CN111583145A

CN111583145A - Image noise reduction method and device, electronic equipment and storage medium

Info

Publication number: CN111583145A
Application number: CN202010366413.7A
Authority: CN
Inventors: 王州霞; 张佳维; 任思捷; 张帆
Original assignee: Shenzhen Shangtang Intelligent Sensor Technology Co ltd
Current assignee: Shenzhen Shangtang Intelligent Sensor Technology Co ltd
Priority date: 2020-04-30
Filing date: 2020-04-30
Publication date: 2020-08-25
Anticipated expiration: 2040-04-30
Also published as: CN111583145B

Abstract

The present disclosure relates to an image noise reduction method and apparatus, an electronic device, and a storage medium, the method including: acquiring an image to be subjected to noise reduction and a corresponding motion detection map, wherein the image to be subjected to noise reduction is obtained according to a plurality of frames of images, and the motion detection map is used for representing the conversion relation between the image to be subjected to noise reduction and the plurality of frames of images; obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the motion detection graph, wherein the noise reduction degree graph is used for representing the noise reduction degree corresponding to pixel points contained in the image to be subjected to noise reduction; and carrying out noise reduction processing on the image to be subjected to noise reduction according to the noise reduction degree graph to obtain a noise reduction image. The embodiment of the disclosure can improve the noise reduction precision.

Description

Image noise reduction method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to an image denoising method and apparatus, an electronic device, and a storage medium.

Background

Due to the hardware limitation of light source irradiation and terminal equipment, the shot pictures have more noises, and the problems of image blurring, unsharpness and the like are caused, so that the noise reduction of the images is an important link in the shooting of digital cameras and mobile phones.

Disclosure of Invention

The present disclosure provides an image denoising technical scheme for improving denoising precision.

According to an aspect of the present disclosure, there is provided an image noise reduction method, including:

acquiring an image to be subjected to noise reduction and a corresponding motion detection map, wherein the image to be subjected to noise reduction is obtained according to a plurality of frames of images, and the motion detection map is used for representing the conversion relation between the image to be subjected to noise reduction and the plurality of frames of images;

obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the motion detection graph, wherein the noise reduction degree graph is used for representing the noise reduction degree corresponding to pixel points contained in the image to be subjected to noise reduction;

and carrying out noise reduction processing on the image to be subjected to noise reduction according to the noise reduction degree graph to obtain a noise reduction image.

In a possible implementation manner, the acquiring an image to be denoised and a corresponding motion detection map includes:

carrying out motion detection on the collected multi-frame images to obtain a motion detection result, wherein a motion area in each frame of image is identified in the motion detection result;

and superposing the multi-frame images according to the motion detection result to obtain an image to be subjected to noise reduction and a motion detection image corresponding to the image to be subjected to noise reduction.

In a possible implementation manner, the superimposing the multiple frames of images according to the motion detection result to obtain an image to be noise-reduced and a motion detection map corresponding to the image to be noise-reduced includes:

determining a first image corresponding to the first pixel point from the multi-frame image according to the motion detection result aiming at any first pixel point in the image to be denoised, wherein a second pixel point corresponding to the first pixel point in the first image is in a motion area;

taking any first pixel point with a first image as a target pixel point, and overlapping pixel values corresponding to the target pixel point in the first image to obtain a pixel value corresponding to the target pixel point in the image to be denoised;

and superposing pixel values corresponding to the second pixel points in the multi-frame images aiming at any second pixel point except the target pixel point in the image to be denoised to obtain the pixel value corresponding to the second pixel point in the image to be denoised.

In a possible implementation manner, the obtaining, according to the motion detection map, a noise reduction degree map corresponding to the image to be noise reduced includes:

obtaining corresponding noise reduction strength according to a pixel value corresponding to any third pixel point in the motion detection image and a pixel value of a fourth pixel point corresponding to the third pixel point in the initial noise reduction strength image,

wherein the noise reduction degree is negatively correlated with a pixel value corresponding to the third pixel point in the motion detection map, and the noise reduction degree is positively correlated with a pixel value corresponding to the fourth pixel point in the initial noise reduction degree map;

and obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the noise reduction degree.

In a possible implementation manner, the image to be denoised is an image in a first format, and the method further includes:

and performing image format conversion on the noise-reduced image in the first format to obtain a noise-reduced image in a second format, wherein the noise distribution reduction degree of the image in the first format is higher than that of the image in the second format.

In a possible implementation manner, the denoising processing is performed on the image to be denoised according to the denoising dynamics map through a denoising network, so as to obtain a denoised image, and the method further includes: training the noise reduction network through a preset training set, wherein the training set comprises a noise-free sample image, and the noise-free sample image is obtained by overlapping a plurality of images in an original format.

In one possible implementation, the training the noise reduction network through a preset training set includes:

obtaining a noisy sample image corresponding to the noiseless sample image and a noise reduction strength graph corresponding to the noisy sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image;

inputting the noisy sample image and the noise reduction strength graph corresponding to the noisy sample image into the noise reduction network for noise reduction processing to obtain a noise sample image after noise reduction;

and training the noise reduction network according to the noise-reduced sample image and the noise-free sample image.

In a possible implementation manner, the obtaining a noisy sample image corresponding to the noiseless sample image and a noise reduction map corresponding to the noisy sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image includes:

determining an exposure gain value corresponding to the noise-free sample image;

determining a first noise and a second noise corresponding to the noise-free sample image according to the exposure gain value;

and obtaining a noisy sample image corresponding to the noiseless sample image according to the first noise, the second noise and the noiseless sample image.

In a possible implementation manner, the obtaining a noisy sample image and a noise reduction map corresponding to the noiseless sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image further includes:

determining the noise reduction degree corresponding to the noise-free sample image according to the exposure gain value;

and constructing the noise reduction degree graph according to the noise reduction degree, wherein the noise reduction degree graph is consistent with the size of the noisy sample image.

According to an aspect of the present disclosure, there is provided an image noise reduction device including: the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an image to be subjected to noise reduction and a corresponding motion detection map, the image to be subjected to noise reduction is obtained according to a plurality of frames of images, and the motion detection map is used for representing the conversion relation between the image to be subjected to noise reduction and the plurality of frames of images;

the processing module is used for obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the motion detection graph, and the noise reduction degree graph is used for representing the noise reduction degree corresponding to the pixel points contained in the image to be subjected to noise reduction;

and the noise reduction module is used for carrying out noise reduction processing on the image to be subjected to noise reduction according to the noise reduction intensity map to obtain a noise reduction image.

In a possible implementation manner, the obtaining module is further configured to:

In one possible implementation manner, the processing module is further configured to:

In a possible implementation manner, the image to be denoised is an image in a first format, and the apparatus further includes:

and the conversion module is used for carrying out image format conversion on the noise reduction image in the first format to obtain a noise reduction image in a second format, wherein the noise distribution reduction degree of the image in the first format is higher than that of the image in the second format.

In one possible implementation, the method is implemented by a noise reduction module through a noise reduction network, and the apparatus includes:

the training module is used for training the noise reduction network through a preset training set, the training set comprises a noise-free sample image, and the noise-free sample image is obtained by superposing a plurality of images in original formats.

In one possible implementation manner, the training module is further configured to:

According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

In this way, an image to be noise-reduced and a motion detection map corresponding to the image to be noise-reduced can be obtained, where the image to be noise-reduced is obtained according to multiple frames of images, and the motion detection map is used to represent a conversion relationship between the image to be noise-reduced and the multiple frames of images. And obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the motion detection graph, wherein the noise reduction degree graph is used for representing the noise reduction degree corresponding to the pixel points contained in the image to be subjected to noise reduction. And then, carrying out noise reduction processing on the image to be subjected to noise reduction according to the noise reduction degree graph to obtain a noise reduction image. According to the image denoising method and device, the electronic device, and the storage medium provided by the embodiment of the disclosure, since the denoising strength map obtained according to the motion detection map is used for identifying the denoising strength corresponding to each pixel point in the image to be denoised, and further, denoising processing can be performed on different noise granularities of a motion region and a non-motion region in the image to be denoised according to the denoising strength map, so that the problems of inconsistent global noise granularity and inconsistent local noise granularity in the image to be denoised can be solved, and the denoising precision can be improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

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

FIG. 1 shows a flow diagram of an image denoising method according to an embodiment of the present disclosure;

FIG. 2 shows a schematic structural diagram of a noise reduction network according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of an image denoising method according to an embodiment of the present disclosure;

FIG. 4 shows a block diagram of an image noise reduction apparatus according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure;

fig. 6 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

Fig. 1 shows a flowchart of an image denoising method according to an embodiment of the present disclosure, which may be performed by an electronic device such as a terminal device or a server, the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like, and the method may be implemented by a processor calling a computer readable instruction stored in a memory. Alternatively, the method may be performed by a server.

As shown in fig. 1, the image denoising method includes:

in step S11, an image to be noise-reduced and a corresponding motion detection map are obtained, where the image to be noise-reduced is obtained according to multiple frames of images, and the motion detection map is used to represent a conversion relationship between the image to be noise-reduced and the multiple frames of images.

For example, in order to improve the image quality of an image and reduce the blurring problem caused by jitter and motion, multiple frames of images need to be collected, and then motion detection is performed on the multiple frames of images to obtain a motion detection result corresponding to each frame of image, where a motion area in the frame of image is identified in the motion detection result. And superposing the multi-frame images according to the motion detection result to obtain the images to be subjected to noise reduction and the motion detection maps corresponding to the images to be subjected to noise reduction, wherein any pixel point in the motion detection maps can be used for identifying the number of the images related to the pixel point in the images to be subjected to noise reduction in the multi-frame images.

Illustratively, a plurality of frames of images can be superimposed according to the motion detection result, an image to be denoised can be obtained, the number of frames of the superimposed image corresponding to each pixel in the image to be denoised is determined, a motion detection map corresponding to the image to be denoised is constructed according to the number of frames of the image corresponding to each pixel, that is, a pixel value corresponding to any pixel point in the motion detection map is used for representing a pixel value corresponding to a pixel point corresponding to the pixel point in the image to be denoised, and the pixel value is obtained by superimposing several frames of images.

In step S12, a noise reduction degree map corresponding to the image to be noise reduced is obtained according to the motion detection map, where the noise reduction degree map is used to represent the noise reduction degree corresponding to the pixel points included in the image to be noise reduced.

In the process of image superposition, the noise reduction degree is in direct proportion to the noise granularity of the image, the corresponding noise reduction degree is large because the number of the image frames superposed in the motion region is small and the noise granularity is large, otherwise, the number of the image frames superposed in the non-motion region is large and the noise granularity is small and the corresponding noise reduction degree is small. Therefore, the number of images associated with each pixel point in the image to be denoised in the multi-frame image can be determined according to the motion detection image, and then the denoising strength of each pixel point in the image to be denoised can be adjusted according to the number of images associated with each pixel point in the image to be denoised in the multi-frame image, so that adaptive denoising processing is performed on different noise granularities of a motion region and a non-motion region in the image to be denoised.

For example, the noise reduction degree corresponding to each pixel point in the image to be noise-reduced may be determined according to the motion detection map, where the larger the pixel value corresponding to any pixel point in the motion detection map is, the smaller the noise reduction degree corresponding to the pixel point in the image to be noise-reduced is, correspondingly, the smaller the pixel value corresponding to any pixel point in the motion detection map is, the larger the noise reduction degree corresponding to the pixel point in the image to be noise-reduced is.

In step S13, performing noise reduction processing on the image to be noise reduced according to the noise reduction degree map to obtain a noise reduced image.

For example, the noise reduction processing may be performed on the image to be noise-reduced according to the noise reduction level corresponding to each pixel point in the image to be noise-reduced indicated in the noise reduction level map, so as to obtain the noise-reduced image. Illustratively, the noise reduction processing may be performed on the image to be noise reduced through a pre-trained noise reduction network for performing noise reduction processing on the image to be noise reduced according to the noise reduction strength map, so as to obtain the noise reduction image.

In this way, an image to be noise-reduced and a motion detection map corresponding to the image to be noise-reduced can be obtained, where the image to be noise-reduced is obtained according to multiple frames of images, and the motion detection map is used to represent a conversion relationship between the image to be noise-reduced and the multiple frames of images. And obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the motion detection graph, wherein the noise reduction degree graph is used for representing the noise reduction degree corresponding to the pixel points contained in the image to be subjected to noise reduction. And then, carrying out noise reduction processing on the image to be subjected to noise reduction according to the noise reduction degree graph to obtain a noise reduction image. According to the image denoising method provided by the embodiment of the disclosure, the denoising strength graph obtained according to the motion detection graph is used for identifying the denoising strength corresponding to each pixel point in the image to be denoised, and then denoising processing can be performed on different noise granularities of a motion region and a non-motion region in the image to be denoised according to the denoising strength graph, so that the problems of inconsistent global noise granularity and inconsistent local noise granularity in the image to be denoised can be solved, and the denoising precision can be improved.

In a possible implementation manner, the acquiring an image to be denoised and a corresponding motion detection map may include:

For example, the motion detection may be performed on the acquired multiple frames of images, and a motion region in each frame of image is detected to obtain a motion detection result. And superposing the multi-frame images according to the motion detection result to obtain an image to be denoised, and obtaining a motion detection image corresponding to the image to be denoised according to the superposition process.

In a possible implementation manner, the overlaying the multiple frames of images according to the motion detection result to obtain an image to be noise-reduced and a motion detection map corresponding to the image to be noise-reduced may include:

determining a first image corresponding to a first pixel point from the multi-frame image according to the motion detection result aiming at the first pixel point in the image to be denoised, wherein a second pixel point corresponding to the first pixel point in the first image is in a motion region;

For example, for any first pixel point in the image to be denoised, whether the pixel point corresponding to the first pixel point in each frame of image is in the motion region is determined according to the motion detection result of each frame of image, if an image exists in which the pixel point corresponding to the first pixel point is in the motion region, the image can be determined as the first image, and the first pixel point is determined as the target pixel point. If the pixel point corresponding to the first pixel point in any frame of image is not in the motion area, the first pixel point can be determined as a second pixel point.

For a target pixel point, the pixel values corresponding to the pixel points corresponding to the target pixel point in the corresponding first image of the target pixel point can be superposed to obtain the pixel value corresponding to the target pixel point, and for a second pixel point, the pixel values corresponding to the pixel points corresponding to the second pixel point in all images can be superposed to obtain the pixel value corresponding to the second pixel point.

Illustratively, N frames of images are collected, and for a target pixel a, a pixel a1 corresponding to the target pixel a in 5 frames of images among the target pixel a is in a motion region, then pixel values corresponding to a pixel a1 corresponding to the target pixel a in the 5 frames of images can be superimposed to obtain a pixel value corresponding to the target pixel a in an image to be denoised, and the pixel value corresponding to the target pixel a in a corresponding motion detection map is 5. For the second pixel point b, the pixel values corresponding to the pixel point b1 corresponding to the second pixel point b in the N-frame image may be superimposed to obtain the pixel value corresponding to the second pixel point b in the image to be noise-reduced, where the pixel value corresponding to the second pixel point b in the corresponding motion detection map is N, so as to obtain the image to be noise-reduced and the motion detection map corresponding to the image to be noise-reduced.

In a possible implementation manner, the obtaining, according to the motion detection map, a noise reduction degree map corresponding to the image to be noise reduced may include:

obtaining the noise reduction degree corresponding to the position according to the pixel value corresponding to any third pixel point in the motion detection graph and the pixel value corresponding to the fourth pixel point corresponding to the third pixel point in the initial noise reduction degree graph,

For example, an initial noise reduction level map may be determined, which may be specified by a user, such as: the initial noise reduction degree map may be an image obtained according to the specified noise reduction degree corresponding to each pixel in the image to be noise reduced. Or, the initial noise reduction degree map may be set according to parameters of the terminal device, for example: the initial noise reduction degree map may be an exposure gain value corresponding to the image to be noise reduced. Alternatively, the initial noise reduction map may be obtained by preprocessing an image to be noise reduced, for example: and performing semantic segmentation on the image to be denoised to obtain an initial denoising strength map.

After the initial noise reduction degree map is obtained, the noise reduction degree corresponding to the pixel point corresponding to the third pixel point in the image to be noise reduced can be obtained according to the image frame number identified by any third pixel point in the motion detection map and the noise reduction degree corresponding to the fourth pixel point corresponding to the third pixel point in the initial noise reduction degree map, and then the noise reduction degree map corresponding to the image to be noise reduced is constructed according to the noise reduction degree corresponding to each pixel point.

For example, the noise reduction degree of the pixel point is negatively correlated with the pixel value corresponding to the third pixel point corresponding to the pixel point in the motion detection map, and positively correlated with the pixel value corresponding to the fourth pixel point corresponding to the pixel point in the initial noise reduction degree map. For example, a formula (one) may be used to determine the noise reduction strength corresponding to each position in the image to be noise reduced.

Where p is used to identify the noise reduction level, n is used to identify the pixel values in the motion detection map, p_baseFor identifying the corresponding noise reduction level in the initial noise reduction level map. Therefore, a noise reduction degree graph corresponding to the image to be subjected to noise reduction can be obtained.

In a possible implementation manner, the image to be denoised is an image in a first format, and the method may further include:

For example, the image to be denoised is an unprocessed original image acquired by an image acquisition device, and corresponds to a first format, where the first format is a format that makes the noise distribution of the image high in restoration degree, such as: since the first format may be a raw domain (raw) format, when the noise-reduced Image is obtained by reducing the noise of the Image to be reduced, the obtained noise-reduced Image is also in the first format, the noise-reduced Image may be subjected to Image conversion (for example, Image conversion may be performed in any of, but not limited to, ISP (Image Signal Processor) processing including operations such as demosaicing and tone mapping, PS (photo process), LD, and the like) from the first format to a second format having a lower noise distribution reduction degree than the first format, for example, converted to png (Portable Network Graphics) format, jpg format, and displayed.

Because the noise distribution reduction degree of the image to be denoised in the first format is higher, the image to be denoised in the first format can be subjected to better denoising effect, the denoised image with higher precision is obtained, and the denoised image is converted into the second format and then displayed.

In a possible implementation manner, the performing, by a noise reduction network, noise reduction processing on the image to be noise-reduced according to the noise reduction degree map to obtain a noise-reduced image, where the method further includes: training the noise reduction network through a preset training set, wherein the training set comprises a noise-free sample image, and the noise-free sample image is obtained by overlapping a plurality of images in an original format.

For example, the noise reduction processing may be performed on the image to be noise reduced according to the noise reduction level map according to a preset training set, so as to obtain a noise reduction network of the noise reduction image. The training set comprises a noise-free sample image, wherein the noise-free sample image is obtained by overlapping unprocessed images in an original format acquired by a plurality of pieces of terminal equipment, and the images in the original format can be images in a RAW format. Illustratively, N images in RAW format may be continuously acquired, and the N images in RAW format are averaged and superimposed to obtain a noise-free sample image.

In one possible implementation, the noise-free sample Image may be obtained by performing inverse ISP (Image Signal Processor) processing (e.g., demosaicing, tone mapping, etc.) on the noise-free RGB Image.

Therefore, the noise-free sample image is obtained by superposing a plurality of images in the original format, and the noise-free image obtained by performing simulation on the noise-free sample image is more consistent with real noise, so that the noise reduction network obtained by training the noise-free sample image can be migrated to practical application and has higher migratability.

In a possible implementation manner, the training the noise reduction network through a preset training set may include:

For example, according to the exposure gain value corresponding to the noise-free sample image, noise simulation processing may be performed on the noise-free sample image to obtain a noise-free sample image corresponding to the noise-free sample image.

In a possible implementation manner, the obtaining a noisy sample image corresponding to the noiseless sample image and a noise reduction map corresponding to the noisy sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image may include:

For example, the exposure gain value corresponding to the RAW format image obtained by synthesizing the noise-free sample image collected by the terminal device may be determined as the exposure gain value corresponding to the noise-free sample image; or the value can be randomly taken as the exposure gain value corresponding to the noise-free sample image.

Because the noise of the image in the original format mainly includes the optical quantum noise caused by the light source and the readout noise caused by the hardware of the terminal device, the optical quantum noise conforms to the poisson distribution, and the readout noise conforms to the gaussian distribution, the noisy sample image obtained by simulating the noiseless sample image can approximately conform to the single variance gaussian distribution as shown in the following formula (two):

y～N(μ＝x，²＝λ_read+λ_shotx) formula (II)

Wherein y represents a noisy sample image, x represents a non-noisy sample image, y to N () represent a single heteroscedastic Gaussian distribution function,²denotes the variance, λ_readRepresenting read noise, λ_shotRepresenting optical quantum noise.

The exposure gain value is related to the analog gain, the digital gain and the readout variance corresponding to the sensor of the terminal equipment, different exposure gain values correspond to different analog gains, digital gains and readout variances, and after the exposure gain value is determined, the analog gain, the digital gain and the readout variance corresponding to the exposure gain value can be determined.

After the analog gain, the digital gain and the readout variance are determined, a first noise can be determined according to the digital gain and the readout variance, the first noise can be readout noise, and the correlation relationship between the first noise and the digital gain and the readout variance can refer to formula (three); the second noise may be determined according to the analog gain and the digital gain, and the second noise may be optical quantum noise, where the correlation of the second noise with the analog gain and the digital gain may be referred to as formula (iv).

λ_read＝g_d ² _r ²Formula (III)

λ_shot＝g_dg_aFormula (IV)

Wherein, g_dRepresenting the digital gain, g, of the sensor of the terminal equipment_aRepresenting the analog gain of the sensor of the terminal device,_r ²representing the read-out variance of the sensor of the terminal device.

Thus, the exposure gain value corresponds to the first noise and the second noise, after the exposure gain value is determined, the first noise and the second noise corresponding to the exposure gain value can be obtained according to the corresponding relationship, and then the noisy sample image corresponding to the noiseless sample image can be obtained according to the formula (II).

In a possible implementation manner, the obtaining a noisy sample image corresponding to the noiseless sample image and a noise reduction map corresponding to the noisy sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image may further include:

For example, the exposure gain value is between the maximum exposure gain value and the minimum exposure gain value, wherein the maximum exposure gain value and the minimum exposure gain value are both preset values. And (4) obtaining the noise reduction strength corresponding to the noise-free sample image according to the exposure gain value, the maximum exposure gain value and the minimum exposure gain value, wherein the processing process can refer to a formula (V).

p＝(ISO-ISO_min)/(ISO_max-ISO_min)*p_maxFormula (five)

Where p is used to represent the noise reduction level, ISO is used to represent the exposure gain value, ISO_maxRepresenting the maximum exposure gain value, ISO_minDenotes the minimum exposure gain value, p_maxRepresenting the maximum noise reduction level (preset value).

After the noise reduction degree is determined, a noise reduction degree graph corresponding to the noisy sample image can be constructed according to the noise reduction degree, the length and the width of the noise reduction degree graph are consistent with those of the noisy sample image, and any pixel value in the noise reduction degree graph can be the noise reduction degree.

After the noisy sample image and the noise reduction strength map corresponding to the noisy sample image are obtained, the noisy sample image and the noise reduction strength map corresponding to the noisy sample image can be input into a noise reduction network for noise reduction processing, so that a noise reduction image corresponding to the noisy sample image is obtained.

As an example, the network structure of the noise reduction network may be as shown in fig. 2. As shown in fig. 2, after the noisy sample image is split into four channels by the rggb mode or the bggr mode, the four channels and the noise reduction graph are input into the noise reduction network, where the noise reduction network includes a first matrix dot multiplication module, a second matrix dot multiplication module, a third matrix dot multiplication module, and a matrix addition module. The first matrix dot multiplication module may perform dot multiplication on the input noisy sample image in the four-channel format and the noise reduction degree map to obtain a first processing result. And the second matrix dot multiplication module performs dot multiplication on the first processing result and the noise reduction degree graph to obtain a second processing result. And the third matrix dot multiplication module performs dot multiplication on the second processing result and the noise reduction degree graph to obtain a third processing result. And the matrix addition module is used for adding the third processing result and the four-channel noisy sample image to obtain a noise-reduced image.

After obtaining the noise reduction image corresponding to the noisy sample image according to the noise reduction network, determining the network loss of the noise reduction network according to the noise reduction image corresponding to the noisy sample image and the noiseless sample image corresponding to the noisy sample image, and then adjusting the network parameters of the noise reduction network according to the network loss until the noise reduction network meets the training requirements, for example: the network loss of the noise reduction network is less than a loss threshold.

It should be noted that the loss function of the noise reduction network may be determined in a 1-norm form or a 2-norm form, or other loss functions, and the embodiments of the present disclosure do not make a specification on the loss function here. For example: the network loss of the noise reduction network can be calculated in a 1-norm manner, and the loss function can refer to the following formula (six):

where L denotes a loss of the noise reduction network, H denotes a height corresponding to a noisy sample image, W denotes a width corresponding to a noisy sample image, T1 denotes a noiseless sample image, and T2 denotes a noise-reduced image.

In order that those skilled in the art will better understand the embodiments of the present disclosure, the embodiments of the present disclosure are described below by way of specific examples.

Referring to fig. 3, after the noise-free sample image is obtained, a noise sample image with known noise strength may be synthesized according to the noise-free sample image, and a noise strength map having the same size as the noise sample image may be generated according to the noise strength. And inputting the noisy sample image and the noise dynamics graph corresponding to the noisy sample image into a noise reduction network to obtain a noise reduction image corresponding to the noisy sample image. And carrying out learning training on the noise reduction network through a 1-model.

Therefore, in the noise reduction network obtained by training in the embodiment of the disclosure, the noise strength diagram can act on the feature diagrams of different layers in the network for multiple times in an embedded manner, so that the influence of the noise strength diagram on the noise reduction network is enhanced, the noise reduction network obtained by training is subjected to noise reduction according to the noise strength diagram, and a noise reduction image with higher precision is obtained.

After the noise reduction Network is obtained through training, the image to be noise reduced and the noise reduction strength graph corresponding to the image to be noise reduced can be input into the noise reduction Network to obtain the noise reduction image corresponding to the image to be noise reduced, and the obtained noise reduction image is still in an original format, so that the noise reduction image can be subjected to ISP processing and converted into a picture in formats such as png (Portable Network Graphics).

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides an image noise reduction apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the image noise reduction methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are not repeated.

Fig. 4 illustrates a block diagram of an image noise reduction apparatus according to an embodiment of the present disclosure, which may include, as illustrated in fig. 4:

an obtaining module 401, configured to obtain an image to be noise-reduced and a corresponding motion detection map, where the image to be noise-reduced is obtained according to multiple frames of images, and the motion detection map is used to represent a conversion relationship between the image to be noise-reduced and the multiple frames of images;

the processing module 402 may be configured to obtain a noise reduction degree map corresponding to the image to be noise reduced according to the motion detection map, where the noise reduction degree map is used to represent noise reduction degrees corresponding to pixel points included in the image to be noise reduced;

the denoising module 403 may be configured to perform denoising processing on the image to be denoised according to the denoising strength map, so as to obtain a denoised image.

In this way, an image to be noise-reduced and a motion detection map corresponding to the image to be noise-reduced can be obtained, where the image to be noise-reduced is obtained according to multiple frames of images, and the motion detection map is used to represent a conversion relationship between the image to be noise-reduced and the multiple frames of images. And obtaining a noise reduction degree graph corresponding to the image to be subjected to noise reduction according to the motion detection graph, wherein the noise reduction degree graph is used for representing the noise reduction degree corresponding to the pixel points contained in the image to be subjected to noise reduction. And then, carrying out noise reduction processing on the image to be subjected to noise reduction according to the noise reduction degree graph to obtain a noise reduction image. According to the image noise reduction device provided by the embodiment of the disclosure, the noise reduction graph obtained according to the motion detection graph is used for identifying the noise reduction degree corresponding to each pixel point in the image to be subjected to noise reduction, and then the noise reduction treatment can be performed on different noise granularities of a motion region and a non-motion region in the image to be subjected to noise reduction according to the noise reduction degree graph, so that the problems of inconsistent global noise granularity and inconsistent local noise granularity in the image to be subjected to noise reduction can be solved, and the noise reduction precision can be improved.

In a possible implementation manner, the obtaining module may be further configured to:

In a possible implementation manner, the processing module may be further configured to:

In a possible implementation manner, the image to be noise-reduced is an image in a first format, and the apparatus may further include:

the conversion module may be configured to perform image format conversion on the noise-reduced image in the first format to obtain a noise-reduced image in a second format, where a noise distribution reduction degree of the image in the first format is higher than a noise distribution reduction degree of the image in the second format.

In a possible implementation manner, the training module may be further configured to:

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

The embodiments of the present disclosure also provide a computer program product, which includes computer readable code, and when the computer readable code is run on a device, a processor in the device executes instructions for implementing the image noise reduction method provided in any of the above embodiments.

The embodiments of the present disclosure also provide another computer program product for storing computer readable instructions, which when executed cause a computer to perform the operations of the image noise reduction method provided in any of the above embodiments.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 5 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 5, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices 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 or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a 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 an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary 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 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 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 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may 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 816 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 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 6 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 6, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An image noise reduction method, comprising:

2. The method of claim 1, wherein the obtaining of the image to be denoised and the corresponding motion detection map comprises:

3. The method according to claim 2, wherein the superimposing the multiple frames of images according to the motion detection result to obtain an image to be noise-reduced and a motion detection map corresponding to the image to be noise-reduced includes:

4. The method according to any one of claims 1 to 3, wherein obtaining a noise reduction degree map corresponding to the image to be noise reduced according to the motion detection map comprises:

5. The method according to any one of claims 1 to 4, wherein the image to be noise-reduced is an image of a first format, the method further comprising:

6. The method according to claim 1, wherein the denoising processing of the image to be denoised according to the denoising dynamics map is implemented through a denoising network to obtain a denoised image, and the method further comprises: training the noise reduction network through a preset training set, wherein the training set comprises a noise-free sample image, and the noise-free sample image is obtained by overlapping a plurality of images in an original format.

7. The method of claim 6, wherein training the noise reduction network through a preset training set comprises:

8. The method according to claim 7, wherein obtaining the noise reduction level map corresponding to the noisy sample image and the noisy sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image comprises:

9. The method according to claim 8, wherein obtaining a noisy sample image and a noise reduction level map corresponding to the noiseless sample image according to the noiseless sample image and the exposure gain value corresponding to the noiseless sample image further comprises:

10. An image noise reduction apparatus, comprising:

the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an image to be subjected to noise reduction and a corresponding motion detection map, the image to be subjected to noise reduction is obtained according to a plurality of frames of images, and the motion detection map is used for representing the conversion relation between the image to be subjected to noise reduction and the plurality of frames of images;

11. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 9.

12. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 9.