CN111756954B - Image processing method, image processing device, electronic equipment and computer readable medium - Google Patents
Image processing method, image processing device, electronic equipment and computer readable medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses an image processing method, an image processing device, an electronic device and a computer readable medium. One embodiment of the method comprises: dividing each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image; adjusting a noise variance corresponding to each sub-image based on an exposure value associated with the target image; carrying out frequency conversion on each sub-image to obtain a converted sub-frequency graph; and processing the sub-frequency graph based on the adjusted noise variance to obtain a denoised target image. The method and the device simply, conveniently and accurately realize the noise removal of the target image.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an image processing method and apparatus, an electronic device, and a computer-readable medium.
Background
With the popularization of various digital instruments and digital products, images and videos become the most common information carriers in human activities, and the images and videos contain a large amount of information of objects, so that the images and videos become a main way for people to obtain external original information. However, the images are often disturbed and affected by various noises during the acquisition, transmission and storage of the images, thereby degrading the images. And the quality of the image preprocessing algorithm is directly related to the effect of subsequent image processing, such as image segmentation, target recognition, edge extraction and the like. Therefore, in order to obtain high-quality digital images, it is necessary to perform noise reduction processing on the images to remove unnecessary information from the signals while maintaining the integrity (i.e., main features) of the original information as much as possible. Therefore, noise reduction processing has been a focus of image processing and computer vision research.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an image processing method, apparatus, device and computer readable medium to solve the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an image processing method, including: dividing each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image; adjusting the noise variance corresponding to each sub-image based on the exposure value associated with the target image; carrying out frequency conversion on each sub-image to obtain a converted sub-frequency graph; and processing the sub-frequency graph based on the adjusted noise variance to obtain a denoised target image.
In a second aspect, some embodiments of the present disclosure provide an image processing apparatus comprising: the dividing unit is configured to divide each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image; an adjusting unit configured to adjust a noise variance corresponding to each sub-image based on an exposure value associated with the target image; a transformation unit configured to perform frequency transformation on each of the sub-images to obtain a transformed sub-frequency map; and the processing unit is configured to process the sub-frequency map based on the adjusted noise variance to obtain a denoised target image.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as in any one of the first aspects.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements a method as in any one of the first aspect.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: first, each channel image corresponding to the target image is divided, and a corresponding sub-image set can be obtained. The method for processing the noise by further dividing each channel image is beneficial to improving the denoising effect of the image. And then reasonably and effectively adjusting the noise variance corresponding to each sub-image through the exposure value associated with the target image. Furthermore, each sub-image is converted into the frequency domain, and the characteristics of the sub-image are observed from the angle of frequency distribution to obtain a sub-frequency map. And finally, processing the sub-frequency graph according to the adjusted noise variance to obtain a denoised target image. The image processing method can simply, conveniently and accurately realize the noise removal of the target image.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a schematic diagram of an application scene diagram of an image processing method of some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an image processing method according to the present disclosure;
FIG. 3 is a graph of frequency variation for some embodiments of an image processing method according to the present disclosure;
FIG. 4 is a flow diagram of further embodiments of an image processing method according to the present disclosure;
FIG. 5 is a schematic block diagram of some embodiments of an image processing apparatus according to the present disclosure;
FIG. 6 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of one application scenario of an image processing method according to some embodiments of the present disclosure.
As shown in fig. 1, the electronic device 101 divides each channel image 103 corresponding to the target image 102 according to a certain specification, and obtains a sub-image set in the sub-image set group 104 corresponding to each channel image 103. Wherein, the channel image 103 includes: a red channel image, a green channel image, and a blue channel image. The sub-image set group may include: a first set of sub-images, a second set of sub-images, a third set of sub-images, and a fourth set of sub-images. Optionally, the electronic device 101 may divide each channel image 103 corresponding to the target image 102 according to a ratio of 4 × 4, so as to obtain a sub-image set in the sub-image set group 104 corresponding to each channel image 103.
Then, the noise variance corresponding to each sub-image is correspondingly adjusted through the exposure value 105 associated with the target image 102, so as to obtain an adjusted noise variance 106. Optionally, the exposure value 105 associated with the target image 102 may be used to linearly adjust the noise variance corresponding to each sub-image, so as to obtain an adjusted noise variance 106.
And then, carrying out frequency conversion on each sub-image to obtain a converted sub-frequency map. As an example, the sub-frequency map corresponding to each sub-image may be obtained by performing frequency transformation on each sub-image in each sub-image set in the sub-image set group 104, that is, each sub-frequency map in each sub-frequency map set in the sub-frequency map set group 107. The set of sub-frequency diagrams 107 may include: a first set of sub-frequency maps, a second set of sub-frequency maps, a third set of sub-frequency maps, and a fourth set of sub-frequency maps.
Finally, the sub-frequency map is processed through the adjusted noise variance 106, and a denoised target image 108 is obtained. Optionally, the adjusted noise variance 106 may be normalized first. The normalized result may then be multiplied by the pixel values in the sub-frequency map to obtain an updated sub-frequency map. Finally, the updated sub-frequency map is subjected to inverse frequency transformation, so that the denoised target image 108 can be obtained.
It should be noted that the image processing method may be executed by the electronic device 101. The electronic device 101 may be hardware or software. When the electronic device is hardware, the electronic device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the electronic device 101 is embodied as software, it may be implemented as multiple pieces of software or software modules, for example, to provide distributed services, or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of electronic devices in fig. 1 is merely illustrative. There may be any number of electronic devices, as desired for implementation.
With continued reference to fig. 2, a flow 200 of some embodiments of an image processing method according to the present disclosure is shown. The image processing method comprises the following steps:
In some embodiments, an execution subject of the image processing method (e.g., the electronic device 101 shown in fig. 1) may divide each channel image corresponding to the target image in various ways, resulting in a sub-image set corresponding to each channel image. The target image may be an image in RAW format to be divided. The channel image may be a color channel image of the target image described above. The color channels may include a red color channel, a green color channel, and a blue color channel. Here, the channel image corresponding to the target image may include a red channel, a green channel, and a blue channel. As an example, each channel image corresponding to the target image is randomly divided by a preset number of parts, and a sub-image set corresponding to each channel image is obtained.
In some optional implementation manners of some embodiments, dividing each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image may be: and dividing each channel image according to a preset size to obtain a sub-image set corresponding to each channel image. And the two adjacent sub-images in the sub-image set have pixel overlapping. This pixel overlap process is used to eliminate traces left by subsequent sub-image mergers. As an example, each of the channel images is divided according to a size of 16 × 16, so as to obtain a sub-image set corresponding to each of the channel images. As an example, when each channel image is divided, 8 pixels overlapping each other are ensured to exist between two adjacent sub-images in the divided sub-image set.
In some embodiments, the executing entity may increase or decrease the noise variance corresponding to each sub-image by a predetermined threshold according to the exposure value associated with the target image. Wherein the exposure value may be a scene brightness associated with the target image. The exposure value may be read from a camera system that captures the target image, and may be obtained by the following formula:
where EV represents an exposure value, f represents a diaphragm of the camera, iso represents sensitivity, and t represents an exposure time.
It should be noted that the noise of the image may refer to factors that hinder human perception of the received source information. The noise of the above-mentioned image may include, but is not limited to, at least one of: additive noise, multiplicative noise, quantization noise, "salt and pepper" noise.
In some embodiments, the executing entity may perform frequency transformation on each sub-image to obtain a transformed sub-frequency map. The sub-frequency map may correspond to a gradient distribution map of the intensity of the gray scale change in the sub-image. Wherein the pixel values of the sub-frequency map correspond to the frequency information of the sub-image. It should be noted that different frequency information has different roles in the image structure. The main component of the image is low frequency information. The low frequency information forms the basic grey scale of the image and has little effect on the determination of the image result. The intermediate frequency information of the image determines the basic structure of the image, forming the main edge structure of the image. The high frequency information of the image forms the edges and details of the image, which is a further enhancement of the image content over the intermediate frequency information.
As an example, the execution subject may perform discrete cosine transform on each of the sub-images to obtain a transformed sub-frequency map.
In some optional implementation manners of some embodiments, the frequency transforming each adjusted sub-image to obtain the transformed sub-frequency map may be: and performing Fourier transform on each adjusted sub-image to obtain a transformed sub-frequency map.
As shown in fig. 3, the image 301 may be a sub-image. Then, the sub-image is frequency-transformed to obtain an image 302 as a transformed sub-frequency map.
And 204, processing the sub-frequency map based on the adjusted noise variance to obtain a denoised target image.
In some embodiments, the execution subject may process the sub-frequency map based on the adjusted noise variance to obtain a denoised target image. As an example, the adjusted noise variance may be normalized to obtain a normalized result, the normalized result is multiplied by a pixel value corresponding to the sub-frequency map, and the obtained multiplied result is subjected to inverse discrete cosine transform to obtain a denoised target image.
In some optional implementation manners of some embodiments, the processing the sub-frequency map through the adjusted noise variance to obtain a denoised target image may include the following steps:
and step one, processing the sub-frequency graph based on the adjusted noise variance to obtain a processed sub-image.
And secondly, processing each processed sub-image included in each channel image to obtain a denoised target image.
Optionally, the processing the sub-frequency map based on the adjusted noise variance to obtain a processed sub-image may include the following steps:
in the first step, the pixels on the sub-frequency map are processed based on the adjusted noise variance. As an example, the pixels on the above-described sub-frequency map may be processed by the following formula:
wherein i represents the abscissa value of the pixel on the sub-frequency chart, and j represents the sub-frequency chartThe ordinate value of the upper pixel. Sigma2Represents the above adjusted noise variance, F(i，j)Pixel values with horizontal coordinate i and vertical coordinate j in the sub-frequency chart are shown, and F (i, j) is a processed pixel value with horizontal coordinate i and vertical coordinate j in the sub-frequency chart.
And secondly, performing inverse frequency transformation on the processed sub-frequency map to obtain the processed sub-image.
Optionally, the processing the processed sub-images included in each channel image to obtain the denoised target image may include the following steps:
and firstly, combining the processed sub-images included in each channel image to obtain a combined channel image.
And secondly, combining the combined channel images to obtain the denoised target image.
As can be obtained from the foregoing embodiments, first, each channel image corresponding to the target image is divided, and a corresponding sub-image set can be obtained. The method for processing the noise by further dividing each channel image contributes to improving the denoising effect of the image. And then reasonably and effectively adjusting the noise variance corresponding to each sub-image through the exposure value associated with the target image. Furthermore, each sub-image is converted into the frequency domain, and the characteristics of the sub-image are observed from the angle of frequency distribution to obtain a sub-frequency map. And finally, processing the sub-frequency graph according to the adjusted noise variance to obtain a denoised target image. The image processing method can simply, conveniently and accurately remove the noise of the target image.
With continued reference to fig. 4, a flow 400 of further embodiments of an image processing method according to the present disclosure is shown. The image processing method comprises the following steps:
In some embodiments, the execution subject may determine a root mean square value of the set of pixel values corresponding to each of the sub-images as the noise variance corresponding to each of the sub-images. As an example, the set of pixel values of the sub-image is [ (2,2,2), (4, 4), (6,6,6) ], and a root mean square value corresponding to the set of pixel values of the sub-image is 18.67 as the noise variance.
In some embodiments, the execution main body may adjust the noise variance based on the exposure value. As an example, the smaller the noise variance can be adjusted according to the smaller the exposure value (in dark scene conditions). The larger the exposure value (in bright-field conditions), the larger the noise variance is adjusted.
And step 404, performing frequency transformation on each sub-image to obtain a transformed sub-frequency map.
And 405, processing the sub-frequency graph based on the adjusted noise variance to obtain a denoised target image.
In some embodiments, specific implementation and technical effects of steps 401, 404, and 405 may refer to steps 201, 203, and 204 in those embodiments corresponding to fig. 2, and are not described herein again.
As can be seen from fig. 4, the flow 400 of the image processing method in some embodiments corresponding to fig. 4 highlights a specific step of adjusting the noise variance corresponding to each sub-image, compared to the description of some embodiments corresponding to fig. 2. Therefore, the solutions described in the embodiments determine the noise variance corresponding to each sub-image and adjust the noise variance based on the exposure value, so as to accurately and conveniently remove the noise from the target image.
With continued reference to fig. 5, as an implementation of the above-described method for the above-described figures, the present disclosure provides some embodiments of an image processing apparatus, which correspond to those of the method embodiments described above with reference to fig. 2, and which may be applied in particular to various electronic devices.
As shown in fig. 5, an image processing apparatus 500 of some embodiments includes: a dividing unit 501, an adjusting unit 502, a transforming unit 503, and a processing unit 504. The dividing unit 501 is configured to divide each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image. An adjusting unit 502 configured to adjust the noise variance corresponding to each sub-image based on the exposure value associated with the target image. A transforming unit 503, configured to perform frequency transformation on each of the sub-images to obtain a transformed sub-frequency map. And the processing unit 504 is configured to process the sub-frequency map based on the adjusted noise variance to obtain a denoised target image.
In some optional implementations of some embodiments, the dividing unit 501 may be further configured to: dividing each channel image according to a preset size to obtain a sub-image set corresponding to each channel image, wherein pixel overlapping exists between two adjacent sub-images in the sub-image set.
In some optional implementations of some embodiments, the adjusting unit 502 may be further configured to: determining a noise variance corresponding to each sub-image; and adjusting the noise variance based on the exposure value.
In some optional implementations of some embodiments, the adjusting unit 502 may be further configured to: and determining a root mean square value of the pixel value set corresponding to each sub-image as a noise variance corresponding to each sub-image.
In some optional implementations of some embodiments, the transformation unit 503 may be further configured to: and performing Fourier transform on each adjusted sub-image to obtain a transformed sub-frequency map.
In some optional implementations of some embodiments, the processing unit 504 may be further configured to: processing the sub-frequency graph based on the adjusted noise variance to obtain a processed sub-image; and processing each processed sub-image included in each channel image to obtain a denoised target image.
In some optional implementations of some embodiments, the processing unit 504 may be further configured to: processing pixels on the sub-frequency map based on the adjusted noise variance; and performing inverse frequency transformation on the processed sub-frequency map to obtain the processed sub-image.
In some optional implementations of some embodiments, the processing unit 504 may be further configured to: combining the processed sub-images included in each channel image to obtain a combined channel image; and combining the combined channel images to obtain the denoised target image.
It will be understood that the elements described in the apparatus 500 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 500 and the units included therein, and are not described herein again.
Referring now to FIG. 6, a block diagram of an electronic device (e.g., the electronic device of FIG. 1) 600 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, or the like; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus described above; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: dividing each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image; adjusting the noise variance corresponding to each sub-image based on the exposure value associated with the target image; carrying out frequency conversion on each sub-image to obtain a converted sub-frequency graph; and processing the sub-frequency graph based on the adjusted noise variance to obtain a denoised target image.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code 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 latter scenario, 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).
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 code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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 that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, which may be described as: a processor includes a dividing unit, an adjusting unit, a transforming unit, and a processing unit. The names of these units do not form a limitation on the unit itself in some cases, for example, the dividing unit may also be described as "a unit that divides each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
According to one or more embodiments of the present disclosure, there is provided an image processing method including: dividing each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image; adjusting the noise variance corresponding to each sub-image based on the exposure value associated with the target image; carrying out frequency conversion on each sub-image to obtain a converted sub-frequency graph; and processing the sub-frequency graph based on the adjusted noise variance to obtain a denoised target image.
According to one or more embodiments of the present disclosure, the adjusting the noise variance corresponding to each sub-image based on a predetermined exposure value associated with the target image includes: determining a noise variance corresponding to each sub-image; and adjusting the noise variance based on the exposure value.
According to one or more embodiments of the present disclosure, the determining the noise variance corresponding to each sub-image includes: and determining a root mean square value of the pixel value set corresponding to each sub-image as a noise variance corresponding to each sub-image.
According to one or more embodiments of the present disclosure, the frequency transforming each adjusted sub-image to obtain a transformed sub-frequency map includes: and performing Fourier transform on each adjusted sub-image to obtain a transformed sub-frequency map.
According to one or more embodiments of the present disclosure, the processing the sub-frequency map based on the adjusted noise variance to obtain a denoised target image includes: processing the sub-frequency graph based on the adjusted noise variance to obtain a processed sub-image; and processing each processed sub-image corresponding to each channel image to obtain a denoised target image.
According to one or more embodiments of the present disclosure, the processing the sub-frequency map based on the adjusted noise variance to obtain a processed sub-image includes: processing the pixels on the sub-frequency map based on the adjusted noise variance; and performing inverse frequency transformation on the processed sub-frequency map to obtain the processed sub-image.
According to one or more embodiments of the present disclosure, the processing the processed sub-image corresponding to each channel image to obtain a denoised target image includes: combining the processed sub-images included in each channel image to obtain a combined channel image; and combining the combined channel images to obtain the denoised target image.
According to one or more embodiments of the present disclosure, there is provided an image processing apparatus including: the dividing unit is configured to divide each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image; an adjusting unit configured to adjust a noise variance corresponding to each sub-image based on an exposure value associated with the target image; a transformation unit configured to perform frequency transformation on each of the sub-images to obtain a transformed sub-frequency map; and the processing unit is configured to process the sub-frequency map based on the adjusted noise variance to obtain a denoised target image.
According to one or more embodiments of the present disclosure, the dividing unit may be further configured to: and dividing each channel image according to a preset size to obtain a sub-image set corresponding to each channel image, wherein pixel overlapping exists between two adjacent sub-images in the sub-image set.
According to one or more embodiments of the present disclosure, the adjusting unit may be further configured to: determining a noise variance corresponding to each sub-image; and adjusting the noise variance based on the exposure value.
According to one or more embodiments of the present disclosure, the adjusting unit may be further configured to: and determining a root mean square value of the pixel value set corresponding to each sub-image as a noise variance corresponding to each sub-image.
According to one or more embodiments of the present disclosure, the transformation unit may be further configured to: and performing Fourier transform on each adjusted sub-image to obtain a transformed sub-frequency map.
In accordance with one or more embodiments of the present disclosure, the processing unit may be further configured to: processing the sub-frequency graph based on the adjusted noise variance to obtain a processed sub-image; and processing each processed sub-image corresponding to each channel image to obtain a denoised target image.
In accordance with one or more embodiments of the present disclosure, the processing unit may be further configured to: processing pixels on the sub-frequency map based on the adjusted noise variance; and carrying out inverse frequency transformation on the processed sub-frequency map to obtain the processed sub-image.
In accordance with one or more embodiments of the present disclosure, the processing unit may be further configured to: combining the processed sub-images included in each channel image to obtain a combined channel image; and combining the combined channel images to obtain the denoised target image.
According to one or more embodiments of the present disclosure, there is provided an electronic device including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as described in any of the embodiments above.
According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, wherein the program, when executed by a processor, implements the method as described in any of the embodiments above.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combinations of the above-mentioned features, and other embodiments in which the above-mentioned features or their equivalents are combined arbitrarily without departing from the spirit of the invention are also encompassed. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (9)
1. An image processing method comprising:
dividing each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image;
adjusting a noise variance corresponding to each sub-image based on an exposure value associated with the target image, including: determining a root mean square value of a set of pixel values corresponding to said each sub-image as a noise variance corresponding to said each sub-image; adjusting the noise variance based on the exposure value;
performing frequency transformation on each sub-image to obtain a transformed sub-frequency graph;
and processing the sub-frequency graph based on the adjusted noise variance to obtain a denoised target image.
2. The method according to claim 1, wherein the dividing each channel image corresponding to the target image into the sub-image sets corresponding to the each channel image comprises:
dividing each channel image according to a preset size to obtain a sub-image set corresponding to each channel image, wherein pixel overlapping exists between two adjacent sub-images in the sub-image set.
3. The method of claim 1, wherein the frequency transforming each sub-image to obtain a transformed sub-frequency map comprises:
and carrying out Fourier transform on each sub-image to obtain a transformed sub-frequency map.
4. The method of claim 1, wherein the processing the sub-frequency map based on the adjusted noise variance to obtain a denoised target image comprises:
processing the sub-frequency graph based on the adjusted noise variance to obtain a processed sub-image;
and processing each processed sub-image corresponding to each channel image to obtain a denoised target image.
5. The method of claim 4, wherein the processing the sub-frequency map based on the adjusted noise variance to obtain a processed sub-image comprises:
processing pixels on the sub-frequency map based on the adjusted noise variance;
and carrying out inverse frequency transformation on the processed sub-frequency graph to obtain the processed sub-image.
6. The method according to claim 4, wherein said processing the respective processed sub-images included in each channel image to obtain a denoised target image comprises:
combining the processed sub-images corresponding to each channel image to obtain a combined channel image;
and combining the combined channel images to obtain the denoised target image.
7. An image processing apparatus comprising:
the dividing unit is configured to divide each channel image corresponding to the target image to obtain a sub-image set corresponding to each channel image;
an adjustment unit configured to adjust a noise variance corresponding to each sub-image based on an exposure value associated with the target image, including: determining a root mean square value of a set of pixel values corresponding to said each sub-image as a noise variance corresponding to said each sub-image; adjusting the noise variance based on the exposure value;
a transformation unit configured to perform frequency transformation on each sub-image to obtain a transformed sub-frequency map;
and the processing unit is configured to process the sub-frequency map based on the adjusted noise variance to obtain a denoised target image.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-6.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any one of claims 1-6.
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