the high-frequency optimization information component to the high-frequency information component is the smallest, while the ratio of the mid-frequency optimization information component to the mid-frequency information component is the largest. This means that the adjustment strength is minimized for the high-frequency information component and maximized for the mid-frequency information component. This achieves the effect of enhancing image detail while avoiding edge haloing.

[0118] S220 , superimposing at least two frequency optimization information components onto the denoised image to obtain a sharpened image.

[0119] After determining the optimized information component corresponding to each frequency information component, each frequency optimized information component is superimposed on the denoised image. After the information superposition is completed, a sharpened image with enhanced edges and clear details can be obtained. The specific superposition method can be referred to the methods in the prior art and will not be described in detail here.

[0120] In an exemplary embodiment, an image processing method is provided, referring to Figure 3 As shown, the image processing method is a further improvement of step S220 in the above image processing method. Specifically, multiple frequency optimization information components are superimposed on the denoised image to determine the sharpened image, including:

[0121] S310: Determine a component threshold corresponding to each frequency optimization information component.

[0122] In this step, different component thresholds are set for different frequency optimization information components. Among them, different frequency optimization information components include different frequency levels and different adjustment parameters. Specifically, the component threshold is set separately for different frequency levels, and in the same frequency level, it is also set separately for different adjustment parameters. Then, during superposition, the number of superimposed information components is limited by a dynamic component threshold, so as to avoid superimposing too many optimization information components during the superposition process, which once again causes an over-enhanced edge halo effect. The dynamic component threshold refers to different specific adjustment thresholds for different frequency levels and different adjustment parameters.

[0123] For example, when at least two frequency optimization information components include an intermediate frequency optimization information component corresponding to the brightness parameter of the intermediate frequency information component, an intermediate frequency optimization information component corresponding to the spatial frequency parameter of the intermediate frequency information component, and an intermediate frequency optimization information component corresponding to the light and dark contrast parameter of the intermediate frequency information component, the component threshold corresponding to the intermediate frequency optimization information component includes adjustment thresholds for three adjustment parameters: brightness parameter, spatial frequency parameter, and light and dark contrast parameter. The superposition of the intermediate frequency optimization information components is controlled by multiple adjustment thresholds in the above-mentioned component thresholds, thereby achieving more refined image processing and more flexible control.

[0124] S320 . Within the range of the component threshold, sequentially superimpose the frequency optimization information components corresponding to the component threshold in at least two frequency optimization information components onto the denoised image to determine a sharpened image.

[0125] When superimposing at least two frequency-optimized information components onto the denoised image, a component threshold controls the upper limit of the corresponding optimized information components that are superimposed. Specifically, when the corresponding optimized information component is less than or equal to the corresponding component threshold, the entire optimized information component is superimposed onto the denoised image; when the corresponding optimized information component is greater than the corresponding component threshold, the optimized information component corresponding to the component threshold is superimposed onto the denoised image. Setting the component threshold prevents the superposition of too many optimized information components, which would otherwise cause an overly enhanced edge halo effect. This image processing method produces a sharpened image with enhanced edges and clear details.

[0126] In an example, taking the brightness parameter as an example, the brightness value of pixel A in the denoised image is 50, and the frequency optimization information component corresponding to the pixel in the brightness parameter dimension is 15, but the component threshold corresponding to the pixel in the brightness parameter dimension is 10. Then, in the sharpened image, the brightness value of pixel A is 50+10=60.

[0127] In another example, still taking the brightness parameter as an example, the brightness value of pixel B in the denoised image is 50, the frequency optimization information component corresponding to the pixel in the brightness parameter dimension is 5, and the component threshold corresponding to the pixel in the brightness parameter dimension is 10. Then, in the sharpened image, the brightness value of pixel B is 50+5=55.

[0128] In addition, it should be noted that the component threshold includes a positive component threshold and a negative component threshold. Whether adding or subtracting the optimization information component on the basis of the pixel points of the denoised image, it cannot exceed the corresponding positive component threshold or negative component threshold.

[0129] In an exemplary embodiment, an image processing method is provided, which is a further optimization of the above-mentioned image processing method. Specifically, it includes:

[0130] A differential image is determined based on the image to be processed and the denoised image; then image processing is performed based on the sharpened image, noise information and the differential image to determine the target image.

[0131] In one example, the pixel value of each pixel in the processed image is subtracted from the pixel value of the corresponding pixel in the denoised image to obtain a difference image, where the difference image contains image detail information. Image processing, such as image fusion, is performed on the sharpened image and the image detail information to further improve the detail information of the sharpened image, ultimately obtaining a target image with complete noise suppression and enhanced resolution.

[0132] In one example, the differential image includes image detail information and noise information, where the noise information includes noise location information and noise intensity information. Specifically, during denoising, the noise location information and noise intensity information are first determined. Then, based on the noise location information and noise intensity information, the differential image is separated from the image to be processed. The noise information containing the noise location information and noise intensity information is also extracted for subsequent use. When image fusion is subsequently performed, the differential image is fused with the sharpened image based on the noise location information and noise intensity information extracted from the noise information during the denoising process to enhance the image's detail representation.

[0133] When the difference image is subsequently fused with the sharpened image, different processing is required for image detail information and noise information. Image detail information is information that helps improve the appearance of image detail, i.e., non-interference information; noise information is interference information that has a negative effect on improving image detail. Since the noise location and noise intensity information have been determined during the denoising process of the processed image, and the difference image contains both noise and image detail information, it is possible to clearly determine which information is noise and which is image detail information during image fusion. Therefore, when fusing the difference image with the sharpened image, the location of the noise information is first used to determine whether the information to be fused is interference information or image detail information. If the information is image detail information, different fusion methods can be used based on the frequency level of each image detail. For example, if the image detail information is at a high frequency level, the optimization component of this part can be reduced to achieve more refined image details and avoid overly abrupt details. For example, if the image detail information belongs to the low-frequency level, then in order to avoid this part of the details being too dim relative to other details, the optimization component of this part can be enhanced during fusion.

[0134] As for interference information, that is, noise information, since the location and intensity of the noise information are already known, different fusion strategies can be adopted according to different noise intensities. In one example, if the intensity of the noise information in a certain area is high, for example, it is affected by external light when taking pictures, causing the area to be too bright, then during the image fusion process, this part of the noise can be not fused to avoid affecting the expression of image details. In another example, if the noise information intensity in a certain area is low, for example, there are only some slight blurs, which still contain some details, then during the image fusion process, this part of the noise information can be optimized and fused into the sharpened image to further enhance the fineness of the image details.

[0135] In one example, the NLM denoising method is used to denoise the image to be processed. The difference image separated from the image to be processed contains some tiny detail information and edge information lost during denoising. Under the guidance of noise position information and noise intensity information, the difference image is added back to the sharpened image to further enhance the image detail performance.

[0136] In an exemplary embodiment, an image processing method is provided, referring to Figure 4 As shown in FIG, this image processing method is a further optimization of the above image processing method. Specifically, it includes:

[0137] S410: Determine the weight of each pixel in the image detail information.

[0138] The weight of each pixel refers to its importance to the image. Generally speaking, the greater the absolute value of a pixel's weight, the greater its impact on the image, and the greater the effect of adjusting that pixel on the image. Therefore, determining the weight of each pixel in each image detail can effectively determine the extent of that pixel's influence on the image being processed, facilitating subsequent image fusion processing and improving the quality of the target image.

[0139] S420: Determine supplementary pixel parameters according to the pixel parameters of each pixel in the image detail information and the weight corresponding to the pixel.

[0140] For example, the image detail information is added back to the sharpened image pixel by pixel through weighted weighting to further enhance the image detail performance.

[0141] Among them, for pixels with larger weights, more supplementary pixels can be appropriately set; for pixels with smaller weights, fewer supplementary pixels can be appropriately set.

[0142] In one example, the weight of pixel C in the image detail information is 20, and the weight of pixel D is 30, indicating that pixel D has a greater impact on image quality. That is, more pixel D in the image detail information needs to be added to the sharpened image. In this case, the supplementary pixel parameter of pixel C can be set to 10, and the supplementary pixel parameter of pixel D can be set to 20. That is, 10 pixel Cs are added to the corresponding pixel positions of the sharpened image, and 20 pixel Ds are added to the corresponding pixel positions of the sharpened image to improve the quality of the final image.

[0143] It should be noted that the above examples are only for illustrating the correspondence between the supplementary pixel parameters and the pixel weights. The supplementary pixel parameters do not necessarily refer to the number of pixels that need to be supplemented, but may also refer to other parameters that can reflect the number of supplementary pixels.

[0144] S430 , superimposing the supplementary pixel points onto the sharpened image according to the supplementary pixel point parameters to obtain a target image.

[0145] Since some image detail information of the image to be processed may be removed during the denoising process, the loss of image detail information will inevitably affect the image processing effect. In order to reduce the impact of some image detail information removed during the denoising process on the image processing effect, it is necessary to re-superimpose the image detail information onto the sharpened image.

[0146] Because the lost image detail information is separated out along with the noise, the image detail information can be restored to the sharpened image based on the noise information.

[0147] In one example, the noise information includes noise position information and noise intensity information. When performing image fusion processing, under the guidance of the noise position information and noise intensity information, the supplementary pixels are added back to the sharpened image to obtain the final target image, further enhancing the image detail performance and improving the image quality.

[0148] In another example, the specific process of the image processing method is as follows: the acquired image to be processed is denoised, a differential image with image detail information is separated from the image to be processed, and noise information including noise position information and noise intensity information is determined. The denoised image is then filtered through three different frequency levels to obtain the high-frequency information component, medium-high-frequency information component, and medium-frequency information component of the denoised image. The denoised image is then statistically analyzed for its brightness parameters, spatial frequency parameters, and light-dark contrast parameters. The information components of different frequency levels are adjusted in the above three parameter dimensions to effectively avoid the edge halo effect caused by excessive enhancement of the high-frequency information position of the denoised image. The adjusted optimized information components of different frequencies are then superimposed on the denoised image to obtain a preliminary sharpened image. Thereafter, based on the noise position information and noise intensity information determined during the denoising process, the differential image separated during the denoising process is fused pixel by pixel with the sharpened image to finally obtain a complete target image with noise suppression and enhanced resolution.

[0149] In an exemplary embodiment, an image processing apparatus is provided, referring to Figure 1 and 5 As shown, the image processing device includes an image acquisition module 101, an image denoising module 102, an image filtering module 103, an image sharpening module 104 and an image determination module 105. The image processing device in this embodiment is used to implement the above-mentioned image processing method. In the implementation process,

[0150] An image acquisition module 101 is used to acquire an image to be processed;

[0151] An image denoising module 102 is configured to perform denoising on the image to be processed according to a preset denoising method to determine a denoised image;

[0152] The preset denoising method includes at least one of the following methods: NLM denoising method, bilateral filtering denoising method, Gaussian filtering denoising method and guided filtering denoising method;

[0153] An image filtering module 103 is configured to filter the image of a preset region of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels respectively;

[0154] The image filtering module 103 is specifically configured to filter the image of the preset region of the denoised image using at least two filters selected from a high-pass filter, a medium-high-pass filter, and a band-pass filter to obtain at least two of a high-frequency information component, a medium-high-frequency information component, and a medium-frequency information component of the denoised image.

[0155] The preset area image includes an edge image and / or a detail area image of the denoised image;

[0156] An image sharpening module 104 is configured to perform a superposition process on the denoised image based on at least two frequency information components and a preset adjustment parameter to obtain a sharpened image;

[0157] The preset adjustment parameters include at least one of the following adjustment parameters: a brightness parameter, a spatial frequency parameter, a light-dark contrast parameter, a local histogram parameter, a local pixel method parameter, and a local pixel uniformity parameter;

[0158] The image determination module 105 is configured to determine a target image based on the sharpened image.

[0159] In an exemplary embodiment, an image processing apparatus is provided, referring to Figure 2 and 5 As shown, the image processing device is an improvement of the above image processing device, and is mainly used to implement the above image processing method. During the implementation process, the image sharpening module 104 is specifically used to:

[0160] For at least two frequency information components, respectively adjust the gain parameter of each frequency information component in the preset adjustment parameter, determine the frequency optimization information component corresponding to each frequency information component, and obtain at least two frequency optimization information components;

[0161] At least two frequency-optimized information components are superimposed on the denoised image to obtain a sharpened image.

[0162] In an exemplary embodiment, an image processing device is provided. The image processing device is an improvement of the above image processing device, and is mainly used to implement the above Figure 2 In the implementation process, the image sharpening module 104 is further used to:

[0163] Determining a component threshold corresponding to each frequency optimization information component;

[0164] Within the range of the component threshold, the frequency optimization information components corresponding to the component threshold in at least two frequency optimization information components are sequentially superimposed on the denoised image to determine a sharpened image.

[0165] In an exemplary embodiment, an image processing apparatus is provided, referring to Figure 5 As shown, the image processing device is an improvement of the above image processing device, and is mainly used to implement the above image processing method. During the implementation process,

[0166] The image denoising module 102 is further configured to determine image detail information based on the image to be processed and the denoised image;

[0167] Specifically, it is used to subtract the pixel value of each pixel point in the image to be processed from the pixel value of the corresponding pixel point in the denoised image to obtain a differential image; wherein the differential image includes image detail information.

[0168] The image determination module 105 is further configured to perform image processing based on the sharpened image and the image detail information to determine a target image.

[0169] In an exemplary embodiment, an image processing apparatus is provided, referring to Figure 4 and 5 As shown, the image processing device is an improvement of the above image processing device, and is mainly used to implement the above image processing method. During the implementation process, the image determination module 105 is specifically used to:

[0170] Determine the weight of each pixel in the image detail information;

[0171] Determine the supplementary pixel parameters according to the pixel parameters of each pixel in the image detail information and the weight corresponding to the pixel;

[0172] According to the supplementary pixel parameters, the supplementary pixels are added to the sharpened image to obtain the target image.

[0173] In one exemplary embodiment, reference Figure 6 As shown, an electronic device is provided. For example, the electronic device 400 can be a mobile phone, a computer, a tablet device, a camera, a video camera, or other device with an image processing function.

[0174] Electronic device 400 may include one or more of the following components: a processing component 402 , a memory 404 , a power component 406 , a multimedia component 408 , an audio component 410 , an input / output (I / O) interface 412 , a sensor component 414 , and a communication component 416 .

[0175] The processing component 402 generally controls the overall operation of the device 400, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the image capture method and / or image processing method described above. In addition, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.

[0176] The memory 404 is configured to store various types of data to support operations on the device 400. Examples of such data include instructions for any application or method operating on the device 400, contact data, phone book data, messages, pictures, videos, etc. The memory 404 can 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.

[0177] The power component 406 provides power to the various components of the device 400. The power component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 400.

[0178] The multimedia component 408 includes a screen that provides an output interface between the device 400 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 touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front camera and / or a rear camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0179] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC) that is configured to receive external audio signals when the device 400 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 404 or transmitted via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.

[0180] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0181] The sensor assembly 414 includes one or more sensors for providing various aspects of status assessment for the electronic device 400. For example, the sensor assembly 414 can detect the open / closed state of the electronic device 400, the relative positioning of components, such as the display and keypad of the electronic device 400. The sensor assembly 414 can also detect changes in the position of the device 400 or a component of the electronic device 400, the presence or absence of user contact with the device 400, the orientation or acceleration / deceleration of the device 400, and temperature changes of the device 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0182] The communication component 416 is configured to facilitate wired or wireless communication between the device 400 and other devices. The device 700 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 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 416 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.

[0183] In an exemplary embodiment, the electronic device 400 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-described methods.

[0184] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions. The instructions are executable by the processor 420 of the device 400 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the above-described image processing method.

[0185] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0186] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An image processing method, applied to electronic equipment, characterized in that: The image processing method comprises: Get the image to be processed; Performing denoising on the image to be processed according to a preset denoising method to determine a denoised image; performing filtering processing on a preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels respectively; Performing a superposition process on the denoised image according to the at least two frequency information components and the preset adjustment parameters to obtain a sharpened image; determining a target image according to the sharpened image; The method further comprises: Determining a differential image based on the image to be processed and the denoised image; wherein the differential image includes image detail information and noise information, the noise information includes noise position information and noise intensity information, and the image detail information is useful image information that is removed along with the noise when denoising the image to be processed; Determining a target image according to the sharpened image includes: Performing image processing based on the sharpened image, the noise information, and the differential image to determine a target image; Determining a differential image based on the image to be processed and the denoised image includes: Subtract the pixel value of each pixel in the image to be processed from the pixel value of the corresponding pixel in the denoised image to obtain a difference image; The performing image processing according to the sharpened image, the noise information and the differential image to determine the target image includes: determining the image detail information in the differential image according to the noise position information and the noise intensity information; Determining the weight of each pixel in the image detail information; Determining supplementary pixel parameters based on the pixel parameters of each pixel in the image detail information and the weight corresponding to the pixel; According to the supplementary pixel parameters, the supplementary pixel points are superimposed on the sharpened image to obtain the target image.

2. The image processing method according to claim 1, wherein: The filtering process is performed on the preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, including: The preset area image of the denoised image is filtered using at least two filters selected from a high-pass filter, a medium-high-pass filter, and a band-pass filter to obtain at least two of the high-frequency information components, the medium-high-frequency information components, and the medium-frequency information components of the denoised image corresponding to the at least two filters.

3. The image processing method according to claim 1 or 2, characterized in that: The preset area image includes an edge image and / or a detail area image of the denoised image.

4. The image processing method according to claim 1, wherein: The step of performing superposition processing on the denoised image according to the at least two frequency information components and the preset adjustment parameters to obtain a sharpened image includes: For the at least two frequency information components, respectively adjust the gain parameter of each frequency information component in the preset adjustment parameter, determine the frequency optimization information component corresponding to each frequency information component, and obtain at least two frequency optimization information components; The at least two frequency optimization information components are superimposed on the denoised image to obtain the sharpened image.

5. The image processing method according to claim 4, characterized in that The step of superimposing the at least two frequency optimization information components onto the denoised image to obtain the sharpened image includes: Determining a component threshold corresponding to each frequency optimization information component; Within the range of the component threshold, the frequency optimization information components corresponding to the component threshold in the at least two frequency optimization information components are sequentially superimposed on the denoised image to determine a sharpened image.

6. The image processing method according to any one of claims 1, 4 or 5, characterized in that: The preset adjustment parameters include at least one of the following adjustment parameters: a brightness parameter, a spatial frequency parameter, a light-dark contrast parameter, a local histogram parameter, a local pixel method parameter, and a local pixel uniformity parameter.

7. An image processing device, applied to an electronic device, characterized in that: The image processing device comprises: An image acquisition module, used for acquiring an image to be processed; An image denoising module is used to perform denoising on the image to be processed according to a preset denoising method to determine a denoised image; an image filtering module, configured to perform filtering processing on a preset region image of the denoised image at at least two preset frequency levels to obtain at least two frequency information components of the denoised image, wherein the at least two frequency information components correspond to the at least two preset frequency levels respectively; an image sharpening module, configured to perform a superposition process on the denoised image according to the at least two frequency information components and a preset adjustment parameter to obtain a sharpened image; An image determination module, configured to determine a target image based on the sharpened image; The image denoising module is further configured to determine a difference image based on the image to be processed and the denoised image; wherein the difference image includes image detail information and noise information, the noise information includes noise location information and noise intensity information, and the image detail information is useful image information that is removed along with the noise when denoising the image to be processed; The image determination module is specifically configured to perform image processing based on the sharpened image, noise information, and differential image to determine the target image; The image denoising module is further used to: Subtract the pixel value of each pixel in the image to be processed from the pixel value of the corresponding pixel in the denoised image to obtain a difference image; The image determination module is specifically used for: determining the image detail information in the differential image according to the noise position information and the noise intensity information; Determining the weight of each pixel in the image detail information; Determining supplementary pixel parameters based on the pixel parameters of each pixel in the image detail information and the weight corresponding to the pixel; According to the supplementary pixel parameters, the supplementary pixel points are superimposed on the sharpened image to obtain the target image.

8. The image processing device according to claim 7, wherein The image filtering module is specifically used for: The preset area image of the denoised image is filtered using at least two filters selected from a high-pass filter, a medium-high-pass filter, and a band-pass filter to obtain at least two of the high-frequency information components, the medium-high-frequency information components, and the medium-frequency information components of the denoised image corresponding to the at least two filters.

9. The image processing device according to claim 7 or 8, characterized in that The preset area image includes an edge image and / or a detail area image of the denoised image.

10. The image processing device according to claim 7, wherein The image sharpening module is specifically used for: For the at least two frequency information components, respectively adjust the gain parameter of each frequency information component in the preset adjustment parameter, determine the frequency optimization information component corresponding to each frequency information component, and obtain at least two frequency optimization information components; The at least two frequency optimization information components are superimposed on the denoised image to obtain the sharpened image.

11. The image processing device according to claim 10, wherein The image sharpening module is further configured to: Determining a component threshold corresponding to each frequency optimization information component; Within the range of the component threshold, the frequency optimization information components corresponding to the component threshold in the at least two frequency optimization information components are sequentially superimposed on the denoised image to determine a sharpened image.

12. The image processing device according to any one of claims 7, 10 or 11, characterized in that: The preset adjustment parameters include at least one of the following adjustment parameters: a brightness parameter, a spatial frequency parameter, a light-dark contrast parameter, a local histogram parameter, a local pixel method parameter, and a local pixel uniformity parameter.

13. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the image processing method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium, characterized in that When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the image processing method according to any one of claims 1 to 6.

Images