Image denoising method and device, equipment, storage medium

Abstract

The application discloses an image denoising method and device, equipment and a storage medium. The method comprises the following steps: acquiring the brightness level of an original image in a current environment; determining the denoising parameters corresponding to different frequency noises in the original image under the brightness level; and performing denoising processing on the corresponding frequency noises according to the denoising parameters corresponding to the frequency noises. The workload of denoising processing can be reduced, and the original image can be subjected to targeted denoising processing, so that the presentation effect of the processed image is improved.

Description

Technical Field

[0001] This application relates to image processing technology, including but not limited to an image noise reduction method, apparatus, device, and storage medium. Background Technology

[0002] Most current smart terminals include a camera, which can lead to numerous application scenarios. Furthermore, as people's living standards improve and the application areas of cameras expand, users have increasingly higher demands for image quality, necessitating a series of image processing steps.

[0003] In the process of image processing, in order to achieve better image effects, it is necessary to perform noise reduction on the image. By performing noise reduction on the image, the interference caused by noise such as grainy noise in the image can be effectively reduced, making the image look cleaner and softer.

[0004] However, in traditional image denoising, on the one hand, there are no systematic denoising parameters for engineers to refer to. When denoising an image, engineers need to constantly modify and verify the denoising parameters to obtain relatively accurate results. Furthermore, denoising is greatly affected by the debugging of other processing steps; any modification to the effect parameters of other processes increases the workload of denoising. On the other hand, in some image denoising processes, denoising is generally performed on the entire image based on a single denoising parameter, without targeted denoising for different regions of the image, leading to inconsistent image quality after denoising. Summary of the Invention

[0005] In view of this, the image denoising method, apparatus, device, and storage medium provided in the embodiments of this application can reduce the workload of denoising processing and perform targeted denoising processing on the original image, thereby improving the presentation effect of the processed image. The image denoising method, apparatus, device, and storage medium provided in the embodiments of this application are implemented as follows:

[0006] The image noise reduction method provided in this application includes:

[0007] Obtain the brightness level of the original image in the current environment;

[0008] At each brightness level, determine the noise reduction parameters corresponding to different frequencies of noise in the original image.

[0009] Based on the noise reduction parameters corresponding to each frequency noise, noise reduction processing is performed on the corresponding frequency noise.

[0010] In some embodiments, at a brightness level, determining the noise reduction parameters corresponding to different frequencies of noise in the original image includes:

[0011] Based on the color space of the original image, the noise in the original image is classified to obtain various types of noise;

[0012] At each brightness level, for each type of noise, noise reduction parameters corresponding to different frequencies of noise under each type of noise are determined. The target type of noise can be any one of multiple types of noise.

[0013] In some embodiments, at a brightness level, for each type of noise, the noise reduction parameters corresponding to different frequencies of noise under each type of noise are determined, including:

[0014] At each brightness level, for each type of target noise, determine the proportion of noise at any frequency under that target type of noise in the total noise.

[0015] Based on the proportion, determine the noise reduction parameters corresponding to the proportion at any frequency under the target type.

[0016] In some embodiments, obtaining the brightness level of the original image in the current environment includes:

[0017] Detect the ambient brightness value of the original image in the current environment;

[0018] Based on the preset correspondence between brightness values ​​and brightness levels, the brightness level corresponding to the ambient brightness value is determined.

[0019] In some embodiments, the original image is a preview image or the original image obtained by taking the photo.

[0020] The image noise reduction apparatus provided in this application includes:

[0021] The acquisition module is used to acquire the brightness level of the original image in the current environment;

[0022] The determination module is used to determine the noise reduction parameters corresponding to different frequencies of noise in the original image at different brightness levels.

[0023] The processing module is used to perform noise reduction processing on the corresponding frequency noise according to the noise reduction parameters corresponding to each frequency noise.

[0024] In some embodiments, the apparatus further includes:

[0025] The classification module is used to classify noise in the original image according to the color space of the original image, resulting in various types of noise;

[0026] The determining module is specifically used to determine, at the brightness level, the noise reduction parameters corresponding to different frequencies of noise for each target type of noise, wherein the target type of noise is any one of multiple types of noise.

[0027] In some embodiments, the determining module is specifically used for:

[0028] At each brightness level, for each type of target noise, determine the proportion of noise at any frequency under that target type of noise in the total noise.

[0029] Based on the proportion, determine the noise reduction parameters corresponding to the proportion at any frequency under the target type.

[0030] In some embodiments, the acquisition module is specifically used for:

[0031] Detect the ambient brightness value of the original image in the current environment;

[0032] Based on the preset correspondence between brightness values ​​and brightness levels, the brightness level corresponding to the ambient brightness value is determined.

[0033] The computer device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0034] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method described in this application embodiment.

[0035] The image denoising method, apparatus, computer device, and computer-readable storage medium provided in this application embodiment acquire the brightness level of the original image in the current environment; determine the denoising parameters corresponding to different frequencies of noise in the original image at the brightness level; and perform denoising processing on the corresponding frequency noise according to the denoising parameters corresponding to each frequency noise. In this way, on the one hand, different denoising parameters are set for original images with different ambient brightness and different frequency noise, and the relevant parameters can be called during denoising processing without the need for constant modification by debugging personnel, thereby reducing the workload of denoising processing; on the other hand, setting different denoising parameters for different ambient brightness and different frequency noise in the original image also enables targeted denoising processing of the original image, thereby improving the presentation effect of the processed image and solving the technical problems mentioned in the background art. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application.

[0037] Figure 1 A schematic diagram illustrating the implementation process of an image noise reduction method provided in an embodiment of this application;

[0038] Figure 2 A schematic diagram illustrating the implementation process of another image noise reduction method provided in this application embodiment;

[0039] Figure 3 A schematic diagram illustrating the implementation process of the image noise reduction method provided in this application embodiment;

[0040] Figure 4 A schematic diagram illustrating the implementation process of another image denoising method provided in this application embodiment;

[0041] Figure 5 This is a schematic diagram of the image noise reduction device provided in the embodiments of this application;

[0042] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0045] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0046] It should be noted that the terms "first, second, third" used in the embodiments of this application are used to distinguish similar or different objects and do not represent a specific order of objects. It can be understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0047] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0048] Camera: A webcam (CAMERA or WEBCAM), also known as a computer camera, computer eye, electronic eye, etc., is a video input device that is widely used in video conferencing, telemedicine, and real-time monitoring.

[0049] Ordinary users can also communicate with each other online via webcam, using both video and audio. Additionally, users can utilize it for various popular digital imaging and video processing applications.

[0050] Images often contain noise signals of different frequencies after preprocessing by an image signal processor. In order to obtain higher quality images, noise reduction processing is generally performed on the images.

[0051] However, in traditional image denoising, on the one hand, there are no systematic denoising parameters for engineers to refer to. When denoising an image, engineers need to constantly modify and verify the denoising parameters to obtain relatively accurate results. Furthermore, denoising is greatly affected by the debugging of other processing steps; any modification to the effect parameters of other processes increases the workload of denoising. On the other hand, in some image denoising processes, different original images are generally denoised based on a single preset denoising parameter, without targeted denoising of the original images, leading to inconsistent image quality after denoising.

[0052] In view of this, embodiments of this application provide an image noise reduction method, which is applied to an electronic device. This electronic device can be various types of devices with information processing capabilities. For example, the electronic device may include a personal computer, laptop, PDA, or television; the electronic device may also be a mobile terminal, such as a mobile phone, in-vehicle computer, tablet computer, or projector. The function implemented by this method can be achieved by a processor in the electronic device calling program code. Of course, the program code can be stored in a computer storage medium. Therefore, the electronic device includes at least a processor and a storage medium.

[0053] Figure 1 This is a schematic diagram illustrating the implementation process of the image denoising method provided in this application embodiment. It can reduce the workload of denoising processing and perform targeted denoising processing on the original image, thereby improving the presentation effect of the processed image. Figure 1 As shown, the method may include the following steps 101 to 103:

[0054] Step 101: Obtain the brightness level of the original image in the current environment.

[0055] In this embodiment of the application, the type of the original image is not limited. For example, the original image can be a preview image or the original image obtained after shooting. That is, the original image can be a preview image presented by the electronic device before shooting is completed, or it can be the original image obtained after the electronic device has completed shooting.

[0056] In some embodiments, step 101 can be implemented by performing steps 201 to 202 in the following embodiments.

[0057] Step 102: Determine the noise reduction parameters corresponding to different frequencies of noise in the original image at the brightness level.

[0058] Understandably, when performing various processing operations on an image, it can be divided into regions based on frequency. The frequency of an image refers to an indicator of the drastic change in the grayscale value of a pixel in the image; it is the gradient of grayscale in a two-dimensional space. Based on this, an image can be divided into high-frequency, mid-frequency, and low-frequency regions.

[0059] The low-frequency region of an image, mainly the smooth areas, forms the basic gray levels and has a relatively small impact on the image structure. The mid-frequency region determines the basic structure of the image, forming the main edge structure. The high-frequency region, which represents areas where gray levels change rapidly within a unit distance, reflects the image edges (i.e., details), forming the edges and details of the image. Since image edges represent the main features of the entire image and directly affect the visual presentation, image denoising requires preserving relevant edge information. In other words, to improve denoising performance, different processing methods need to be applied to noise points of different frequencies within the image.

[0060] In some embodiments, after determining the brightness level of the original image in the current environment, the noise of different frequencies in the original image is directly subjected to uniform standard noise reduction based on the preset correspondence between the brightness level and the noise reduction parameters, which will result in poor visual presentation of the processed image.

[0061] In view of this, in the embodiments of this application, after determining the brightness level of the original image in the current environment, the noise in the original image is further divided based on frequency under the current brightness level, and the corresponding noise reduction parameters are determined for noise of different frequencies. This enables targeted noise reduction processing of noise of different frequencies based on the noise reduction parameters of different frequencies, thereby improving the noise reduction effect.

[0062] Step 103: Perform noise reduction processing on the corresponding frequency noise according to the noise reduction parameters corresponding to each frequency noise.

[0063] In this embodiment, the brightness level of the original image under the current environment is obtained; at the brightness level, denoising parameters corresponding to different frequencies of noise in the original image are determined; and denoising processing is performed on the corresponding frequency noise according to the denoising parameters. In this way, on the one hand, for original images with different ambient brightness and different frequencies of noise, there are pre-set different denoising parameters. Thus, when processing different original images, denoising can be performed based on a unified standard, without requiring constant modifications by debugging personnel, thereby reducing the workload of denoising processing. On the other hand, when denoising the original image, different denoising parameters correspond to different ambient brightness and different frequencies of noise in the original image, thereby enabling targeted denoising processing of the original image and improving the presentation effect of the processed image.

[0064] This application provides another image noise reduction method. Figure 2 This is a schematic diagram illustrating the implementation process of the image denoising method provided in the embodiments of this application, such as... Figure 2 As shown, the method may include the following steps 201 to 205:

[0065] Step 201: Detect the ambient brightness value of the original image in the current environment.

[0066] In this embodiment, after obtaining the original image, the electronic device automatically detects the brightness of the surrounding environment where the original image is located, which is the current ambient brightness. It is understood that the ambient brightness is different in different scenarios. For example, the ambient brightness of the electronic device at night or in a completely dark environment is different from the ambient brightness of the electronic device in a restaurant, KTV or bar. The ambient brightness of the electronic device in an office is also different from the ambient brightness of the electronic device in a bright outdoor or indoor environment during the day.

[0067] In this embodiment of the application, the method for detecting the ambient brightness of the original image is not limited.

[0068] For example, in some embodiments, in order to ensure that the brightness levels of different original images can be judged according to a uniform standard, a data lookup table containing a one-to-one correspondence between exposure table sequence values ​​and ambient brightness values ​​can be stored in the electronic device in advance. In this way, when the electronic device previews or takes a picture of the current environment and obtains the exposure table sequence value of the current environment, it can obtain the current ambient brightness value based on the pre-stored data lookup table.

[0069] In other embodiments, the detection of ambient brightness in which the original image is located can also be achieved using a light sensor in the electronic device. A light sensor, also called a brightness sensor, consists of two components: a projector and a receiver. The projector focuses light through a lens, transmits it to the lens of the receiver, and then to the receiving sensor. The receiving sensor converts the received light signal into an electrical signal. Therefore, based on this light sensor, the electronic device can obtain the current ambient brightness value according to the ambient light level in which it is located.

[0070] Step 202: Determine the brightness level corresponding to the ambient brightness value based on the preset correspondence between brightness values ​​and brightness levels.

[0071] Understandably, before denoising the original image, the correspondence between brightness values ​​and brightness levels can be pre-stored in the electronic device. In this way, after detecting the ambient brightness value of the original image in the current environment, the brightness level corresponding to the ambient brightness value of the original image can be determined based on the pre-stored correspondence.

[0072] The specific numerical correspondence between brightness values ​​and brightness levels is not limited and can be set based on actual needs. For example, in some embodiments, ambient brightness values ​​in the range of [20 lux-50 lux] can be set as brightness level 1, and ambient brightness values ​​in the range of [50 lux-120 lux] can be set as brightness level 2, etc.

[0073] Thus, assuming the ambient brightness value of the currently detected original image is 31 lux, the brightness level of the original image can be determined as brightness level 1. Then, under brightness level 1, step 203 is executed to classify the noise type in the original image.

[0074] Understandably, the noise reduction parameters will differ for different brightness levels.

[0075] It should be noted that there is no limitation on the execution order of steps 201 and 203. They can be executed in parallel, either by first determining the brightness level of the original image and then classifying the noise type of the original image, or by executing them in parallel.

[0076] Step 203: Based on the color space of the original image, classify the noise in the original image to obtain various types of noise.

[0077] In this embodiment of the application, in order to further refine the noise reduction parameters corresponding to different frequencies of noise, after determining the brightness level of the original image, the noise type of the original image can also be determined under the current brightness level, and then the noise reduction parameters corresponding to different frequencies of noise under each type of noise can be determined respectively.

[0078] For example, in some embodiments, the noise type in the original image can be classified in terms of color space, dividing the noise into luminance noise and chroma noise; further, in some embodiments, the chroma noise can be further refined into blue chroma component noise cb and red chroma component noise cr.

[0079] In one feasible embodiment, such as Figure 3 As shown, after determining the ambient brightness value of the current environment of the original image, the brightness level corresponding to the ambient brightness value is determined based on the preset correspondence between brightness value and brightness level. For example, if the current brightness level of the original image is determined to be brightness level 1, the noise of the original image can be further classified under brightness level 1, such as dividing the noise into brightness noise Y, color noise cb and color noise cr.

[0080] Step 204: Under the brightness level, for each type of noise, determine the noise reduction parameters corresponding to different frequencies of noise under each type of noise. The target type noise can be any one of the multiple types of noise.

[0081] In some embodiments, step 204 can be achieved by performing steps 2041 to 2042 as shown in the following embodiments:

[0082] Step 2041: At the brightness level, for each type of target noise, determine the proportion of any frequency noise under that target type noise in the total noise.

[0083] In one feasible embodiment, such as Figure 3 As shown, after determining the ambient brightness value of the environment in which the original image is currently located, the brightness level corresponding to the ambient brightness value is determined based on the preset correspondence between brightness values ​​and brightness levels. For example, if the current brightness level of the original image is determined to be brightness level 1, the noise of the original image can be further classified under brightness level 1, such as dividing the noise into bright noise (Y), color noise (cb), and color noise (cr). Subsequently, under the target type of noise, the proportion of each frequency noise in the total noise is calculated. For example, under bright noise, the proportion of high-frequency noise in the total noise under the bright noise type is calculated to be 50%, the proportion of mid-frequency noise in the total noise under the bright noise type is 30%, and the proportion of low-frequency noise in the total noise under the bright noise type is 20%. Similarly, for color noise (cb), the proportion of its corresponding different frequencies (high frequency, mid frequency, low frequency) noise under this noise type can also be calculated in the same way; and for color noise (cr), the proportion of its corresponding different frequencies (high frequency, mid frequency, low frequency) noise under this noise type can also be calculated in the same way.

[0084] Step 2042: Based on the proportion, determine the noise reduction parameters corresponding to the proportion at any frequency noise level under the target type.

[0085] Here, when determining the noise reduction parameters corresponding to each frequency noise, one can first determine the threshold range of each frequency noise based on the preset threshold range, and then determine the noise reduction parameters corresponding to each frequency noise based on the noise reduction parameters corresponding to the threshold range.

[0086] For example, such as Figure 3 As shown, under the determined bright noise type, high-frequency noise accounts for 50% of the total noise in this type, mid-frequency noise accounts for 30%, and low-frequency noise accounts for 20%. Assuming the preset threshold range corresponds to the noise reduction parameters as follows: [0%-40%] corresponds to noise reduction parameter 1, [41%-70%] corresponds to noise reduction parameter 2, and [71%-100%] corresponds to noise reduction parameter 3, then it can be determined that under the bright noise type, high-frequency noise is located in the threshold range [41%-70%], and its corresponding noise reduction parameter is noise reduction parameter 2; low-frequency and mid-frequency noise are both located in the threshold range [0%-40%], and their corresponding noise reduction parameters are both noise reduction parameter 1. Thus, subsequent noise reduction processing is based on the noise reduction parameters corresponding to different frequencies of noise, and targeted processing is applied accordingly, thereby improving the noise reduction effect.

[0087] Of course, the above division of threshold intervals is only illustrative. In actual applications, the division can be made based on actual needs, and this application does not limit it.

[0088] Similarly, for color noise CB, the noise reduction parameters for different frequencies (high frequency, mid frequency, low frequency) of noise under this noise type can be calculated in the same way; and for color noise CR, the noise reduction parameters for different frequencies (high frequency, mid frequency, low frequency) of noise under this noise type can also be calculated in the same way.

[0089] It can be seen that under the same type of noise, such as bright noise, the noise reduction parameters corresponding to different frequencies of noise may be the same or different. Furthermore, the noise reduction parameters corresponding to different frequencies of noise under different types of noise may also be the same or different. For example, the noise reduction parameter corresponding to high-frequency noise under bright noise type is noise reduction parameter 2, and the noise reduction parameter corresponding to mid-frequency noise under color noise CB type may also be noise reduction parameter 2.

[0090] Step 205: Perform noise reduction processing on the corresponding frequency noise according to the noise reduction parameters corresponding to each frequency noise.

[0091] In this embodiment, after detecting the ambient brightness value of the original image, the corresponding brightness level is first determined based on the ambient brightness value. Then, under the current brightness level, the noise in the original image is classified to obtain different types of noise. For each type of noise, the corresponding noise reduction parameters for different frequencies are determined, and noise reduction processing is performed on the corresponding frequency noise according to the noise reduction parameters. In this way, when performing noise reduction processing on the original image, on the one hand, the brightness, type, and frequency that affect the noise are considered, so that the noise can be processed in a targeted manner to provide a noise reduction effect; on the other hand, based on the specific noise reduction parameters, noise reduction can be performed based on a unified standard when processing different original images, thereby reducing the workload of noise reduction processing.

[0092] The following describes an exemplary application of the embodiments of this application in a real-world application scenario.

[0093] Figure 4 The overall flow of the image noise reduction method provided in the embodiments of this application is as follows. Figure 4 As shown, the method includes the following steps 401 to 405:

[0094] Step 401: Configure noise reduction parameters on the computer. Specifically: Register the correspondence between the exposure table and the brightness level to determine the current environmental gain (i.e., brightness level) of the image; configure the noise type judgment threshold to distinguish noise types; configure the statistical thresholds for different trigger segments of different noise types; configure different noise reduction levels (i.e., noise reduction parameters) according to the statistical percentage (i.e., proportion).

[0095] Step 402: Take a picture with the camera to obtain the original image, and detect the current environment gain of the original image.

[0096] Step 403: Under the current environment gain, classify the noise in the original image and count the proportion of each trigger segment.

[0097] Step 404: Compare the weight of each frequency with the corresponding judgment threshold to obtain the corresponding noise reduction level.

[0098] Step 405: Apply the corresponding noise reduction level to the noise at the corresponding frequency.

[0099] In this embodiment, the brightness value is first divided into different brightness levels (gain) based on the ambient brightness of the original image. Then, under the current brightness level of the original image, the type of noise (Y, Cb, Cr) in the original image is distinguished. Then, based on the distinction of different frequencies of noise (High, Middle, Low), different noise reduction levels (Denoise Grade) are set according to the distribution of different frequencies of noise in different trigger segments.

[0100] The automatic noise reduction level adjustment algorithm provided in this embodiment reduces the debugging workload compared to the original noise reduction algorithm. Furthermore, it has a flexible matching mechanism for different noise levels in different environments, allowing for free adjustment according to user preferences. This not only eliminates many tedious debugging processes but also makes the debugging data more accurate by matching noise reduction levels through statistical data. Moreover, when parameters of other modules change, the algorithm can detect the noise situation after the change in the current environment and quickly obtain the noise reduction level.

[0101] It should be understood that, although Figure 1 , Figure 2 and Figure 4 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 , Figure 2 and Figure 4 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0102] Based on the foregoing embodiments, this application provides an image noise reduction device, which includes various modules and units included in each module, and can be implemented by a processor; of course, it can also be implemented by specific logic circuits; in the implementation process, the processor can be a central processing unit (CPU), microprocessor (MPU), digital signal processor (DSP) or field programmable gate array (FPGA), etc.

[0103] Figure 5 This is a schematic diagram of the image noise reduction device provided in the embodiments of this application, as shown below. Figure 5 As shown, the device 500 includes an acquisition module 501, a determination module 502, and a processing module 503, wherein:

[0104] The acquisition module is used to acquire the brightness level of the original image in the current environment;

[0105] The determination module is used to determine the noise reduction parameters corresponding to different frequencies of noise in the original image at different brightness levels.

[0106] The processing module is used to perform noise reduction processing on the corresponding frequency noise according to the noise reduction parameters corresponding to each frequency noise.

[0107] In some embodiments, the apparatus further includes a classification module, which is used to classify noise in the original image according to the color space of the original image to obtain multiple types of noise; the determination module is specifically used to determine, at a brightness level, for each target type of noise, the noise reduction parameters corresponding to different frequencies of noise under each target type of noise, wherein the target type of noise is any one of the multiple types of noise.

[0108] In some embodiments, the determining module is specifically used for:

[0109] At each brightness level, for each type of target noise, determine the proportion of noise at any frequency under that target type of noise in the total noise.

[0110] Based on the proportion, determine the noise reduction parameters corresponding to the proportion at any frequency under the target type.

[0111] In some embodiments, the acquisition module is specifically used for:

[0112] Detect the ambient brightness value of the original image in the current environment;

[0113] Based on the preset correspondence between brightness values ​​and brightness levels, the brightness level corresponding to the ambient brightness value is determined.

[0114] In some embodiments, the original image is a preview image or the original image obtained by taking a picture.

[0115] In this embodiment, on the one hand, different noise reduction parameters are pre-set for original images with different ambient brightness and different frequencies of noise. In this way, when processing different original images, noise reduction can be performed based on a unified standard without the need for debugging personnel to make constant modifications, thereby reducing the workload of noise reduction processing. On the other hand, when denoising the original image, different noise reduction parameters are corresponding to different ambient brightness and different frequencies of noise in the original image, thereby enabling targeted noise reduction processing of the original image and improving the presentation effect of the processed image.

[0116] The descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0117] It should be noted that, in the embodiments of this application... Figure 5 The module division of the image noise reduction device shown is illustrative and represents only one logical functional division; in actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, exist as separate physical units, or be integrated into one unit with two or more units. The integrated units can be implemented in hardware, as software functional units, or a combination of both.

[0118] It should be noted that, in the embodiments of this application, if the above-described methods are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0119] This application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an image noise reduction method.

[0120] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in the above embodiments.

[0121] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps in the method provided in the above-described method embodiments.

[0122] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In one embodiment, the image noise reduction apparatus provided in this application can be implemented as a computer program, which can be implemented in the form of, for example, Figure 6 It runs on the computer device shown. The computer device's memory can store the various program modules that make up the sampling device, for example, Figure 5 The acquisition module, determination module, and processing module are shown. The computer program, comprised of these modules, causes the processor to execute the steps of the image denoising methods described in the various embodiments of this application.

[0124] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium, storage medium, and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0125] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0126] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three kinds of relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist simultaneously, and object B exists alone.

[0127] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or modules can be electrical, mechanical, or other forms.

[0129] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, each functional module in the various embodiments of this application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the integrated modules can be implemented in hardware or in the form of hardware plus software functional units.

[0131] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0132] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0133] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0134] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0135] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0136] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An image denoising method, characterized in that, The method comprises: obtaining a brightness level of an original image in a current environment; determining, under the brightness level, a noise reduction parameter corresponding to each frequency noise in the original image; performing noise reduction processing on the corresponding frequency noise according to the noise reduction parameter corresponding to each frequency noise; wherein the determining, under the brightness level, of the noise reduction parameter corresponding to each frequency noise in the original image comprises: classifying the noise in the original image according to a color space of the original image to obtain multiple types of noise; determining, under the brightness level, a proportion of total noise of any frequency noise under a target type of noise for each target type of noise, the target type of noise being any one of the multiple types of noise; determining, according to the proportion, a noise reduction parameter corresponding to the proportion under the target type of noise.

2. The method of claim 1, wherein, The obtaining of the brightness level of the original image in the current environment comprises: detecting an ambient brightness value of the original image in the current environment; determining a brightness level corresponding to the ambient brightness value according to a preset corresponding relationship between brightness values and brightness levels.

3. The method according to claim 1 or 2, characterized in that, The original image is a photographed preview image or an original image obtained by photographing.

4. An image noise reduction apparatus, characterized by comprising: comprises: an obtaining module configured to obtain a brightness level of an original image in a current environment; a determining module configured to determine, under the brightness level, a noise reduction parameter corresponding to each frequency noise in the original image; a processing module configured to perform noise reduction processing on the corresponding frequency noise according to the noise reduction parameter corresponding to each frequency noise; a classifying module configured to classify the noise in the original image according to a color space of the original image to obtain multiple types of noise; the determining module is specifically configured to determine, under the brightness level, a proportion of total noise of any frequency noise under a target type of noise for each target type of noise, the target type of noise being any one of the multiple types of noise; determine, according to the proportion, a noise reduction parameter corresponding to the proportion under the target type of noise.

5. A computer device comprising a memory and a processor, the memory storing a computer program capable of running on the processor, characterized in that, The processor executes the program to implement the steps of the method of any one of claims 1 to 3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1 to 3.

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