Patent Description:
In related art, in order to improve the quality of a collected image, after the image is collected, it is necessary to carry out denoising on the collected image. As the image processing techniques play an increasingly important role in the fields of artificial intelligence (AI) recognition, fingerprint detection, automatic driving, etc., the influence of a denoising algorithm is increasing day by day.

In the related art, <CIT> provides a multi-channel edge-aware chrominance noise reduction. Noise in an image is reduced in a manner that takes into account edge information in one or more channels of the image. A first image is received that is formatted according to a red-green-blue (RGB) color model. The first image is converted from the RGB color model to a second color model that includes at least a luminance channel, a first chrominance channel, and a second chrominance channel that are representative of the first image. The first and second chrominance channels are each denoised in a manner that accounts at least for edge information in the luminance channel, and may also include edge information from other channels in a manner that accounts for per-channel noise characteristics. The luminance channel and denoised first and second chrominance channels are converted to a second image formatted according to the RGB color model that is a noise-reduced version of the first image.

In the related art, "<NPL> discloses the following technical solution: this work presents an image denoising algorithm, arguably the simplest among all the counterparts, but surprisingly effective. The algorithm exploits the image pixel correlation in the special dimension as well as in the color dimension. The color channels of an image are first decorrelated with a <NUM>-point orthogonal transform. Each decorrelated channel is then denoised separately via local DCT (discrete cosine transform) thresholding: a channel is decomposed into sliding local patches, which are denoised by thresholding in the DCT domain, and then averaged and aggregated to reconstruct the channel. The denoised image is obtained from the denoised decorrelated channels by inverting the <NUM>-point orthogonal transform.

The present application provides an image processing method and apparatus and a storage medium.

According to a first aspect of the invention, a computer-implemented image processing method as defined in claim <NUM> is provided.

Optionally, fusing the first channel image in all the channel images and the first denoised image corresponding to the first channel image to obtain the composite image includes:.

Optionally, weighting the color components corresponding to the first channel image and the color components corresponding to the first denoised image based on the composition weights to obtain the weighted color component includes:.

Optionally, denoising the channel images formed by the color components of the respective channels in the second color space respectively to obtain the denoised images corresponding to the respective channel images includes:.

Optionally, converting the channel images from the spatial domain to the spectral domain respectively includes:
carrying out discrete cosine transform on the channel images respectively to convert the respective channel images from the spatial domain to the spectral domain.

Optionally, the color components corresponding to the first channel image are used to represent luminance of the original image; and
the color components corresponding to the second channel image are used to represent color of the original image.

According to a second aspect of the invention, an image processing apparatus according to claim <NUM> is provided. Furthermore, embodiments as defined in claims <NUM>-<NUM> are provided.

According to a third aspect of the invention, a non-transitory computer-readable storage medium as defined in claim <NUM> is provided.

According to a fourth aspect of the invention, a computer program product as defined in claim <NUM> is provided.

Technical solutions provided by the invention and its embodiments may include the following beneficial effects:.

According to the invention, the original image is converted from the first color space to the second color space. Since the correlation between the color components of the respective channels in the second color space of the original image is smaller than the correlation between the color components of the respective channels in the first color space, by utilizing the conversion between the color spaces, the correlation between the color components of the respective channels corresponding to the original image is weakened, so that the channel images formed by the color components of the respective channels are denoised respectively, thereby realizing denoising of the color components of different channels at the same time. The operation is convenient, and denoising effect can be improved, thereby ensuring the quality of the finally obtained target image.

Understandably, the above general description and the following detailed description are exemplary and explanatory and cannot limit the present application. The scope of the invention and its embodiments is defined by the appended claims.

The accompanying drawings here are incorporated into the specification and constitute a part of the specification, show the examples that comply with the present application, and are used to explain the principle of the present application together with the specification.

Some examples will be illustrated in detail here, and examples of the application are shown in the drawings. When the following description involves the drawings, unless otherwise indicated, the same numeral in different drawings indicates the same or similar elements. The implementation modes described in the following examples do not represent all implementation modes consistent with the present application. On the contrary, they are merely examples of apparatuses and methods consistent with some aspects of the present application as described in detail in the appended claims.

In related art, channels of a collected image have a large correlation in an original color space and most of current denoising methods are limited to a fixed color space, so that a color component corresponding to one channel can be subjected to denoising at a time. For example, one of a luminance component or a color component can be denoised, so that the obtained image has poor quality.

An example of the present application provides an image processing method. <FIG> is a flow chart of an image processing method shown according to an example. As shown in <FIG>, the method includes the following steps:.

In step <NUM>, converting an original image represented by a first color space into the original image represented by a second color space, where a correlation between color components of respective channels in the second color space of the original image is smaller than a correlation between color components of respective channels in the first color space.

In step <NUM>, denoising channel images formed by the color components of the respective channels in the second color space respectively to obtain denoised images corresponding to the respective channel images; and in step <NUM>, obtaining a target image based on the denoised images.

The image processing method involved in the example of the present application may be applied to an electronic device. Here, the electronic device includes a mobile terminal and a fixed terminal. The mobile terminal includes: a mobile phone, a tablet computer, a notebook computer, etc. The fixed terminal includes: a personal computer. In other optional examples, the image processing method may also run on a network-side device. The network-side device includes a server, a processing center, etc. Certainly, the electronic device may also be a camera that can be used alone, or a camera that can be embedded in a terminal device.

In the example of the present application, the original image may be an image collected by an image collection module of the electronic device, or an image pre-stored on the electronic device, or an image acquired by the electronic device from other devices, which is not specifically limited here. In some examples, the original image may be an image with noise represented by the first color space.

In the example of the present application, after the original image with noise is acquired, the original image represented by the first color space is converted into the original image represented by the second color space.

Here, the first color space may include: an RGB color space. R represents a red channel, G represents a green channel, and B represents a blue channel. The second color space may include: an Lαβ color space, L represents a luminance component, α and β represent two chroma components, α represents a yellow-blue opponent channel, and β represents a red-green opponent channel. Certainly, in other examples, the first color space may further include: an LMS color space, L represents a first main component, M represents a second main component, and S represents a third main component. In another example, the first color space may include: an HSV color space, H represents hue, S represents saturation, and V represents value. As long as the correlation between the color components of the respective channels in the second color space is smaller than the correlation between the color components of the respective channels in the first color space, there is no specific limitation here.

In the example of the present application, after the original image represented by the first color space is converted into the original image represented by the second color space, the channel images formed by the color components of the respective channels in the second color space are denoised respectively to obtain the denoised images corresponding to the respective channel images.

Taking the second color space being the Lαβ color space as an example, the second color space has a first channel, a second channel and a third channel. In an implementation process, color components in the first channel, the second channel and the third channel can be denoised respectively, so that three denoised images can be obtained.

In some other examples, taking the first color space being the RGB color space and the second color space being the Lαβ color space as an example, in an implementation process, the original image can be converted from the RGB color space to the LMS color space, and then converted from the LMS color space to the Lαβ color space.

A formula of conversion from the RGB color space to the LMS color space is as follows: <MAT>
in formula (<NUM>), L, M and S represent values of the LMS color space; and R, G and B represent values of the RGB color space.

A formula of conversion from the LMS color space to the Lαβ color space is as follows: <MAT>
in formula (<NUM>), L, M and S represent values of the LMS color space; and L, α and β represent values of the Lαβ color space. Here, the logarithm of the color components of the channels in the LMS color space is found respectively in order to converge data and make color distribution more in line with the perception of human eyes.

Here, based on the orthogonality of the color space, the three channels of the color image (original image) can be separated, and the color image can be converted to the LMS color space, and then converted to the Lαβ color space by orthogonalization and decorrelation. Then, the L, α and β channels are subjected to denoising respectively after domain conversion, and the target image in the Lαβ color space is converted into a required color space format, so that the denoised color image can be obtained. The denoising after domain conversion mainly as follows: the original image is converted from a spatial domain to a spectral domain by discrete cosine transform, adaptive Gaussian smoothing is carried out on the image converted to the spectral domain, and then inverse discrete cosine transform is carried out on the image subjected to Gaussian smoothing to obtain the denoised image. In the example of the present application, noise can be separated more effectively by using the adaptive Gaussian smoothing operation.

According to the invention, the original image is converted from the first color space to the second color space. Since the correlation between the color components of the respective channels in the second color space of the original image is smaller than the correlation between the color components of the respective channels in the first color space, by utilizing the conversion between the color spaces, the correlation between the color components of the respective channels corresponding to the original image is weakened, so that the channel images formed by the color components of the respective channels are denoised respectively, and the denoising of the color components of different channels at the same time can be realized. The operation is convenient, and denoising effect can be improved, such that the quality of the finally obtained target image can be ensured.

In some other examples, after the target image is obtained, since the target image is represented by the second color space, in the implementation process, the target image represented by the second color space can be converted into the target image represented by a target color space. Taking the second color space being the Lαβ color space and the target color space being the RGB color space as an example, the target image can be converted from the Lαβ color space to the LMS color space, and then converted from the LMS color space to the RGB color space.

A formula of conversion from the Lαβ color space to the LMS color space is as follows: <MAT>
in formula (<NUM>), L, M and S represent values of the LMS color space; and L, α and β represent values of the Lαβ color space.

A formula of conversion from the LMS color space to the RGB color space is as follows: <MAT>
in formula (<NUM>), L, M and S represent values of the LMS color space; and R, G and B represent values of the RGB color space.

In some other examples, the target color space may include: the LMS color space, the HSV color space and the like, which is not specifically limited here.

In some examples, denoising the channel images formed by the color components of the respective channels in the second color space respectively to obtain the denoised images corresponding to the respective channel images includes:.

In some examples, converting the channel images from the spatial domain to the spectral domain respectively includes:
carrying out discrete cosine transform on the channel images respectively to convert the respective channel images from the spatial domain to the spectral domain.

For example, after the original image is converted to the second color space, discrete cosine transform can be carried out on the color components of the respective channels in the second color space so that the channel images formed by the color components of the respective channels can be converted from the spatial domain to the spectral domain, and Gaussian smoothing can be carried out on the channel images in the spectral domain respectively. After the Gaussian smoothing, inverse discrete cosine transform is carried out on the channel images subjected to Gaussian smoothing to obtain the denoised images corresponding to the respective channel images.

Here, after the channel images are converted from the spatial domain to the spectral domain, a low-frequency part, an intermediate-frequency part and a high-frequency part in the respective channel images are distributed in a set order from an upper left corner to a lower right corner of the respective channel images. At the moment, adaptive Gaussian smoothing can be carried out on the respective channel images in the spectral domain, which is equivalent to carrying out different threshold limited filtrations on different spatial frequencies, for example, an adaptive threshold can be set for the high-frequency part. After Gaussian smoothing is carried out on the respective channel images, inverse discrete cosine transform is carried out on the channel images subjected to Gaussian smoothing to obtain the denoised images corresponding to the respective channel images.

Taking a single-channel image converted to the second color space (for example, the Lαβ color space) being O(r,θ) as an example, the distribution of the spectral domain after the adaptive Gaussian smoothing is: <MAT>.

in formula (<NUM>), T(ρ,ϕ) represents distribution of the spectral domain of the single-channel image subjected to adaptive Gaussian smoothing; ρ and ϕ represent base coordinates in the spectral domain; O(r,θ) represents the single-channel image, and r and θ represent base coordinates in the spatial domain; D[ ] represents a discrete cosine transform function; Gauss (r - r<NUM>,θ) represents a function whose value varies with radius by taking the upper left corner of the single-channel image as the center of a circle; and r<NUM> represents a distance of variation of the center of the circle, that is, a distance from the upper left corner to the center of the image.

The single-channel image may be any channel image in the second color space. Taking the second color space being the Lαβ color space as an example, the single-channel image may be any of an L channel image, a α channel image and a β channel image.

A denoised single-channel image is: <MAT>
in formula (<NUM>), O'(r,θ) represents the denoised single-channel image; T(ρ,ϕ) represents distribution of the spectral domain of the single-channel image subjected to adaptive Gaussian smoothing; and D-<NUM>[ ] represents an inverse discrete cosine transform function.

In the example of the present application, after the respective channel images are converted from the spatial domain to the spectral domain, the channel images converted to the spectral domain are denoised respectively. Based on denoising after domain conversion, the discrete cosine transform and the Gaussian smoothing of the spectral domain are used to denoise the channel images respectively, so that a good denoising effect can be realized.

According to the invention, obtaining the target image based on the denoised images includes:.

After the first channel image is denoised, the first channel image and the first denoised image corresponding to the first channel image are fused to obtain the composite image. Taking the second color space being the Lαβ color space as an example, the first channel image may be the L channel image; and the second channel image may include the α channel image and the β channel image. That is, in an implementation process, the original L channel image and the denoised image of the L channel image can be fused to obtain the composite image.

In the example of the present application, the fusing process may be as follows: color components for forming the composite image is obtained by means of summing and averaging the respective color components in the first channel image and the respective color components in the first denoised image, so that the composite image is obtained. In other optional examples, the first channel image and the first denoised image may also be fused based on other manners, which are not specifically limited here.

After the composite image is obtained, the target image may be obtained based on the composite image and the second denoised image corresponding to the second channel image other than the first channel image. For example, the color components of the composite image and the color components of the second denoised image may be used as the color components of the respective channels of the target image respectively.

Still taking the second color space being the Lαβ color space as an example, the color components of the composite image may be determined as the color components of the L channel, the color components of the first denoised image may be determined as the color components of the α channel, and the color components of the second denoised image may be determined as the color components of the β channel.

According to the invention, after the first channel image and the first denoised image are fused to obtain the composite image, the target image is obtained based on the composite image and the second denoised image corresponding to the second channel image other than the first channel image, so that the obtained target image is more natural and more in line with the perception of human eyes.

In some other examples, a number of the first channel images may be at least one, a number of the second channel images may also be at least one, and here, the number of the first channel images and the number of the second channel images may be the same or different. For example, the number of the first channel images may be one, and the number of the second channel images may be two. For another example, the number of the first channel images may be two, and the number of the second channel images may be one. For still another example, the number of the first channel images and the number of the second channel images are both one, which will not be illustrated one by one here.

In some other examples, after the composite image is obtained, image quality parameters of the composite image may be determined, and whether the image quality parameters are greater than preset standard quality parameters may be determined. When the image quality parameters of the composite image are greater than the standard quality parameters, the target image is obtained based on the composite image and the second denoised image corresponding to the second channel image other than the first channel image. By limiting the quality parameters, the quality of the finally obtained target image can be ensured.

In some other examples, the image quality parameters may include: image luminance, image saturation, image color, or other parameters, which are not specifically limited here. The standard quality parameters may be set according to needs, for example, may be set according to empirical values, or may also be set according to historical image parameters, or furthermore, may further be set according to user needs, which are not specifically limited here.

In some examples, fusing the first channel image in all the channel images and the first denoised image corresponding to the first channel image to obtain the composite image includes:.

In the example of the present application, the first channel image and the first denoised image may be weighted based on the composition weights to obtain the weighted color component, and the composite image is formed based on the weighted color component.

Here, the original image of the first channel (first channel image) and the denoised image (first denoised image) may be composited first to obtain the composite image, and the proportions of the first channel image and the first denoised image in the composite image may be controlled by setting the composition weights, so that image details can be reserved on the basis of denoising of the composite image, such that the high-quality composite image is obtained.

In some examples, weighting the color components corresponding to the first channel image and the color components corresponding to the first denoised image based on the composition weights to obtain the weighted color component includes:.

Here, the first composition weight and the second composition weight may be preset, or may also be determined according to current image parameters of the first channel image and the second denoised image. For example, the current image parameters of the first channel image and the second denoised image may be acquired, and then input into a pre-trained parameter determination model to obtain corresponding first composition weight and second composition weight. The parameter determination model may be obtained by training based on a neural network model. Certainly, in other examples, the first composition weight and the second composition weight may be customized, or the first composition weight and the second composition weight may also be determined according to historical empirical values. Here, manners for determining the first composition weight and the second composition weight will not be enumerated one by one.

In some other examples, the composite image may be expressed as: <MAT>
in formula (<NUM>), O"(r,θ) represents the composite image; α represents the first composition weight; O(r,θ) represents the first channel image; b represents the second composition weight; and O'(r,θ) represents the first denoised image.

Still taking the second color space being the Lαβ color space as an example, in the example of the present application, the original image of the first channel (first channel image) and the denoised image (first denoised image) may be composited first to obtain the composite image, and the proportions of the first channel image and the first denoised image may be controlled by setting coefficients (the first composition weight and the second composition weight), so that image details can be reserved on the basis of denoising of the composite image, such that the high-quality composite image is obtained.

In some other examples, the type of the first channel image may be different from the type of the second channel image. For example, the color components corresponding to the first channel image are used to represent the luminance of the original image, and the color components corresponding to the second channel image are used to represent the color of the original image. For another example, the color components corresponding to the first channel image are used to represent the color of the original image, and the color components corresponding to the second channel image are used to represent the luminance of the original image.

In some other examples, the type of the first channel image may be the same as the type of the second channel image. For example, the color components corresponding to the first channel image and the second channel image are both used to represent the luminance of the original image; or, the color components corresponding to the first channel image and the second channel image are both used to represent the color of the original image.

In some examples, the color components corresponding to the first channel image are used to represent the luminance of the original image. The color components corresponding to the second channel image are used to represent the color of the original image.

In the present application, since the color components corresponding to the first channel image are used to represent the luminance of the original image and the color components corresponding to the second channel image are used to represent the color of the original image, by denoising different channel images at the same time respectively, the luminance noise and the color noise can be reduced at the same time. Compared with the original denoising method of cameras based on camera image signal processing (ISP), the image processing method adds a manner for reducing color noise, and the denoising effect is optimized and regulated by using Gaussian smoothing filtration.

<FIG> is a flow chart of another image processing method shown according to an example. As shown in <FIG>, the method includes the following steps:.

Taking the first color space being the RGB color space, the second color space being the Lαβ color space and the third color space being the LMS color space as an example, in an implementation process, the original image can be converted from the RGB color space to the LMS color space, and then converted from the LMS color space to the Lαβ color space.

In step <NUM>, discrete cosine transform is carried out on a first channel image.

Here, taking the second color space being the Lαβ color space as an example, the first channel image may be the L channel image.

In step <NUM>, Gaussian smoothing is carried out on a discrete cosine transform result corresponding to the first channel image.

In step <NUM>, inverse discrete cosine transform is carried out on a Gaussian smoothing result corresponding to the first channel image to obtain a first denoised image.

In step <NUM>, the first channel image and the first denoised image are fused to obtain a composite image.

In step <NUM>, discrete cosine transform is carried out on a second channel image other than the first channel image.

Here, taking the second color space being the Lαβ color space as an example, the first channel image may include: the L channel image, and the second channel image may include: the α channel image and the β channel image. In some other examples, the first channel image may include: the α channel image; and the second channel image may include: the L channel image and the β channel image. In some other examples, the first channel image may include: the α channel image and the β channel image, and the second channel image may include: the L channel image. The types of the first channel image and the second channel image may be set according to needs and will not be illustrated one by one here.

In step <NUM>, Gaussian smoothing is carried out on a discrete cosine transform result corresponding to the second channel image.

In step <NUM>, inverse discrete cosine transform is carried out on a Gaussian smoothing result corresponding to the second channel image to obtain a second denoised image.

In step <NUM>, a target image represented by the second color space is obtained based on the composite image and the second denoised image.

In step <NUM>, the target image represented by the second color space is converted into the target image represented by a target color space.

In step <NUM>, the target image represented by the target color space is output.

In the example of the present application, the respective channels are subjected to decorrelation by utilizing the orthogonality of the color spaces, so that the correlation between the respective channels of the image is greatly reduced, which is beneficial to the denoising effect. The original image is converted from the spatial domain to the spectral domain by discrete cosine transform, adaptive Gaussian smoothing is carried out on the image converted to the spectral domain, and then inverse discrete cosine transform is carried out on the image subjected to Gaussian smoothing to obtain the denoised image, so that a good denoising effect can be realized. By denoising different channel images at the same time respectively, the luminance noise and the color noise can be reduced at the same time. Compared with the original denoising method of cameras based on camera ISP, the image processing method adds a manner for reducing color noise, and the denoising effect can be optimized by using Gaussian smoothing filtration.

<FIG> is a block diagram of an image processing apparatus shown according to an example. As shown in <FIG>, the apparatus <NUM> mainly includes:.

The acquisition module <NUM> is configured to:.

In some examples, the acquisition module <NUM> is configured to:.

In some examples, the denoising module <NUM> is configured to:.

In some examples, the denoising module is configured to:
carry out discrete cosine transform on the channel images respectively to convert the respective channel images from the spatial domain to the spectral domain.

For the apparatus in the above examples, the specific manner in which each module executes the operation has been described in detail in the examples related to the method, and the detailed description will not be provided here.

<FIG> is a block diagram of an apparatus <NUM> for image processing shown according to an example. For example, the apparatus <NUM> may be a mobile phone, a computer, a digital broadcasting terminal, a message transceiving device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant or the like.

Referring to <FIG>, the apparatus <NUM> may include one or more of the following components: a processing component <NUM>, a memory <NUM>, an electrical component <NUM>, a multimedia component <NUM>, an audio component <NUM>, an input/output (I/O) interface <NUM>, a sensor component <NUM> and a communication component <NUM>.

The processing component <NUM> generally controls overall operations of the apparatus <NUM>, such as operations associated with display, telephone calls, data communication, camera operations and recording operations. The processing component <NUM> may include one or more processors <NUM> to execute instructions to complete all or part of the steps of the above method. In addition, the processing component <NUM> may include one or more modules to facilitate the interaction between the processing component <NUM> and other components. For example, the processing component <NUM> may include a multimedia module to facilitate the interaction between the multimedia component <NUM> and the processing component <NUM>.

The memory <NUM> is configured to store various types of data to support operations in the apparatus <NUM>. Examples of these data include instructions for any application or method operating on the apparatus <NUM>, contact data, phone book data, messages, pictures, videos and the like. The memory <NUM> may be implemented by any type of volatile or non-volatile storage device or a combination of them, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

The electrical component <NUM> provides electric power to various components of the apparatus <NUM>. The electrical component <NUM> may include a power supply management system, one or more power supplies, and other components associated with generation, management and distribution of power for the apparatus <NUM>.

The multimedia component <NUM> includes a screen that provides an output interface between the apparatus <NUM> and the user. In some examples, the screen may include a liquid crystal display (LCD) and a touch panel (TP). The touch panel includes one or more touch sensors to sense touch, slide and gestures on the touch panel. The touch sensor may sense the boundary of a touch or slide operation, and also detect the duration and pressure related to the touch or slide operation. In some examples, the multimedia component <NUM> includes a front camera and/or a rear camera. When the apparatus <NUM> is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each of the front camera and the rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component <NUM> is configured to output and/or input audio signals. For example, the audio component <NUM> includes a microphone (MIC). When the apparatus <NUM> is in an operation mode, such as a call mode, a recording mode and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in the memory <NUM> or transmitted via the communication component <NUM>. In some examples, the audio component <NUM> further includes a speaker for outputting audio signals.

The I/O interface <NUM> provides an interface between the processing component <NUM> and a peripheral interface module. The above peripheral interface module may be a keyboard, a click wheel, buttons and the like. These buttons may include, but are not limited to: a home button, a volume button, a start button and a lock button.

The sensor component <NUM> includes one or more sensors for providing various aspects of state evaluation to the apparatus <NUM>. For example, the sensor component <NUM> may detect an on/off state of the apparatus <NUM> and the relative positioning of the components. For example, the components are a display and a keypad of the apparatus <NUM>. The sensor component <NUM> may also detect the position change of the apparatus <NUM> or a component of the apparatus <NUM>, the presence or absence of contact between a user and the apparatus <NUM>, the orientation or acceleration/deceleration of the apparatus <NUM>, and the temperature change of the apparatus <NUM>. The sensor component <NUM> may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact. The sensor component <NUM> may further include a light sensor, such as a CMOS or CCD image sensor, for use in imaging application. In some examples, the sensor component <NUM> may further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.

The communication component <NUM> is configured to facilitate wired or wireless communication between the apparatus <NUM> and other devices. The apparatus <NUM> may access a wireless network based on a communication standard, such as WiFi, <NUM> or <NUM>, or a combination of them. In an example, the communication component <NUM> receives a broadcast signal from an external broadcast management system or broadcasts related information via a broadcast channel. In an example, the communication component <NUM> further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an example, the apparatus <NUM> may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements, and is used to execute the above method.

In an example, further provided is a non-transitory computer-readable storage medium including instructions is further provided, for example, a memory <NUM> including instructions. The above instructions may be executed by the processor <NUM> of the apparatus <NUM> to complete the above method. For example, the non-transitory computer-readable storage medium may be an ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device or the like.

Provided is a non-transitory computer-readable storage medium. When instructions in the storage medium are executed by a processor of an image processing apparatus, the image processing apparatus is capable of executing an image processing method. The method includes:.

<FIG> is a block diagram of another apparatus <NUM> for image processing shown according to an example. For example, the apparatus <NUM> may be provided as a server. Referring to <FIG>, the apparatus <NUM> includes a processing component <NUM> which further includes one or more processors, and a memory resource represented by a memory <NUM>, for storing instructions executable by the processing component <NUM>, such as applications. Applications stored in the memory <NUM> may include one or more modules each corresponding to a set of instructions. In addition, the processing component <NUM> is configured to execute instructions to execute the above image processing method. The method includes:.

The apparatus <NUM> may further include a power supply component <NUM> configured to execute power supply management of the apparatus <NUM>, a wired or wireless network interface <NUM> configured to connect the apparatus <NUM> to the network, and an input/output (I/O) interface <NUM>. The apparatus <NUM> can operate based on an operating system stored in the memory <NUM>, for example, Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

Those skilled in the art will easily think of other implementation solutions of the present application after considering the description and practicing the present application disclosed herein. The present application is intended to cover any variations, uses or adaptive changes of the present application. These variations, uses or adaptive changes follow the general principles of the present application and include common knowledge or conventional technical means in the technical field that are not disclosed in the present application. The description and the examples are regarded to be exemplary, and the true scope of the present application is defined by the following claims.

Claim 1:
A computer-implemented image processing method, comprising:
converting (<NUM>) an original image represented by a first color space into the original image represented by a second color space, wherein a correlation between color components of respective channels in the second color space of the original image is smaller than a correlation between color components of respective channels in the first color space; and
denoising (<NUM>) channel images formed by the color components of the respective channels in the second color space respectively to obtain denoised images corresponding to the respective channel images; and
obtaining (<NUM>) a target image based on the denoised images;
the method characterized in that the
obtaining (<NUM>) the target image based on the denoised images comprises:
fusing (<NUM>) a first channel image in all the channel images and a first denoised image corresponding to the first channel image to obtain a composite image; and
obtaining (<NUM>) the target image based on the composite image and a second denoised image corresponding to a second channel image other than the first channel image.