CN115953310A - Image noise reduction method and device, chip and module equipment - Google Patents
Image noise reduction method and device, chip and module equipment Download PDFInfo
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Abstract
The application discloses an image noise reduction method, an image noise reduction device, a chip and module equipment, wherein the method comprises the following steps: acquiring an image to be processed; determining pyramid layer number N corresponding to an image to be processed; decomposing an image to be processed into N layers of sub-images with a pyramid structure; performing reconstruction processing and noise reduction processing on the M layers of sub-images to obtain M layers of target images, wherein M is N-1, the M layers of sub-images are images except the highest layer image in the N layers of sub-images, and the lowest layer of target image in the M layers of target images is an image subjected to noise reduction of the image to be processed; in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on the basis of an image obtained by slicing the i-th layer sub-image and an image obtained by slicing the i + 1-th layer target image, wherein the i-th layer sub-image is any layer of image in the M-layer sub-images. Based on the method described in the application, the image denoising performance can be ensured, and the image quality is improved.
Description
Technical Field
The invention relates to the field of computers, in particular to an image noise reduction method, an image noise reduction device, a chip and module equipment.
Background
With the increasing evolution of camera functions in mobile devices, the requirements for image quality are also increasing. An image noise reduction link in an Image Signal Processor (ISP) chip determines the quality of a photographed image observed by the human eyes, and image noise reduction is usually required in both image preview and photographing. The traditional image noise reduction is carried out in a single frame, such as common gaussian filtering, bilateral filtering and the like, and the noise reduction methods have certain limitations in the aspects of noise removal and detail preservation. In order to improve the noise reduction capability, more and more ISP chips start to support image noise reduction in a pyramid structure, and the noise reduction can support noise reduction with higher strength while maintaining details. The pyramid structure is a mode of decomposing an original image into a plurality of images with different sizes, processing the images, and then re-fusing the images.
However, the processing performance of image noise reduction using the existing pyramid structure is limited, the image noise reduction performance cannot be guaranteed, and the image quality is also reduced.
Disclosure of Invention
The application provides an image noise reduction method, an image noise reduction device, a chip and module equipment, which can ensure the image noise reduction performance and improve the image quality.
In a first aspect, the present application provides an image denoising method, including: acquiring an image to be processed; determining pyramid layer number N corresponding to the image to be processed, wherein the pyramid layer number is the layer number for decomposing the image to be processed into a pyramid structure, and N is an integer greater than 1; decomposing the image to be processed into N layers of sub-images with pyramid structures; performing reconstruction processing and noise reduction processing on M layers of sub-images to obtain M layers of target images, wherein M is N-1, the M layers of sub-images are images except the highest layer image in the N layers of sub-images, and the target image at the bottommost layer in the M layers of target images is an image subjected to noise reduction on the image to be processed; in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on an image obtained by slicing the i-th layer sub-image and an image obtained by slicing the i + 1-th layer target image, and the i-th layer sub-image is any one layer of image in the M-layer sub-images.
Based on the method described in the first aspect, the image to be processed is decomposed into the multilayer sub-images with the pyramid structure, the multilayer sub-images are reconstructed, and the noise reduction processing is performed on each layer of reconstructed image. Meanwhile, in the process of reconstructing each layer of sub-image, a method of determining whether to slice according to the image size is adopted, so that the effectiveness of image noise reduction of the pyramid structure can be ensured, excessive slicing processing can not be caused, and the reduction of the processing performance of the pyramid structure is avoided.
In a possible implementation manner, the ith layer target image is obtained by performing noise reduction on a reconstructed image after reconstructing the ith layer sub-image based on the (i + 1) th layer target image and the ith layer sub-image, and the (i + 1) th layer target image is obtained by reconstructing the (i + 1) th layer sub-image and performing noise reduction on the reconstructed image.
In one possible implementation, acquiring an image to be processed includes: carrying out slicing processing on the complete image to obtain a plurality of images to be processed; the method further comprises the following steps: and merging the target image of the bottommost layer corresponding to each image to be processed in the plurality of images to be processed to obtain a first image, wherein the first image is the image of the complete image subjected to noise reduction. Based on the mode, the processing performance of the pyramid structure can be ensured, and the image noise reduction quality can be improved.
In one possible implementation, the slice processing is performed on the complete image, and includes: and if the width of the complete image is larger than a preset threshold value, carrying out slicing processing on the complete image. Based on the mode, the effectiveness and the reasonability of the image slice are ensured, and the processing performance of the pyramid structure is ensured.
In one possible implementation, decomposing the image to be processed into N layers of sub-images includes: filtering the image to be processed to obtain a second image; and carrying out sampling processing on the second image for N times to obtain N layers of sub-images. Based on the mode, the image can be effectively decomposed, and the decomposition efficiency is improved.
In a second aspect, the present application provides an image noise reduction apparatus, comprising: an acquisition unit for acquiring an image to be processed; a determining unit, configured to determine a pyramid layer number N corresponding to the image to be processed, where the pyramid layer number is a layer number obtained by decomposing the image to be processed into a pyramid structure, and N is an integer greater than 1; the processing unit is used for decomposing the image to be processed into N layers of sub-images with pyramid structures; the processing unit is further configured to perform reconstruction processing and noise reduction processing on the M layers of sub-images to obtain M layers of target images, where M is N-1, the M layers of images are images of the N layers of images except for a highest layer of image, and a target image of a lowest layer of the M layers of target images is an image of the image to be processed after noise reduction; in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on the basis of the image obtained by slicing the i-th layer sub-image and the image obtained by slicing the i + 1-th layer target image, and the i-th layer sub-image is any layer of image in the M-layer sub-images.
In a third aspect, the present application provides a chip comprising a processor and a communication interface, the processor being configured to cause the chip to perform the method of the first aspect or any one of its possible implementations.
In a fourth aspect, the present application provides a module device, which includes a communication module, a power module, a storage module, and a chip, wherein: the power module is used for providing electric energy for the module equipment; the storage module is used for storing data and instructions; the communication module is used for carrying out internal communication of the module equipment or is used for carrying out communication between the module equipment and external equipment; the chip is configured to perform the method of the first aspect or any one of its possible implementations.
In a fifth aspect, an embodiment of the present invention discloses an image noise reduction apparatus, which includes a memory for storing a computer program and a processor, where the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method in the first aspect or any possible implementation manner thereof.
In a sixth aspect, the present application provides a computer-readable storage medium having stored thereon computer-readable instructions that, when run on an image noise reduction apparatus, cause the image noise reduction apparatus to perform the method of the first aspect or any possible implementation thereof.
In a seventh aspect, the present application provides a computer program or a computer program product comprising code or instructions which, when run on a computer, cause the computer to perform the method as in the first aspect or any one of its possible implementations.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic diagram of an image pyramid structure according to an embodiment of the present disclosure;
FIG. 2 is a schematic flowchart of an image denoising method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an image noise reduction apparatus according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of another image noise reduction apparatus provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a module apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the following embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the specification of this application and the appended claims, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the listed items.
It should be noted that the terms "first," "second," "third," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to ensure the image noise reduction performance and improve the image quality, the application provides an image noise reduction method. In a specific implementation, the above-mentioned image noise reduction method may be executed by a terminal device. The following describes a terminal device:
the terminal device includes a device for providing voice and/or data connectivity to a user, for example, the terminal device is a device with wireless transceiving function, and can be deployed on land, including indoors or outdoors, hand-held, worn or vehicle-mounted; can also be deployed on the water surface (such as a ship and the like); and may also be deployed in the air (e.g., airplanes, balloons, satellites, etc.). The terminal may be a mobile phone (mobile phone), a tablet computer (Pad), a computer with wireless transceiving function, a Virtual Reality (VR) terminal device, an Augmented Reality (AR) terminal device, a wireless terminal in industrial control (industrial control), a vehicle-mounted terminal device, a wireless terminal in unmanned driving (self driving), a wireless terminal in remote medical (remote medical), a wireless terminal in smart grid (smart grid), a wireless terminal in transportation safety (transportation safety), a wireless terminal in smart city (smart city), a wireless terminal in wearable home (smart home), a terminal device, and the like. The embodiments of the present application do not limit the application scenarios. A terminal may also be referred to as a terminal device, user Equipment (UE), access terminal device, in-vehicle terminal, industrial control terminal, UE unit, UE station, mobile station, remote terminal device, mobile device, UE terminal device, wireless communication device, UE agent, or UE device, among others. The terminals may also be fixed or mobile. In this embodiment of the present application, the apparatus for implementing the function of the terminal device may be the terminal device, or may be an apparatus capable of supporting the terminal device to implement the function, for example, a chip system or a combined device and a component that can implement the function of the terminal device, and the apparatus may be installed in the terminal device.
In order to facilitate understanding of the scheme provided by the embodiment of the present application, an image pyramid structure is described below:
the image pyramid is a kind of multi-scale representation of an image, and is an effective but conceptually simple structure to interpret an image in multi-resolution. The image pyramid of an image is a series of image resolution sets that progressively decrease in a pyramid shape (bottom-up) and are derived from the same original image. It is obtained by down-sampling in steps, and sampling is not stopped until a certain end condition is reached. We compare the images one level at a time to a pyramid, with the higher the level, the smaller the image and the lower the resolution. Gaussian and laplacian pyramids are the two most common image pyramids. As shown in fig. 1, the image pyramid includes 5 layers of images, i.e., a 1 st layer image, a 2 nd layer image, a 3 rd layer image, a 4 th layer image, and a 5 th layer image, wherein the image at the bottom layer (i.e., the 1 st layer image) is the original image and the size of the image is reduced once for each layer. Image pyramids are commonly used in image scaling, image reconstruction, image fusion, image enhancement techniques.
The image denoising method provided by the present application is further described in detail below. Referring to fig. 2, fig. 2 is a schematic flowchart of an image denoising method according to an embodiment of the present disclosure. As shown in fig. 2, the image noise reduction method includes steps S201 to S204, and the method shown in fig. 2 is performed such that the main body includes a terminal device (such as the above-mentioned terminal device). Alternatively, the method execution body shown in fig. 2 includes a chip, a chip system, or a processor in the terminal device. Alternatively, the method execution main body shown in fig. 2 may also be a logic module or software that can implement all or part of the functions of the terminal device. The embodiment of the present application does not limit the main execution body of the image denoising method. Fig. 2 illustrates an example in which a terminal device is used as an execution subject. Wherein:
s201, the terminal equipment acquires an image to be processed.
In this embodiment of the present application, the image to be processed may be a photograph taken by a camera, may also be a camera preview image, and may also be a frame of a video, which is not limited herein. In addition, the image to be processed may be obtained by the terminal device directly from a certain application program, may be extracted from a database, may be obtained from another device, and is not limited herein.
S202, the terminal device determines the pyramid layer number N corresponding to the image to be processed.
In the embodiment of the present application, the pyramid layer number is a layer number for decomposing the image to be processed into a pyramid structure, and N is an integer greater than 1. The terminal device may perform data processing on the image to be processed by using a software tool, and calculate the number of pyramid layers corresponding to the image to be processed, that is, the number of layers of the pyramid structure that needs to be decomposed into. In general, the length and width of each image layer in the pyramid structure are reduced to half of those of the previous image layer, and since the length and width of each image layer are reduced to half of those of the previous image layer, the reduction speed of the image is very fast, and thus the number of layers of the pyramid is 3-6, but the pyramid is not limited thereto.
It should be noted that, the greater the number of pyramid layers is, the better the quality of the processed image is, but as the number of pyramid layers increases, the required hardware resources also increase continuously, and if the number of pyramid layers exceeds a preset threshold (for example, 6 layers), the hardware resources are overloaded, thereby affecting the hardware performance of the terminal device. Therefore, the terminal device also needs to ensure that the determined pyramid layer number cannot exceed the preset layer number threshold.
S203, the terminal device decomposes the image to be processed into N layers of sub-images with a pyramid structure.
In the embodiment of the present application, the terminal device sequentially stores the N layers of sub-images decomposed into the pyramid structure in a memory (e.g., a Double Data Rate SDRAM (DDR)), and when reconstructing the image subsequently, the terminal device can directly extract the N layers of sub-images from the memory.
In a possible implementation manner, when the terminal device decomposes the image to be processed into N layers of sub-images, a specific implementation manner may be: filtering the image to be processed to obtain a second image; and carrying out sampling processing on the second image for N times to obtain N layers of sub-images. Based on the mode, the image can be effectively decomposed, and the decomposition efficiency is improved.
Taking a gaussian pyramid as an example, the bottom layer of the pyramid structure is the image to be processed, each layer above the pyramid structure is processed by gaussian fuzzy filtering, and then the size of the image is reduced by down-sampling, so that N layers of sub-images are obtained by decomposition.
And S204, the terminal equipment carries out reconstruction processing and noise reduction processing on the M layers of sub-images to obtain M layers of target images.
In this embodiment, M is N-1, the M-layer sub-images are images of the N-layer sub-images except for the highest-layer image, and the target image of the lowest layer of the M-layer target images is an image obtained by denoising the image to be processed. In the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on an image obtained by slicing the i-th layer sub-image and an image obtained by slicing the i + 1-th layer target image, and the i-th layer sub-image is any one layer of image in the M layers of sub-images.
That is to say, the terminal device reconstructs each layer of sub-image in the M layers of sub-images, and each layer of reconstructed image is subjected to noise reduction processing, so that the multi-scale noise reduction processing can greatly reduce image noise, obtain a good noise reduction effect on brightness and color, and improve image quality. Meanwhile, in the process of reconstructing each layer of sub-image, a method of determining whether to slice according to the image size is adopted, so that the effectiveness of image noise reduction of the pyramid structure can be ensured, excessive slicing processing can not be caused, the reduction of the processing performance of the pyramid structure is avoided, and the time for image processing is prolonged.
In a possible implementation manner, the ith layer target image is obtained by performing noise reduction processing on a reconstructed image after reconstructing the ith layer sub-image based on the (i + 1) th layer target image and the ith layer sub-image, and the (i + 1) th layer target image is obtained by reconstructing the (i + 1) th layer sub-image and performing noise reduction processing on the reconstructed image.
For example, the terminal device decomposes the image to be processed into 5-layer sub-images with a pyramid structure, namely a 1 st-layer sub-image, a 2 nd-layer sub-image, a 3 rd-layer sub-image and a 4 th-layer sub-image, wherein the 1 st-layer sub-image is an original image of the image to be processed. In the first reconstruction, the terminal equipment reconstructs a 3 rd layer sub-image by using the 4 th layer sub-image and the 3 rd layer sub-image, and then the terminal equipment performs filter denoising processing on the reconstructed image to obtain a 3 rd layer target image; in the second reconstruction, the terminal equipment reconstructs the layer 2 sub-image by using the layer 3 target image and the layer 2 sub-image, and then the terminal equipment performs filter denoising processing on the reconstructed image to obtain a layer 2 target image; in the 3 rd reconstruction, the terminal device reconstructs the layer 1 sub-image by using the layer 2 target image and the layer 1 sub-image, and then the terminal device performs filter denoising processing on the reconstructed image to obtain the layer 1 target image. Therefore, the terminal device determines 4 layers of target images, wherein the target image at the bottommost layer (namely, the target image at the 1 st layer) is the image of the image to be processed after noise reduction.
In addition, assuming that in the 3 rd reconstruction, the width of the layer 2 target image used by the terminal device is greater than a preset threshold (e.g. 2592), the layer 2 target image needs to be sliced, so as to obtain slice 1 and slice 2; meanwhile, the layer 1 sub-image needs to be sliced to obtain slice 3 and slice 4. Then the terminal equipment reconstructs an image 1 by using the slice 1 and the slice 3, reconstructs an image 2 by using the slice 2 and the slice 4, and finally, the image 1 and the image 2 are combined to obtain a reconstructed layer 1 sub-image.
In a possible implementation manner, when the terminal device acquires the image to be processed, a specific implementation manner may be: carrying out slicing processing on the complete image to obtain a plurality of images to be processed; the method further comprises the following steps: and the terminal equipment combines the target image of the bottommost layer corresponding to each image to be processed in the plurality of images to be processed to obtain a first image, wherein the first image is the image of the complete image after noise reduction. That is to say, the complete image is sliced into a plurality of images to be respectively subjected to decomposition, reconstruction and noise reduction, and the images are combined after the processing, so that the processing performance of the pyramid structure is ensured and the noise reduction quality of the images is improved.
For example, after the complete image slicing process, 3 images to be processed are obtained, which are an image a to be processed, an image b to be processed, and an image c to be processed, respectively. After the terminal device respectively performs decomposition, reconstruction and noise reduction on the 3 images to be processed, merging the target image a at the bottommost layer obtained by processing the image a to be processed, the target image B at the bottommost layer obtained by processing the image B to be processed and the target image C at the bottommost layer obtained by processing the image C to be processed to obtain an image P (a first image), namely the image subjected to noise reduction of the complete image.
Optionally, when the terminal device performs slice processing on the complete image, a specific implementation manner may be: and if the width of the complete image is larger than a preset threshold value, carrying out slicing processing on the complete image. Based on the mode, the effectiveness and reasonability of the image slice are ensured, and the processing performance of the pyramid structure is ensured.
For example, assuming that the preset threshold is 2592 and the resolution of the complete image is 6528 × 4896, the width (6528) of the complete image is greater than the preset threshold, so that the image to be processed can be sliced into 3 images to be processed.
Therefore, based on the method described in fig. 2, the image to be processed is decomposed into a plurality of layers of sub-images with a pyramid structure, the plurality of layers of sub-images are reconstructed, and each layer of reconstructed image is subjected to noise reduction. Meanwhile, in the process of reconstructing each layer of sub-image, a method of determining whether to slice according to the image size is adopted, so that the effectiveness of image noise reduction of the pyramid structure can be ensured, excessive slicing processing can not be caused, and the reduction of the processing performance of the pyramid structure is avoided.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an image noise reduction apparatus according to an embodiment of the present invention, where the image noise reduction apparatus may be a terminal device or a device (e.g., a chip) having a function of the terminal device. Specifically, as shown in fig. 3, the image denoising apparatus 300 may include an acquisition unit 301, a determination unit 302, and a processing unit 303. Wherein:
an acquisition unit 301 configured to acquire an image to be processed;
a determining unit 302, configured to determine a pyramid layer number N corresponding to the image to be processed, where the pyramid layer number is a layer number of the image to be processed that is decomposed into a pyramid structure, and N is an integer greater than 1;
the processing unit 303 is configured to decompose the image to be processed into N layers of sub-images with a pyramid structure;
the processing unit 303 is further configured to perform reconstruction processing and denoising processing on M layers of sub-images to obtain M layers of target images, where M is N-1, the M layers of sub-images are images of the N layers of sub-images except for a highest layer image, and a target image of a lowest layer of the M layers of target images is an image of the image to be processed after denoising;
in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on the basis of the image obtained by slicing the i-th layer sub-image and the image obtained by slicing the i + 1-th layer target image, and the i-th layer sub-image is any layer of image in the M-layer sub-images.
In a possible implementation manner, the ith layer target image is obtained by performing noise reduction on a reconstructed image after reconstructing the ith layer sub-image based on the (i + 1) th layer target image and the ith layer sub-image, and the (i + 1) th layer target image is obtained by reconstructing the (i + 1) th layer sub-image and performing noise reduction on the reconstructed image.
In a possible implementation manner, the obtaining unit 301, when obtaining the image to be processed, may specifically be configured to: carrying out slicing processing on the complete image to obtain a plurality of images to be processed; a processing unit 303, further configured to: and merging the target image of the bottommost layer corresponding to each image to be processed in the plurality of images to be processed to obtain a first image, wherein the first image is the image of the complete image subjected to noise reduction.
In a possible implementation manner, the obtaining unit 301, when performing slice processing on the complete image, may specifically be configured to: and if the width of the complete image is larger than a preset threshold value, carrying out slicing processing on the complete image.
In a possible implementation manner, the processing unit 303, when decomposing the image to be processed into N layers of sub-images, may specifically be configured to: filtering the image to be processed to obtain a second image; and carrying out sampling processing on the second image for N times to obtain N layers of sub-images.
The embodiment of the present application further provides a chip, where the chip may perform relevant steps of the terminal device in the foregoing method embodiment. The chip, including a processor and a communication interface, the processor configured to cause the chip to perform the operations of: acquiring an image to be processed; determining pyramid layer number N corresponding to the image to be processed, wherein the pyramid layer number is the layer number for decomposing the image to be processed into a pyramid structure, and N is an integer greater than 1; decomposing the image to be processed into N layers of sub-images with pyramid structures; performing reconstruction processing and noise reduction processing on M layers of sub-images to obtain M layers of target images, wherein M is N-1, the M layers of sub-images are images except the highest layer image in the N layers of sub-images, and the target image at the bottommost layer in the M layers of target images is an image subjected to noise reduction on the image to be processed; in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on the basis of the image obtained by slicing the i-th layer sub-image and the image obtained by slicing the i + 1-th layer target image, and the i-th layer sub-image is any layer of image in the M-layer sub-images.
In a possible implementation manner, the ith layer target image is obtained by performing noise reduction on a reconstructed image after reconstructing the ith layer sub-image based on the (i + 1) th layer target image and the ith layer sub-image, and the (i + 1) th layer target image is obtained by reconstructing the (i + 1) th layer sub-image and performing noise reduction on the reconstructed image.
In a possible implementation manner, the chip, when acquiring the image to be processed, may be specifically configured to: carrying out slicing processing on the complete image to obtain a plurality of images to be processed; the chip is also used for: and merging the target image of the bottommost layer corresponding to each image to be processed in the plurality of images to be processed to obtain a first image, wherein the first image is the image of the complete image subjected to noise reduction.
In a possible implementation manner, the chip, when performing slice processing on a complete image, may be specifically configured to: and if the width of the complete image is larger than a preset threshold value, carrying out slicing processing on the complete image.
In a possible implementation manner, when the chip decomposes the image to be processed into N layers of sub-images, the chip may be specifically configured to: filtering the image to be processed to obtain a second image; and carrying out sampling processing on the second image for N times to obtain N layers of sub-images.
For each device or product applied to or integrated in the chip, each module included in the device or product may be implemented by hardware such as a circuit, or at least a part of the modules may be implemented by a software program running on a processor integrated in the chip, and the rest (if any) part of the modules may be implemented by hardware such as a circuit.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an image denoising device according to an embodiment of the present invention. The image noise reduction apparatus 400 may include a memory 401 and a processor 402. Optionally, a communication interface 403 is also included. The memory 401, processor 402, and communication interface 403 are connected by one or more communication buses. Wherein the communication interface 403 is controlled by the processor 402 for transceiving information.
The communication interface 403 is used to receive or transmit data.
The processor 402 may be a Central Processing Unit (CPU), and the processor 402 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor, but in the alternative, the processor 402 may be any conventional processor or the like. Wherein:
a memory 401 for storing program instructions.
A processor 402 for calling program instructions stored in the memory 401.
The processor 402 calls the program instructions stored in the memory 401 to make the image noise reduction apparatus 400 execute the method executed by the terminal device in the above method embodiment.
As shown in fig. 5, fig. 5 is a schematic structural diagram of a module device according to an embodiment of the present disclosure. The module device 500 can perform the steps related to the terminal device in the foregoing method embodiments, and the module device 500 includes: a communication module 501, a power module 502, a memory module 503, and a chip 504.
The power module 502 is used for providing power for the module device; the storage module 503 is used for storing data and instructions; the communication module 501 is used for performing internal communication of the module device, or is used for performing communication between the module device and an external device; the chip 504 is used for executing the method executed by the terminal device in the above method embodiment.
It should be noted that details that are not mentioned in the embodiments corresponding to fig. 3 to fig. 5 and specific implementation manners of each step may refer to the embodiment shown in fig. 2 and the foregoing details, and are not described again here.
Embodiments of the present application further provide a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is executed on a processor, the method flow of the foregoing method embodiments is implemented.
Embodiments of the present application further provide a computer program product, where when the computer program product runs on a processor, the method flow of the above method embodiments is implemented.
With regard to each module/unit included in each apparatus and product described in the above embodiments, it may be a software module/unit, or may also be a hardware module/unit, or may also be a part of a software module/unit and a part of a hardware module/unit. For example, each module/unit included in each apparatus or product applied to or integrated in a chip may be implemented by hardware such as a circuit, or at least a part of the modules/units may be implemented by a software program running on an integrated processor in the chip, and the rest (if any) part of the modules/units may be implemented by hardware such as a circuit; for each device and product applied to or integrated with the chip module, each module/unit included in the device and product may be implemented by hardware such as a circuit, and different modules/units may be located in the same piece (for example, a chip, a circuit module, etc.) or different components of the chip module, or at least part of the modules/units may be implemented by a software program running on a processor integrated inside the chip module, and the rest (if any) part of the modules/units may be implemented by hardware such as a circuit; for each device or product applied to or integrated in the terminal, the modules/units included in the device or product may all be implemented by hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal, or at least some of the modules/units may be implemented by software programs running on a processor integrated in the terminal, and the rest (if any) of the modules/units may be implemented by hardware such as a circuit.
It should be noted that for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some acts may occur in other orders or concurrently depending on the application. Further, those skilled in the art will recognize that the embodiments described in this specification are preferred embodiments and that acts or modules referred to are not necessarily required for this application.
The descriptions of the embodiments provided in the present application may be referred to each other, and the descriptions of the embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. For convenience and brevity of description, for example, the functions and operations performed by the devices and apparatuses provided in the embodiments of the present application may refer to the related descriptions of the method embodiments of the present application, and may also be referred to, combined with or cited among the method embodiments and the device embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method for image noise reduction, the method comprising:
acquiring an image to be processed;
determining pyramid layer number N corresponding to the image to be processed, wherein the pyramid layer number is the layer number for decomposing the image to be processed into a pyramid structure, and N is an integer greater than 1;
decomposing the image to be processed into N layers of sub-images with pyramid structures;
performing reconstruction processing and noise reduction processing on the M layers of sub-images to obtain M layers of target images, wherein M is N-1, the M layers of sub-images are images except the highest layer of images in the N layers of sub-images, and the target image at the bottommost layer in the M layers of target images is an image subjected to noise reduction on the image to be processed;
in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing on the basis of the image obtained by slicing the i-th layer sub-image and the image obtained by slicing the i + 1-th layer target image, and the i-th layer sub-image is any one layer of image in the M layers of sub-images.
2. The method according to claim 1, wherein the i-th layer target image is obtained by performing noise reduction on a reconstructed image after reconstructing the i-th layer sub-image based on the i + 1-th layer target image and the i-th layer sub-image, and the i + 1-th layer target image is obtained by reconstructing the i + 1-th layer sub-image and performing noise reduction.
3. The method of claim 1, wherein the acquiring the image to be processed comprises:
carrying out slicing processing on the complete image to obtain a plurality of images to be processed;
the method further comprises the following steps:
and merging the target image of the bottommost layer corresponding to each image to be processed in the plurality of images to be processed to obtain a first image, wherein the first image is the image of the complete image subjected to noise reduction.
4. The method of claim 3, wherein the slicing the full image comprises:
and if the width of the complete image is larger than the preset threshold value, carrying out slicing processing on the complete image.
5. The method according to any one of claims 1 to 4, wherein the decomposing the image to be processed into N layers of sub-images comprises:
filtering the image to be processed to obtain a second image;
and carrying out N times of sampling processing on the second image to obtain N layers of sub-images.
6. An image noise reduction apparatus, characterized in that the apparatus comprises:
the acquisition unit is used for acquiring an image to be processed;
a determining unit, configured to determine a pyramid layer number N corresponding to the image to be processed, where the pyramid layer number is a layer number obtained by decomposing the image to be processed into a pyramid structure, and N is an integer greater than 1;
the processing unit is used for decomposing the image to be processed into N layers of sub-images with pyramid structures;
the processing unit is further configured to perform reconstruction processing and noise reduction processing on M layers of sub-images to obtain M layers of target images, where M is N-1, the M layers of images are images of the N layers of images except for a highest layer of image, and a target image of a lowest layer of the M layers of target images is an image of the image to be processed after noise reduction;
in the process of reconstructing the i-th layer sub-image, if the width of the i + 1-th layer target image is greater than a preset threshold, the i-th layer target image is obtained by performing reconstruction processing based on the image sliced from the i-th layer sub-image and the image sliced from the i + 1-th layer target image, and the i-th layer sub-image is any layer of image in the M-layer sub-images.
7. A chip comprising a processor and a communication interface, the processor being configured to cause the chip to perform the method of any one of claims 1 to 5.
8. The utility model provides a module equipment, its characterized in that, module equipment includes communication module, power module, storage module and chip, wherein:
the power supply module is used for providing electric energy for the module equipment;
the storage module is used for storing data and instructions;
the communication module is used for carrying out internal communication of module equipment or is used for carrying out communication between the module equipment and external equipment;
the chip is used for executing the method of any one of claims 1 to 5.
9. An image noise reduction apparatus, comprising a memory for storing a computer program comprising program instructions and a processor configured to invoke the program instructions to cause the image noise reduction apparatus to perform the method of any of claims 1 to 5.
10. A computer-readable storage medium having computer-readable instructions stored thereon, which, when run on an image noise reduction apparatus, cause the image noise reduction apparatus to perform the method of any one of claims 1 to 5.
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