WO2020147698A1 - 画面优化方法、装置、终端及对应的存储介质 - Google Patents

画面优化方法、装置、终端及对应的存储介质 Download PDF

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Publication number
WO2020147698A1
WO2020147698A1 PCT/CN2020/071877 CN2020071877W WO2020147698A1 WO 2020147698 A1 WO2020147698 A1 WO 2020147698A1 CN 2020071877 W CN2020071877 W CN 2020071877W WO 2020147698 A1 WO2020147698 A1 WO 2020147698A1
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picture
target
area
alignment
target picture
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PCT/CN2020/071877
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English (en)
French (fr)
Inventor
邵志兢
陈丹
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深圳看到科技有限公司
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Priority to US17/423,116 priority Critical patent/US20220130025A1/en
Publication of WO2020147698A1 publication Critical patent/WO2020147698A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/32—Determination of transform parameters for the alignment of images, i.e. image registration using correlation-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/37—Determination of transform parameters for the alignment of images, i.e. image registration using transform domain methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20201—Motion blur correction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

Definitions

  • the present invention relates to the field of image processing technology, in particular to a method, device, terminal and corresponding storage medium for picture optimization.
  • handheld camera terminals are increasingly affected by the user's hand movement. For example, the images captured by the existing handheld camera terminals are prone to motion blur or image ghosting.
  • the embodiment of the present invention provides a picture optimization method and device that can better eliminate the motion blur or picture ghost phenomenon in the picture; in order to solve the problem of the existing picture optimization method and device being affected by the user's hand movement, the picture is easy to take. There is a technical problem with motion blur or image ghosting.
  • the embodiment of the present invention provides a picture optimization method, which includes:
  • the reference picture alignment areas of the multiple reference pictures are used to superimpose and merge the target picture alignment areas corresponding to the target picture, so as to perform a noise reduction operation on the target picture.
  • the alignment area of each target picture in the target picture is obtained based on the pixel gray scale of the target picture and the reference picture, and the corresponding reference picture in each reference picture is aligned
  • the regional steps include:
  • the pixel gray scale of the target reduction screen for the previous setting of the zoom ratio is compared with the previous one. Compare the pixel gray levels of the reference reduced screen with the zoom ratio set at the previous level to obtain the corresponding area of the target reduced screen with the zoom ratio set at the previous level and the reference reduced screen with the zoom ratio set at the previous level. Repeat step C until the first Corresponding areas of the target zoom-out screen with the first-level setting zoom ratio and the reference zoom-out screen with the first-level setting zoom ratio;
  • the area shapes of the multiple target picture alignment areas are the same, and the overlapping area of adjacent target picture alignment areas is greater than or equal to 50% of the area area of the target picture alignment area.
  • the target picture and the reference picture are a continuous shooting picture of the same area within a set time or a plurality of continuous video frames displaying the same area within a set time.
  • the step of using reference picture alignment areas of multiple reference pictures to superimpose and merge the target picture alignment areas corresponding to the target picture based on the similarity includes:
  • the reference picture alignment regions of the multiple reference pictures are used to superimpose and merge the target picture alignment regions corresponding to the target picture.
  • the step of using reference picture alignment regions of multiple reference pictures to superimpose and merge the target picture alignment regions corresponding to the target picture based on the superposition fusion weight of the reference pictures includes :
  • Discrete inverse Fourier transform is performed on the target Fourier spectrum of the target picture alignment area after the superposition and fusion to obtain the target picture alignment area after the superposition and fusion.
  • the picture optimization method further includes:
  • Partial brightness adjustment is performed on the area of the target image whose brightness value is less than the set value after the noise reduction operation.
  • the embodiment of the present invention also provides a picture optimization device, which includes:
  • a related picture acquisition module for acquiring a target picture and corresponding multiple reference pictures; wherein the target picture and the reference picture are related pictures in the same area;
  • An area division module configured to divide the target screen into multiple target screen alignment areas according to a set area size, and adjacent target screen alignment areas have overlapping areas;
  • the comparison module is used to obtain the alignment area of each target picture in the target picture, the corresponding reference picture alignment area in each reference picture and the alignment area with the corresponding reference picture based on the pixel gray scale of the target picture and the reference picture Similarity;
  • the optimization module is configured to use the reference picture alignment areas of multiple reference pictures to superimpose and merge the target picture alignment areas corresponding to the target picture based on the similarity, so as to perform a noise reduction operation on the target picture.
  • An embodiment of the present invention also provides a computer-readable storage medium, which stores processor-executable instructions, and the instructions are loaded by one or more processors to execute the above-mentioned screen optimization method.
  • An embodiment of the present invention also provides a terminal, which includes a processor and a memory, the memory stores a plurality of instructions, and the processor loads the instructions from the memory to execute the above-mentioned screen optimization method.
  • the picture optimization method and picture optimization device of the present invention use multiple reference pictures to optimize the target picture, which can better eliminate the interference information in the target picture. Effectively eliminate the motion blur phenomenon in the picture, and at the same time eliminate the picture ghost phenomenon; effectively solve the existing picture optimization methods and devices that are affected by the user's hand movement and the shooting picture is prone to motion blur or picture ghost phenomenon. technical problem.
  • FIG. 1 is a flowchart of the first embodiment of the picture optimization method of the present invention
  • step S302 is a flowchart of step S302 of the first embodiment of the picture optimization method of the present invention.
  • Fig. 6a is a schematic diagram of a target image before performing local brightness adjustment
  • FIG. 6b is a schematic diagram of the target image after local brightness adjustment is performed on the dark area
  • FIG. 7 is a schematic structural diagram of the first embodiment of the picture optimization device of the present invention.
  • FIG. 8 is a schematic structural diagram of a second embodiment of the picture optimization device of the present invention.
  • FIG. 9 is a schematic diagram of a working environment structure of an electronic device where the picture optimization apparatus of the present invention is located.
  • the picture optimization method and picture optimization device of the present invention are used in electronic equipment capable of continuous picture shooting or video shooting.
  • the electronic equipment includes but is not limited to wearable devices, head-mounted devices, medical and health platforms, personal computers, server computers, handheld or laptop devices, mobile devices (such as mobile phones, personal digital assistants (PDA), media players) Etc.), multi-processor systems, consumer electronic devices, small computers, large computers, distributed computing environments including any of the above systems or devices, etc.
  • the electronic device is preferably an electronic photographing terminal capable of taking pictures or video shooting for continuous picture or video shooting. Since the electronic device can use multiple reference pictures to optimize the target picture, it can better eliminate the target picture. This can effectively eliminate the motion blur phenomenon in the picture, and at the same time eliminate the picture ghost phenomenon.
  • FIG. 1 is a flowchart of a first embodiment of a picture optimization method of the present invention.
  • the picture optimization method of this embodiment can be implemented using the above-mentioned electronic device.
  • the picture optimization method includes:
  • Step S101 acquiring a target picture and corresponding multiple reference pictures, where the target picture and the reference picture are related pictures in the same area;
  • Step S102 Divide the target picture into multiple target picture alignment areas according to the set area size, and adjacent target picture alignment areas have overlapping areas;
  • Step S103 based on the pixel gray levels of the target picture and the reference picture, obtain each target picture alignment area in the target picture, the corresponding reference picture alignment area in each reference picture, and the similarity with the corresponding reference picture alignment area;
  • Step S104 Based on the similarity, the reference picture alignment areas of the multiple reference pictures are used to superimpose and merge the target picture alignment areas corresponding to the target picture, so as to perform a noise reduction operation on the target picture.
  • a picture optimization device (such as an electronic photographing terminal, etc.) acquires a target picture and corresponding multiple reference pictures.
  • the reference picture obtained is to optimize the target picture, so the target picture and the reference picture should be related pictures in the same area.
  • the target image and the reference image may be a continuous shooting image (continuous shooting photo) of the same area within a set time or multiple continuous video frames (videos) displaying the same area within a set time. Therefore, the reference picture and the target picture should have a lot of related content about the same area, so the target picture can be optimized using the reference picture.
  • step S102 the image optimization device performs a segmentation operation on the target image according to a preset set area size. Specifically, the target image is divided into multiple target image alignment areas, and adjacent target image alignment areas have overlapping areas.
  • the area shapes of the multiple target image alignment areas are the same, and the overlapping area of adjacent target image alignment areas is greater than or equal to 50% of the area of the target image alignment area. Since the target picture appears more than twice in all target picture alignment areas, this can better reduce the error generated during subsequent target picture alignment areas and reference picture matching.
  • step S103 the picture optimization device obtains each target picture alignment area in the target picture based on the pixel gray levels of the target picture and the reference picture, the corresponding reference picture alignment area in each reference picture and the alignment area of the corresponding reference picture. Similarity.
  • step S103 includes:
  • Step S201 using n set zoom ratios, generating n target reduced pictures according to the target picture, and generating n reference reduced pictures according to the reference pictures.
  • the target picture can be reduced by 2 times, 4 times, 8 times, etc., to obtain n target reduced pictures
  • the reference picture can be reduced by 2 times, 4 times, 8 times, etc., to obtain n reference reduced pictures.
  • Step S202 comparing the pixel gray level of the target reduced screen with the n-th level set zoom ratio with the pixel gray level of the reference reduced screen with the n-th level set zoom ratio to obtain the n-th level target reduced screen with the zoom ratio set Corresponding area of the reference zoomed-out screen with the n-th level set zoom ratio; wherein the m-th level set zoom ratio is greater than the m-1 level set zoom ratio, and m and n are both positive integers.
  • the third level is set to zoom ratio Compare the pixel gray scale of the target reduced screen with the pixel gray scale of the reference reduced screen with the third-level set zoom ratio, and obtain the target reduced screen with the third-level set zoom ratio and the third-level set zoom ratio.
  • the corresponding area of the screen you can divide the target zoomed-out screen of the third level to set the zoom ratio, and then traverse the reference zoomed-out screen of the third level to set the zoom ratio according to the divided area, so as to obtain the target of the third-level zoom ratio.
  • Each divided area of the reduced screen corresponds to the corresponding area of the reference reduced screen with the third-level setting zoom ratio. Since the comparison area of the target reduced image and the reference reduced image with a zoom ratio of 8 times is small, the comparison speed of the pixel gray scale can be better accelerated.
  • Step S203 in the corresponding area of the target reduced screen with the n-th level set zoom ratio and the reference reduced screen with the n-th level set zoom ratio, the pixel gray level of the target reduced screen with the upper level set zoom ratio is compared with the upper Compare the pixel gray levels of the reference reduced screen with the zoom ratio set at the first level, and obtain the corresponding area of the target reduced screen with the zoom ratio set at the previous level and the reference reduced screen with the zoom ratio set at the previous level. Repeat step C until it is obtained Corresponding areas of the target reduced screen for the first-level setting zoom ratio and the reference reduced screen for the first-level setting zoom ratio.
  • the pixel gray scale of the second-level setting zoom ratio is reduced to the second level
  • the pixel gray scale of the reference zoomed-out screen for setting the zoom ratio is compared.
  • the target zoomed-out screen for the second-level zoom ratio can be divided into regions, and then the reference zoom-out zoom ratio of the second-level zoom ratio can be set in turn according to the divided regions.
  • the pixel gray scale of the first-level setting zoom ratio is reduced to the first level.
  • the pixel gray scale of the reference zoomed-out screen for setting the zoom ratio is compared.
  • the target zoomed-out screen for the first-level zoom ratio can be divided into regions, and then the reference zoomed-out screens for the zoom ratio can be set in the first level according to the divided regions.
  • Step S204 in the corresponding areas of the target reduced picture with the first-level zoom ratio set and the reference reduced picture with the first-level zoom ratio set, the pixels of each target picture alignment area in the target picture and each reference picture are grayed out
  • the traversal operation can be performed on each reference picture in turn according to the target picture alignment area in the target picture, so as to obtain the reference picture alignment area corresponding to each target picture alignment area in the target picture in each reference picture.
  • step S202 to step S204 the reference picture alignment area corresponding to the target picture alignment area is generated through the multi-level target reduction picture and the multi-level reference reduction picture, which can speed up the acquisition of the corresponding reference picture alignment area and reduce the acquisition of reference picture alignment The amount of calculation of the area.
  • the comparison operation here can perform displacement compensation for small deviations of the target image caused by hand shaking during shooting.
  • the target image alignment area and the corresponding reference image alignment area After obtaining the target image alignment area and the corresponding reference image alignment area, based on the pixel gray scale of the target image alignment area and the corresponding reference image alignment area, determine the similarity of the target image alignment area and the corresponding reference image alignment area degree. The higher the degree of consistency between the pixel gray level of the target image alignment area and the pixel gray level of the corresponding reference image alignment area, the higher the similarity between the target image alignment area and the corresponding reference image alignment area.
  • step S104 the picture optimization apparatus uses the reference picture alignment areas of multiple reference pictures to superimpose and fuse the target picture alignment areas corresponding to the target picture based on the similarity between the target picture alignment area and the corresponding reference picture alignment area obtained in step S103 To perform noise reduction operations on the target image.
  • FIG. 3 is a flowchart of step S104 of the first embodiment of the picture optimization method of the present invention.
  • This step S104 includes:
  • step S301 the picture optimization apparatus generates a superposition fusion weight of the corresponding reference picture based on the similarity between the target picture alignment area and the reference picture alignment area corresponding to each reference picture.
  • the superposition fusion weight here refers to the weight relationship of fusing the reference picture alignment areas of multiple reference pictures to the corresponding target picture alignment areas.
  • the alignment area of the reference picture with low similarity has a relatively large difference from the alignment area of the target picture, and the effect of optimizing and correcting the alignment area of the target picture is also small, so the corresponding superimposition and fusion weight is smaller.
  • the difference between the alignment area of the reference picture and the alignment area of the target picture with higher similarity is smaller, and the optimization and correction effect of the alignment area of the target picture is greater, so the corresponding superposition and fusion weight is larger.
  • step S302 the picture optimization apparatus uses the reference picture alignment regions of multiple reference pictures to superimpose and merge the target picture alignment regions corresponding to the target picture based on the superposition and fusion weight of the reference pictures obtained in step S301. Please refer to FIG. 4 for details.
  • FIG. 4 is a flowchart of step S302 of the first embodiment of the picture optimization method of the present invention. This step S302 includes:
  • Step S401 The image optimization device performs discrete Fourier transform on the target image alignment area corresponding to the target image to obtain the target Fourier spectrum of the target image alignment area.
  • step S402 the picture optimization apparatus performs discrete Fourier transform on the reference picture alignment area of the reference picture to obtain a reference Fourier spectrum of the reference picture alignment area of the reference picture. In this way, the superposition and fusion of the reference picture alignment area and the target picture alignment area can be effectively performed through the Fourier spectrum.
  • Step S403 using the superposition fusion weight of the reference picture obtained in step S301 and the reference Fourier spectrum of the reference picture alignment area to perform weighted superposition on the target Fourier spectrum of the target picture alignment area to obtain the target picture alignment after superposition and fusion The target Fourier spectrum of the area.
  • Step S404 Perform inverse discrete Fourier transform on the target Fourier spectrum of the target image alignment area after the superposition and fusion obtained in step S403 to obtain the target image alignment area after the superposition and fusion.
  • the real signal of the picture generally does not change during continuous shooting or in continuous video frames, and the noise signal does randomly occur in the reference picture or the target picture. Therefore, using multiple reference pictures to superimpose and optimize the target picture can effectively realize the noise reduction operation on the target picture.
  • the picture optimization method of this embodiment uses multiple reference pictures to optimize the target picture, which can better eliminate the interference information in the target picture, thereby effectively eliminating the motion blur phenomenon in the picture and simultaneously eliminating the picture ghost phenomenon.
  • FIG. 5 is a flowchart of a second embodiment of the picture optimization method of the present invention.
  • the picture optimization method of this embodiment can be implemented using the above-mentioned electronic device, and the picture optimization method includes:
  • Step S501 acquiring a target picture and corresponding multiple reference pictures, where the target picture and the reference picture are related pictures in the same area;
  • Step S502 Divide the target picture into multiple target picture alignment areas according to the set area size, and adjacent target picture alignment areas have overlapping areas;
  • Step S503 based on the pixel gray levels of the target picture and the reference picture, obtain each target picture alignment area in the target picture, the corresponding reference picture alignment area in each reference picture, and the similarity with the corresponding reference picture alignment area;
  • Step S504 Based on the similarity, the reference picture alignment areas of the multiple reference pictures are used to superimpose and merge the target picture alignment areas corresponding to the target picture, so as to perform a noise reduction operation on the target picture;
  • Step S505 obtaining a brightness distribution map of the target image after the noise reduction operation
  • Step S506 Perform local brightness adjustment on the area where the brightness value of the target image after the noise reduction operation is less than the set value.
  • Steps S501 to S504 of this embodiment are the same or similar to the related descriptions in steps S101 to S104 of the first embodiment of the above-mentioned picture optimization method.
  • steps S101 to S101 of the first embodiment of the above-mentioned picture optimization method Relevant description in step S104.
  • step S505 after the image optimization apparatus of this embodiment performs the noise reduction operation, the overall noise of the target image has been reduced. Therefore, the local contrast of the target image can be adjusted to improve the detail presentation ability of the target image.
  • the image optimization device obtains the brightness distribution map of the target image after the noise reduction operation, so as to adjust the contrast of the target image according to the brightness.
  • step S506 the image optimization device performs local brightness adjustment on the area of the target image after the noise reduction operation whose brightness value is less than the set value. That is, the dark part brightening method is used to multiply the pixel brightness of the area whose brightness value is less than the set value in the target screen by a coefficient greater than one, and then the area is brightened, thereby improving the detail display ability of the area.
  • the overall noise of the picture is relatively small, and the noise of the target picture has a very limited influence on the display power of the brightened area.
  • FIGS. 6a and 6b where FIG. 6a is a schematic diagram of the target image before local brightness adjustment, and FIG. 6b is a schematic diagram of the target image after local brightness adjustment is performed on the dark area. It can be clearly seen from the figure that the detail display ability in Figure 6b is stronger than that in Figure 6a.
  • the picture optimization method of this embodiment performs a partial brightening operation on the target picture after noise reduction, which further improves the detail display power of the target picture and the color saturation of the target picture.
  • the present invention also provides a picture optimization device. Please refer to FIG. 7.
  • FIG. 7 is a schematic structural diagram of the first embodiment of the picture optimization device of the present invention.
  • the picture optimization device of this embodiment can be implemented using the first embodiment of the picture optimization method described above.
  • the picture optimization device 70 of this embodiment includes a related picture acquisition module 71, an area division module 72, a comparison module 73, and an optimization module 74.
  • the related picture acquisition module 71 is used to acquire the target picture and the corresponding multiple reference pictures; the target picture and the reference picture are related pictures in the same area; the area dividing module 72 is used to divide the target picture into multiple according to the set area size The target picture alignment area, adjacent target picture alignment areas have overlapping areas; the comparison module 73 is used to obtain the alignment area of each target picture in the target picture based on the pixel gray levels of the target picture and the reference picture, and correspond to each target picture in each reference picture.
  • the relevant picture acquisition module 71 acquires the target picture and the corresponding multiple reference pictures.
  • the reference picture obtained is to optimize the target picture, so the target picture and the reference picture should be related pictures in the same area.
  • the area dividing module 72 performs a dividing operation on the target picture and the reference picture according to the preset set area size. Specifically, the target image is divided into multiple target image alignment areas, and adjacent target image alignment areas have overlapping areas.
  • the area shapes of the multiple target image alignment areas are the same, and the overlapping area of adjacent target image alignment areas is greater than or equal to 50% of the area of the target image alignment area. Since the target picture appears more than twice in all target picture alignment areas, this can better reduce the error generated during subsequent target picture alignment areas and reference picture matching.
  • the comparison module 73 obtains each target picture alignment area in the target picture, the corresponding reference picture alignment area in each reference picture, and the similarity with the corresponding reference picture alignment area based on the pixel gray levels of the target picture and the reference picture.
  • the optimization module 74 uses the reference picture alignment areas of multiple reference pictures to superimpose and fuse the corresponding target picture alignment areas of the target picture based on the obtained target picture alignment area and the similarity of the corresponding reference picture alignment area, so as to perform fusion on the target picture. Noise reduction operation.
  • the specific working principle of the picture optimization apparatus of this embodiment is the same as or similar to the description in the first embodiment of the above-mentioned picture optimization method.
  • the picture optimization method of this embodiment uses multiple reference pictures to optimize the target picture, which can better eliminate the interference information in the target picture, thereby effectively eliminating the motion blur phenomenon in the picture and simultaneously eliminating the picture ghost phenomenon.
  • FIG. 8 is a schematic structural diagram of a second embodiment of the image optimization apparatus of the present invention.
  • the picture optimization apparatus of this embodiment can be implemented using the second embodiment of the above-mentioned picture optimization method.
  • the picture optimization device 80 of this embodiment includes a related picture acquisition module 81, a region division module 85, a comparison module 83, an optimization module 84, a brightness acquisition module 85, and a brightness adjustment module 86.
  • the related picture acquisition module 81 is used to acquire the target picture and the corresponding multiple reference pictures; the target picture and the reference picture are related pictures in the same area; the area division module 82 is used to divide the target picture into multiple according to the set area size The target picture alignment area, adjacent target picture alignment areas have overlapping areas; the comparison module 83 is used to obtain the alignment area of each target picture in the target picture based on the pixel gray scale of the target picture and the reference picture, corresponding to each reference picture The alignment area of the reference picture and the similarity with the alignment area of the corresponding reference picture; the optimization module 84 is used for superimposing and fusing the alignment area of the target picture corresponding to the target picture by using the reference picture alignment area of the multiple reference pictures based on the similarity
  • the target image performs a noise reduction operation; the brightness obtaining module 85 is used to obtain the brightness distribution map of the target image after the noise reduction operation; the brightness adjustment module 86 is used to perform the noise reduction operation on the area where the brightness value is less than the set value, Perform local brightness adjustment.
  • the picture optimization device 80 of this embodiment further includes: the brightness obtaining module 85 obtains the brightness distribution map of the target picture after the noise reduction operation, so as to perform the contrast of the target picture according to the brightness. Adjustment. Then, the brightness adjustment module 86 performs local brightness adjustment on the area of the target image after the noise reduction operation whose brightness value is less than the set value. That is, the dark part brightening method is used to multiply the pixel brightness of the area whose brightness value is less than the set value in the target screen by a coefficient greater than one, and then the area is brightened, thereby improving the detail display ability of the area.
  • the overall noise of the picture is relatively small, and the noise of the target picture has a very limited influence on the display power of the brightened area.
  • the image optimization device of this embodiment performs a partial brightening operation on the target image after noise reduction, which further improves the detail display power of the target image and the color saturation of the target image.
  • the picture optimization method and picture optimization device of the present invention use multiple reference pictures to optimize the target picture, which can better eliminate the interference information in the target picture, thereby effectively eliminating the motion blur phenomenon in the picture, and at the same time, eliminating the virtual picture. Shadow phenomenon; effectively solves the technical problem of the existing picture optimization methods and devices that are affected by the user's hand movement and the shooting picture is prone to motion blur or picture ghost phenomenon.
  • a component may be, but is not limited to, a process, a processor, an object, an executable application, a thread of execution, a program, and/or a computer running on a processor.
  • a component may be, but is not limited to, a process, a processor, an object, an executable application, a thread of execution, a program, and/or a computer running on a processor.
  • the application running on the controller and the controller can be components.
  • One or more components may exist in an executing process and/or thread, and the components may be located on one computer and/or distributed between two or more computers.
  • Example electronic devices 912 include, but are not limited to, wearable devices, head-mounted devices, healthcare platforms, personal computers, server computers, handheld or laptop devices, mobile devices (such as mobile phones, personal digital assistants (PDAs), media players) Etc.), multi-processor systems, consumer electronic devices, small computers, large computers, distributed computing environments including any of the above systems or devices, etc.
  • Computer readable instructions can be distributed via computer readable media (discussed below).
  • Computer readable instructions can be implemented as program modules, such as functions, objects, application programming interfaces (APIs), data structures, etc. that perform specific tasks or implement specific abstract data types.
  • program modules such as functions, objects, application programming interfaces (APIs), data structures, etc. that perform specific tasks or implement specific abstract data types.
  • APIs application programming interfaces
  • data structures such as lists, etc. that perform specific tasks or implement specific abstract data types.
  • the functions of the computer-readable instructions can be randomly combined or distributed in various environments.
  • FIG. 9 illustrates an example of an electronic device 912 including one or more embodiments of the picture optimization apparatus of the present invention.
  • the electronic device 912 includes at least one processing unit 916 and memory 918.
  • the memory 918 may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. This configuration is illustrated by the dashed line 914 in FIG. 9.
  • the electronic device 912 may include additional features and/or functions.
  • the device 912 may also include additional storage devices (for example, removable and/or non-removable), including but not limited to magnetic storage devices, optical storage devices, and so on.
  • additional storage devices for example, removable and/or non-removable
  • Such an additional storage device is illustrated by the storage device 920 in FIG. 9.
  • computer-readable instructions for implementing one or more embodiments provided herein may be in the storage device 920.
  • the storage device 920 may also store other computer-readable instructions for implementing an operating system, application programs, and the like.
  • the computer-readable instructions may be loaded into the memory 918 and executed by the processing unit 916, for example.
  • Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions or other data.
  • the memory 918 and the storage device 920 are examples of computer storage media.
  • Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other storage technologies, CD-ROM, digital versatile disk (DVD) or other optical storage devices, cassette tapes, magnetic tapes, disk storage devices or other magnetic storage devices, Or any other medium that can be used to store desired information and can be accessed by the electronic device 912. Any such computer storage medium may be part of the electronic device 912.
  • the electronic device 912 may also include a communication connection 926 that allows the electronic device 912 to communicate with other devices.
  • the communication connection 926 may include, but is not limited to, a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interface for connecting the electronic device 912 to other electronic devices.
  • the communication connection 926 may include a wired connection or a wireless connection.
  • the communication connection 926 can transmit and/or receive communication media.
  • Computer-readable medium may include communication media.
  • Communication media typically contain computer-readable instructions or other data in a “modulated data signal” such as a carrier wave or other transmission mechanism, and include any information delivery media.
  • modulated data signal may include a signal in which one or more of the signal characteristics are set or changed in a manner that encodes information into the signal.
  • the electronic device 912 may include an input device 924, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device, and/or any other input device.
  • the device 912 may also include an output device 922, such as one or more displays, speakers, printers, and/or any other output devices.
  • the input device 924 and the output device 922 may be connected to the electronic device 912 via a wired connection, a wireless connection, or any combination thereof. In one embodiment, an input device or output device from another electronic device may be used as the input device 924 or output device 922 of the electronic device 912.
  • the components of the electronic device 912 may be connected through various interconnections (such as buses). Such interconnections may include Peripheral Component Interconnect (PCI) (such as PCI Express), Universal Serial Bus (USB), FireWire (IEEE 1394), optical bus structure, and so on.
  • PCI Peripheral Component Interconnect
  • USB Universal Serial Bus
  • FireWire IEEE 1394
  • optical bus structure and so on.
  • the components of the electronic device 912 may be interconnected through a network.
  • the memory 918 may be composed of multiple physical memory units located in different physical locations and interconnected by a network.
  • storage devices used to store computer-readable instructions can be distributed across a network.
  • the electronic device 930 accessible via the network 928 may store computer-readable instructions for implementing one or more embodiments provided by the present invention.
  • the electronic device 912 can access the electronic device 930 and download part or all of the computer-readable instructions for execution.
  • the electronic device 912 may download multiple computer-readable instructions as needed, or some instructions may be executed at the electronic device 912 and some instructions may be executed at the electronic device 930.
  • the one or more operations may constitute one or more computer-readable instructions stored on a computer-readable medium, which, when executed by an electronic device, will cause the computing device to perform the operations.
  • the order describing some or all operations should not be interpreted as implying that these operations must be order-dependent. Those skilled in the art will understand alternative sequencing that has the benefit of this description. Moreover, it should be understood that not all operations are necessarily present in every embodiment provided herein.
  • the functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
  • the storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk.

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Abstract

一种画面优化方法，其包括：获取目标画面以及对应的多张参考画面(S101)；按设定区域尺寸，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域(S102)；基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度(S103)；基于相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作(S104)。

Description

画面优化方法、装置、终端及对应的存储介质 技术领域
本发明涉及图像处理技术领域，特别是涉及一种画面优化方法、装置、终端及对应的存储介质。
 背景技术
随着科技的发展，人们对手持拍摄终端的拍摄画面要求越来越高，如用户希望拍摄照片的清晰度越来越高，以及希望拍摄终端的拍摄要求越来越低。
但是由于手持拍摄终端的小型化以及便携化设计，人们经常会在运动时使用拍摄终端进行画面拍摄操作，虽然这样使得手持拍摄终端的使用便利度增加且手持拍摄终端的使用场景也越来越多，但是同时手持拍摄终端受用户手部运动的影响也越来越大，如现有的手持拍摄终端拍摄的画面容易出现运动模糊或画面虚影的现象。
故，有必要提供一种画面优化方法及装置，以解决现有技术所存在的问题。
 技术问题
本发明实施例提供一种可较好的消除画面中的运动模糊或画面虚影现象的画面优化方法及装置；以解决现有的画面优化方法及装置中受用户手部运动影响导致拍摄画面容易出现运动模糊或画面虚影现象的技术问题。
 技术解决方案
本发明实施例提供一种画面优化方法，其包括：
获取目标画面以及对应的多张参考画面；其中所述目标画面和所述参考画面为同一区域的相关画面；
按设定区域尺寸，将所述目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；以及
基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合，以对所述目标画面进行降噪操作。
在本发明所述的画面优化方法中，所述基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域的步骤包括：
A、使用n个设定缩放比例，根据所述目标画面生成n个目标缩小画面，并根据所述参考画面生成n个参考缩小画面；
B、将第n级设定缩放比例的目标缩小画面的像素灰阶与第n级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域；其中第m级设定缩放比例大于m-1级设定缩放比例，m、n均为正整数；
C、在第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域中，将上一级设定缩放比例的目标缩小画面的像素灰阶与上一级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取上一级设定缩放比例的目标缩小画面与上一级设定缩放比例的参考缩小画面的对应区域，重复步骤C直至获取第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域；
D、在第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域中，将目标画面中每个目标画面对齐区域的像素灰阶与每个参考画面的像素灰阶进行比较，获取目标画面中每个目标画面对齐区域在每个参考画面中对应的参考画面对齐区域。
在本发明所述的画面优化方法中，多个目标画面对齐区域的区域形状相同，相邻的目标画面对齐区域的重叠区域大于等于所述目标画面对齐区域的区域面积的50%。
在本发明所述的画面优化方法中，所述目标画面和所述参考画面为设定时间内对同一区域的连拍画面或设定时间内的显示同一区域的多个连续的视频画面帧。
在本发明所述的画面优化方法中，所述基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合的步骤包括：
基于所述目标画面对齐区域与每个参考画面对应的参考画面对齐区域的相似度，生成对应的参考画面的叠加融合权重；
基于所述参考画面的叠加融合权重，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合。
在本发明所述的画面优化方法中，所述基于所述参考画面的叠加融合权重，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合的步骤包括：
对所述目标画面相应的目标画面对齐区域进行离散傅里叶变换，获取所述目标画面对齐区域的目标傅里叶频谱；
对所述参考画面的参考画面对齐区域进行离散傅里叶变换，获取所述参考画面的参考画面对齐区域的参考傅里叶频谱；
使用所述参考画面的叠加融合权重以及参考画面对齐区域的参考傅里叶频谱，对所述目标画面对齐区域的目标傅里叶频谱进行加权叠加，以得到叠加融合后的目标画面对齐区域的目标傅里叶频谱；
对所述叠加融合后的目标画面对齐区域的目标傅里叶频谱进行离散傅里叶逆变换，得到叠加融合后的目标画面对齐区域。
在本发明所述的画面优化方法中，所述画面优化方法还包括：
获取降噪操作后的目标画面的亮度分布图；
对所述降噪操作后的目标画面中亮度值小于设定值的区域，进行局部亮度调节。
本发明实施例还提供一种画面优化装置，其包括：
相关画面获取模块，用于获取目标画面以及对应的多张参考画面；其中所述目标画面和所述参考画面为同一区域的相关画面；
区域划分模块，用于按设定区域尺寸，将所述目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
对比模块，用于基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；以及
优化模块，用于基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合，以对所述目标画面进行降噪操作。
本发明实施例还提供一种计算机可读存储介质，其内存储有处理器可执行指令，所述指令由一个或一个以上处理器加载，以执行上述的画面优化方法。
本发明实施例还提供一种终端，其包括处理器和存储器，所述存储器存储有多条指令，所述处理器从所述存储器中加载指令，以执行上述的画面优化方法。
 有益效果
相较于现有技术的画面优化方法及画面优化装置，本发明的画面优化方法及画面优化装置使用多张参考画面对目标画面进行优化，可以较好的消除目标画面中的干扰信息，从而可有效的消除画面中的运动模糊现象，并且同时消除画面虚影现象；有效的解决了现有的画面优化方法及装置中受用户手部运动影响导致拍摄画面容易出现运动模糊或画面虚影现象的技术问题。
 附图说明
图1为本发明的画面优化方法的第一实施例的流程图；
图2为本发明的画面优化方法的第一实施例的步骤S103的流程图；
图3为本发明的画面优化方法的第一实施例的步骤S104的流程图；
图4为本发明的画面优化方法的第一实施例的步骤S302的流程图；
图5为本发明的画面优化方法的第二实施例的流程图；
图6a为进行局部亮度调节前的目标画面的示意图；
图6b为对暗部区域进行局部亮度调节后的目标画面的示意图；
图7为本发明的画面优化装置的第一实施例的结构示意图；
图8为本发明的画面优化装置的第二实施例的结构示意图；
图9为本发明的画面优化装置所在的电子设备的工作环境结构示意图。
 本发明的最佳实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的画面优化方法及画面优化装置用于可进行画面连拍或视频拍摄的电子设备中。该电子设备包括但不限于可穿戴设备、头戴设备、医疗健康平台、个人计算机、服务器计算机、手持式或膝上型设备、移动设备(比如移动电话、个人数字助理(PDA)、媒体播放器等等）、多处理器系统、消费型电子设备、小型计算机、大型计算机、包括上述任意系统或设备的分布式计算环境，等等。该电子设备优选为可进行拍照或视频拍摄的电子拍摄终端，以便进行画面连拍或视频拍摄，由于该电子设备可使用多张参考画面对目标画面进行优化，因此可较好的消除目标画面中的干扰信息，从而有效的消除画面中的运动模糊现象，同时消除画面虚影现象。
请参照图1，图1为本发明的画面优化方法的第一实施例的流程图，本实施例的画面优化方法可使用上述的电子设备进行实施，该画面优化方法包括：
步骤S101，获取目标画面以及对应的多张参考画面，其中目标画面和参考画面为同一区域的相关画面；
步骤S102，按设定区域尺寸，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
步骤S103，基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；
步骤S104，基于相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作。
下面详细说明本实施例的画面优化方法的各步骤的具体流程。
在步骤S101中，画面优化装置（如电子拍摄终端等）获取目标画面以及对应的多张参考画面。其中获取的参考画面是为了对目标画面进行优化，因此目标画面和参考画面应为同一区域的相关画面。
具体的，目标画面和参考画面可为设定时间内对同一区域的连拍画面（连拍照片）或设定时间内的显示同一区域的多个连续的视频画面帧（视频）。因此参考画面和目标画面应具有大量关于同一区域的相关内容，因此可使用参考画面对目标画面进行优化。
在步骤S102中，画面优化装置按照预设的设定区域尺寸，对目标画面进行分割操作。具体的，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域。
其中多个目标画面对齐区域的区域形状相同，相邻的目标画面对齐区域的重叠区域大于等于目标画面对齐区域的区域面积的50%。由于目标画面在所有的目标画面对齐区域中均出现两次以上，这样可以较好的降低后续目标画面对齐区域以及参考画面匹配时所产生的误差。
在步骤S103中，画面优化装置基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度。
具体的，获取目标画面对齐区域与对应的参考画面对齐区域的流程请参照图2，图2为本发明的画面优化方法的第一实施例的步骤S103的流程图。该步骤S103包括：
步骤S201，使用n个设定缩放比例，根据目标画面生成n个目标缩小画面，并根据参考画面生成n个参考缩小画面。如这里可将目标画面缩小2倍、4倍、8倍等，获取n个目标缩小画面；将参考画面缩小2倍、4倍、8倍等，获取n个参考缩小画面。
步骤S202，将第n级设定缩放比例的目标缩小画面的像素灰阶与第n级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域；其中第m级设定缩放比例大于m-1级设定缩放比例，m、n均为正整数。
这里可以设置第三级设定缩放比例为8倍，第二级设定缩放比例为4倍，第一级设定缩放比例为2倍，这在本步骤中，将第三级设定缩放比例的目标缩小画面的像素灰阶与第三级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取第三级设定缩放比例的目标缩小画面与第三级设定缩放比例的参考缩小画面的对应区域。这里可对第三级设定缩放比例的目标缩小画面进行区域划分，然后按划分区域依次在第三级设定缩放比例的参考缩小画面进行遍历操作，从而获取第三级设定缩放比例的目标缩小画面的每个划分区域与第三级设定缩放比例的参考缩小画面的对应区域。由于设定缩放比例为8倍的目标缩小画面以及参考缩小画面的比较区域较小，因此可较好的加快像素灰阶的比较速度。
步骤S203，在第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域中，将上一级设定缩放比例的目标缩小画面的像素灰阶与上一级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取上一级设定缩放比例的目标缩小画面与上一级设定缩放比例的参考缩小画面的对应区域，重复步骤C直至获取第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域。
如在第三级设定缩放比例的目标缩小画面与第三级设定缩放比例的参考缩小画面的对应区域中，将第二级设定缩放比例的目标缩小画面的像素灰阶与第二级设定缩放比例的参考缩小画面的像素灰阶进行比较，这里可对第二级设定缩放比例的目标缩小画面进行区域划分，然后按划分区域依次在第二级设定缩放比例的参考缩小画面进行遍历操作，从而获取第二级设定缩放比例的目标缩小画面的每个划分区域与第二级设定缩放比例的参考缩小画面的对应区域，进而获取第二级设定缩放比例的目标缩小画面与第二级设定缩放比例的参考缩小画面的对应区域。
随后在第二级设定缩放比例的目标缩小画面与第二级设定缩放比例的参考缩小画面的对应区域中，将第一级设定缩放比例的目标缩小画面的像素灰阶与第一级设定缩放比例的参考缩小画面的像素灰阶进行比较，这里可对第一级设定缩放比例的目标缩小画面进行区域划分，然后按划分区域依次在第一级设定缩放比例的参考缩小画面进行遍历操作，从而获取第一级设定缩放比例的目标缩小画面的每个划分区域与第一级设定缩放比例的参考缩小画面的对应区域，进而获取第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域。
步骤S204，在第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域中，将目标画面中每个目标画面对齐区域与每个参考画面的像素灰阶进行比较，这里可按目标画面中的目标画面对齐区域依次在每个参考画面上进行遍历操作，从而获取目标画面中每个目标画面对齐区域在每个参考画面中对应的参考画面对齐区域。
在步骤S202至步骤S204中，通过多级目标缩小画面以及多级参考缩小画面来生成目标画面对齐区域对应的参考画面对齐区域，可以加快对应的参考画面对齐区域的获取速度以及减少获取参考画面对齐区域的计算量。这里的比较操作可以对拍摄时手抖产生的目标画面的小偏差进行位移补偿操作。
当获取目标画面对齐区域以及对应的参考画面对齐区域后，基于目标画面对齐区域的像素灰阶以及对应的参考画面对齐区域的像素灰阶，确定目标画面对齐区域以及对应的参考画面对齐区域的相似度。目标画面对齐区域的像素灰阶与对应的参考画面对齐区域对应位置的像素灰阶一致度越高，则目标画面对齐区域以及对应的参考画面对齐区域的相似度也越高。
在步骤S104中，画面优化装置基于步骤S103获取的目标画面对齐区域以及对应的参考画面对齐区域的相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作。
具体请参照图3，图3为本发明的画面优化方法的第一实施例的步骤S104的流程图。该步骤S104包括：
步骤S301，画面优化装置基于目标画面对齐区域与每个参考画面对应的参考画面对齐区域的相似度，生成对应的参考画面的叠加融合权重。
这里的叠加融合权重是指将多个参考画面的参考画面对齐区域融合到对应的目标画面对齐区域的权重关系。相似度较低的参考画面对齐区域与目标画面对齐区域差异比较大，对目标画面对齐区域的优化修正作用也较小，因此对应的叠加融合权重较小。相似度较高的参考画面对齐区域与目标画面对齐区域的差异较小，对目标画面对齐区域的优化修正作用较大，因此对应的叠加融合权重较大。
步骤S302，画面优化装置基于步骤S301获取的参考画面的叠加融合权重，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合。具体请参照图4，图4为本发明的画面优化方法的第一实施例的步骤S302的流程图。该步骤S302包括：
步骤S401，画面优化装置对目标画面相应的目标画面对齐区域进行离散傅里叶变换，以获取目标画面对齐区域的目标傅里叶频谱。
步骤S402，画面优化装置对参考画面的参考画面对齐区域进行离散傅里叶变换，以获取参考画面的参考画面对齐区域的参考傅里叶频谱。这样可使用可通过傅里叶频谱有效的进行参考画面对齐区域与目标画面对齐区域的叠加融合。
步骤S403，使用步骤S301获取的参考画面的叠加融合权重以及参考画面对齐区域的参考傅里叶频谱，对目标画面对齐区域的目标傅里叶频谱进行加权叠加，以得到叠加融合后的目标画面对齐区域的目标傅里叶频谱。
步骤S404，对步骤S403获取的叠加融合后的目标画面对齐区域的目标傅里叶频谱进行离散傅里叶逆变换，以得到叠加融合后的目标画面对齐区域。
这里由于使用多个参考画面对目标画面进行叠加融合，画面的真实信号在进行连拍时或在连续的视频帧中一般不会发生变化，而噪声信号确实随机发生在参考画面或目标画面中的，因此使用多张参考画面对目标画面进行叠加融合优化，可有效的实现对目标画面的降噪操作。
这样即完成了本实施例的画面优化方法的目标画面的降噪优化过程。
本实施例的画面优化方法使用多张参考画面对目标画面进行优化，可以较好的消除目标画面中的干扰信息，从而可有效的消除画面中的运动模糊现象，并且同时消除画面虚影现象。
请参照图5，图5为本发明的画面优化方法的第二实施例的流程图。本实施例的画面优化方法可使用上述的电子设备进行实施，该画面优化方法包括：
步骤S501，获取目标画面以及对应的多张参考画面，其中目标画面和参考画面为同一区域的相关画面；
步骤S502，按设定区域尺寸，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
步骤S503，基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；
步骤S504，基于相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作；
步骤S505，获取降噪操作后的目标画面的亮度分布图；
步骤S506，对降噪操作后的目标画面中的亮度值小于设定值的区域，进行局部亮度调节。
下面详细说明本实施例的画面优化方法的各步骤的具体流程。
本实施例的步骤S501至步骤S504与上述的画面优化方法的第一实施例的步骤S101至步骤S104中的相关描述相同或相似，具体请参见上述画面优化方法的第一实施例的步骤S101至步骤S104中的相关描述。
在步骤S505中，本实施例的画面优化装置在进行降噪操作后，目标画面的整体噪声已经降低，因此可对目标画面进行局部对比度调整，以提高目标画面的细节展现能力。
在本步骤中，画面优化装置获取降噪操作后的目标画面的亮度分布图，以便根据亮度对目标画面的对比度进行调整。
在步骤S506中，画面优化装置对降噪操作后的目标画面中的亮度值小于设定值的区域，进行局部亮度调节。即使用暗部提亮的方法，将目标画面中亮度值小于设定值的区域的像素亮度乘以一个大于一的系数，对该区域进行提亮操作，从而提高该区域的细节展现能力，由于目标画面的整体噪声较小，目标画面的噪声对提亮区域的画面展现力的影响是十分有限的。
具体可参见图6a和图6b，其中图6a为进行局部亮度调节前的目标画面的示意图，图6b为对暗部区域进行局部亮度调节后的目标画面的示意图。从图中可以明显看出图6b中的细节展现能力强于图6a。
这样即完成了本实施例的画面优化方法的目标画面的画面优化过程。
在第一实施例的基础上，本实施例的画面优化方法对降噪后的目标画面进行局部提亮操作，进一步提高了目标画面的细节展现力以及目标画面的色彩饱和度。
本发明还提供一种画面优化装置，请参照图7，图7为本发明的画面优化装置的第一实施例的结构示意图。本实施例的画面优化装置可使用上述的画面优化方法的第一实施例进行实施。本实施例的画面优化装置70包括相关画面获取模块71、区域划分模块72、对比模块73以及优化模块74。
相关画面获取模块71用于获取目标画面以及对应的多张参考画面；其中目标画面和参考画面为同一区域的相关画面；区域划分模块72用于按设定区域尺寸，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；对比模块73用于基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；优化模块74用于基于相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作。
本实施例的画面优化装置70使用时，首先相关画面获取模块71获取目标画面以及对应的多张参考画面。其中获取的参考画面是为了对目标画面进行优化，因此目标画面和参考画面应为同一区域的相关画面。
随后区域划分模块72按照预设的设定区域尺寸，对目标画面以及参考画面进行分割操作。具体的，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域。
其中多个目标画面对齐区域的区域形状相同，相邻的目标画面对齐区域的重叠区域大于等于目标画面对齐区域的区域面积的50%。由于目标画面在所有的目标画面对齐区域中均出现两次以上，这样可以较好的降低后续目标画面对齐区域以及参考画面匹配时所产生的误差。
然后对比模块73基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度。
最后优化模块74基于获取的目标画面对齐区域以及对应的参考画面对齐区域的相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作。
这样即完成了本实施例的画面优化装置70的目标画面的降噪优化过程。
本实施例的画面优化装置的具体工作原理与上述的画面优化方法的第一实施例中的描述相同或相似，具体请参见上述画面优化方法的第一实施例中的相关描述。
本实施例的画面优化方法使用多张参考画面对目标画面进行优化，可以较好的消除目标画面中的干扰信息，从而可有效的消除画面中的运动模糊现象，并且同时消除画面虚影现象。
 
请参照图8，图8为本发明的画面优化装置的第二实施例的结构示意图。本实施例的画面优化装置可使用上述的画面优化方法的第二实施例进行实施。本实施例的画面优化装置80包括相关画面获取模块81、区域划分模块85、对比模块83、优化模块84、亮度获取模块85以及亮度调节模块86。
相关画面获取模块81用于获取目标画面以及对应的多张参考画面；其中目标画面和参考画面为同一区域的相关画面；区域划分模块82用于按设定区域尺寸，将目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；对比模块83用于基于目标画面以及参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；优化模块84用于基于相似度，使用多个参考画面的参考画面对齐区域对目标画面相应的目标画面对齐区域进行叠加融合，以对目标画面进行降噪操作；亮度获取模块85用于获取降噪操作后的目标画面的亮度分布图；亮度调节模块86用于对降噪操作后的目标画面中亮度值小于设定值的区域，进行局部亮度调节。
在画面优化装置的第一实施例的基础上，本实施例的画面优化装置80还包括：亮度获取模块85获取降噪操作后的目标画面的亮度分布图，以便根据亮度对目标画面的对比度进行调整。随后亮度调节模块86对降噪操作后的目标画面中的亮度值小于设定值的区域，进行局部亮度调节。即使用暗部提亮的方法，将目标画面中亮度值小于设定值的区域的像素亮度乘以一个大于一的系数，对该区域进行提亮操作，从而提高该区域的细节展现能力，由于目标画面的整体噪声较小，目标画面的噪声对提亮区域的画面展现力的影响是十分有限的。
这样即完成了本实施例的画面优化装置80的目标画面的画面优化过程。
在第一实施例的基础上，本实施例的画面优化装置对降噪后的目标画面进行局部提亮操作，进一步提高了目标画面的细节展现力以及目标画面的色彩饱和度。
本发明的画面优化方法及画面优化装置使用多张参考画面对目标画面进行优化，可以较好的消除目标画面中的干扰信息，从而可有效的消除画面中的运动模糊现象，并且同时消除画面虚影现象；有效的解决了现有的画面优化方法及装置中受用户手部运动影响导致拍摄画面容易出现运动模糊或画面虚影现象的技术问题。
如本申请所使用的术语“组件”、“模块”、“系统”、“接口”、“进程”等等一般地旨在指计算机相关实体：硬件、硬件和软件的组合、软件或执行中的软件。例如，组件可以是但不限于是运行在处理器上的进程、处理器、对象、可执行应用、执行的线程、程序和／或计算机。通过图示，运行在控制器上的应用和该控制器二者都可以是组件。一个或多个组件可以有在于执行的进程和／或线程内，并且组件可以位于一个计算机上和／或分布在两个或更多计算机之间。
图9和随后的讨论提供了对实现本发明所述的画面优化装置所在的电子设备的工作环境的简短、概括的描述。图9的工作环境仅仅是适当的工作环境的一个实例并且不旨在建议关于工作环境的用途或功能的范围的任何限制。实例电子设备912包括但不限于可穿戴设备、头戴设备、医疗健康平台、个人计算机、服务器计算机、手持式或膝上型设备、移动设备(比如移动电话、个人数字助理(PDA)、媒体播放器等等）、多处理器系统、消费型电子设备、小型计算机、大型计算机、包括上述任意系统或设备的分布式计算环境，等等。
尽管没有要求，但是在“计算机可读指令”被一个或多个电子设备执行的通用背景下描述实施例。计算机可读指令可以经由计算机可读介质来分布（下文讨论）。计算机可读指令可以实现为程序模块，比如执行特定任务或实现特定抽象数据类型的功能、对象、应用编程接口(API)、数据结构等等。典型地，该计算机可读指令的功能可以在各种环境中随意组合或分布。
图9图示了包括本发明的画面优化装置中的一个或多个实施例的电子设备912的实例。在一种配置中，电子设备912包括至少一个处理单元916和存储器918。根据电子设备的确切配置和类型，存储器918可以是易失性的(比如RAM)、非易失性的（比如ROM、闪存等）或二者的某种组合。该配置在图9中由虚线914图示。
在其他实施例中，电子设备912可以包括附加特征和／或功能。例如，设备912还可以包括附加的存储装置（例如可移除和／或不可移除的），其包括但不限于磁存储装置、光存储装置等等。这种附加存储装置在图9中由存储装置920图示。在一个实施例中，用于实现本文所提供的一个或多个实施例的计算机可读指令可以在存储装置920中。存储装置920还可以存储用于实现操作系统、应用程序等的其他计算机可读指令。计算机可读指令可以载入存储器918中由例如处理单元916执行。
本文所使用的术语“计算机可读介质”包括计算机存储介质。计算机存储介质包括以用于存储诸如计算机可读指令或其他数据之类的信息的任何方法或技术实现的易失性和非易失性、可移除和不可移除介质。存储器918和存储装置920是计算机存储介质的实例。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字通用盘(DVD)或其他光存储装置、盒式磁带、磁带、磁盘存储装置或其他磁存储设备、或可以用于存储期望信息并可以被电子设备912访问的任何其他介质。任意这样的计算机存储介质可以是电子设备912的一部分。
电子设备912还可以包括允许电子设备912与其他设备通信的通信连接926。通信连接926可以包括但不限于调制解调器、网络接口卡(NIC)、集成网络接口、射频发射器／接收器、红外端口、USB连接或用于将电子设备912连接到其他电子设备的其他接口。通信连接926可以包括有线连接或无线连接。通信连接926可以发射和／或接收通信媒体。
术语“计算机可读介质”可以包括通信介质。通信介质典型地包含计算机可读指令或诸如载波或其他传输机构之类的“己调制数据信号”中的其他数据，并且包括任何信息递送介质。术语“己调制数据信号”可以包括这样的信号：该信号特性中的一个或多个按照将信息编码到信号中的方式来设置或改变。
电子设备912可以包括输入设备924，比如键盘、鼠标、笔、语音输入设备、触摸输入设备、红外相机、视频输入设备和／或任何其他输入设备。设备912中也可以包括输出设备922，比如一个或多个显示器、扬声器、打印机和／或任意其他输出设备。输入设备924和输出设备922可以经由有线连接、无线连接或其任意组合连接到电子设备912。在一个实施例中，来自另一个电子设备的输入设备或输出设备可以被用作电子设备912的输入设备924或输出设备922。
电子设备912的组件可以通过各种互连（比如总线）连接。这样的互连可以包括外围组件互连(PCI)(比如快速PCI)、通用串行总线(USB)、火线(IEEE 1394)、光学总线结构等等。在另一个实施例中，电子设备912的组件可以通过网络互连。例如，存储器918可以由位于不同物理位置中的、通过网络互连的多个物理存储器单元构成。
本领域技术人员将认识到，用于存储计算机可读指令的存储设备可以跨越网络分布。例如，可经由网络928访问的电子设备930可以存储用于实现本发明所提供的一个或多个实施例的计算机可读指令。电子设备912可以访问电子设备930并且下载计算机可读指令的一部分或所有以供执行。可替代地，电子设备912可以按需要下载多条计算机可读指令，或者一些指令可以在电子设备912处执行并且一些指令可以在电子设备930处执行。
本文提供了实施例的各种操作。在一个实施例中，所述的一个或多个操作可以构成一个或多个计算机可读介质上存储的计算机可读指令，其在被电子设备执行时将使得计算设备执行所述操作。描述一些或所有操作的顺序不应当被解释为暗示这些操作必需是顺序相关的。本领域技术人员将理解具有本说明书的益处的可替代的排序。而且，应当理解，不是所有操作必需在本文所提供的每个实施例中存在。
而且，尽管已经相对于一个或多个实现方式示出并描述了本公开，但是本领域技术人员基于对本说明书和附图的阅读和理解将会想到等价变型和修改。本公开包括所有这样的修改和变型，并且仅由所附权利要求的范围限制。特别地关于由上述组件（例如元件、资源等）执行的各种功能，用于描述这样的组件的术语旨在对应于执行所述组件的指定功能（例如其在功能上是等价的）的任意组件（除非另外指示），即使在结构上与执行本文所示的本公开的示范性实现方式中的功能的公开结构不等同。此外，尽管本公开的特定特征已经相对于若干实现方式中的仅一个被公开，但是这种特征可以与如可以对给定或特定应用而言是期望和有利的其他实现方式的一个或多个其他特征组合。而且，就术语“包括”、“具有”、“含有”或其变形被用在具体实施方式或权利要求中而言，这样的术语旨在以与术语“包含”相似的方式包括。
本发明实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。上述提到的存储介质可以是只读存储器，磁盘或光盘等。上述的各装置或系统，可以执行相应方法实施例中的方法。
综上所述，虽然本发明已以实施例揭露如上，实施例前的序号仅为描述方便而使用，对本发明各实施例的顺序不造成限制。并且，上述实施例并非用以限制本发明，本领域的普通技术人员，在不脱离本发明的精神和范围内，均可作各种更动与润饰，因此本发明的保护范围以权利要求界定的范围为准。

Claims (15)

  1. 一种画面优化方法，其包括：
    获取目标画面以及对应的多张参考画面；其中所述目标画面和所述参考画面为同一区域的相关画面；
    按设定区域尺寸，将所述目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
    基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；以及
    基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合，以对所述目标画面进行降噪操作。
  2. 根据权利要求1所述的画面优化方法，其中所述基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域的步骤包括：
    A、使用n个设定缩放比例，根据所述目标画面生成n个目标缩小画面，并根据所述参考画面生成n个参考缩小画面；
    B、将第n级设定缩放比例的目标缩小画面的像素灰阶与第n级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域；其中第m级设定缩放比例大于m-1级设定缩放比例，m、n均为正整数；
    C、在第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域中，将上一级设定缩放比例的目标缩小画面的像素灰阶与上一级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取上一级设定缩放比例的目标缩小画面与上一级设定缩放比例的参考缩小画面的对应区域，重复步骤C直至获取第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域；
    D、在第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域中，将目标画面中每个目标画面对齐区域的像素灰阶与每个参考画面的像素灰阶进行比较，获取目标画面中每个目标画面对齐区域在每个参考画面中对应的参考画面对齐区域。
  3. 根据权利要求1所述的画面优化方法，其中多个目标画面对齐区域的区域形状相同，相邻的目标画面对齐区域的重叠区域大于等于所述目标画面对齐区域的区域面积的50%。
  4. 根据权利要求1所述的画面优化方法，其中所述目标画面和所述参考画面为设定时间内对同一区域的连拍画面或设定时间内的显示同一区域的多个连续的视频画面帧。
  5. 根据权利要求1所述的画面优化方法，其中所述基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合的步骤包括：
    基于所述目标画面对齐区域与每个参考画面对应的参考画面对齐区域的相似度，生成对应的参考画面的叠加融合权重；
    基于所述参考画面的叠加融合权重，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合。
  6. 根据权利要求5所述的画面优化方法，其中所述基于所述参考画面的叠加融合权重，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合的步骤包括：
    对所述目标画面相应的目标画面对齐区域进行离散傅里叶变换，获取所述目标画面对齐区域的目标傅里叶频谱；
    对所述参考画面的参考画面对齐区域进行离散傅里叶变换，获取所述参考画面的参考画面对齐区域的参考傅里叶频谱；
    使用所述参考画面的叠加融合权重以及参考画面对齐区域的参考傅里叶频谱，对所述目标画面对齐区域的目标傅里叶频谱进行加权叠加，以得到叠加融合后的目标画面对齐区域的目标傅里叶频谱；
    对所述叠加融合后的目标画面对齐区域的目标傅里叶频谱进行离散傅里叶逆变换，得到叠加融合后的目标画面对齐区域。
  7. 根据权利要求1所述的画面优化方法，其中所述画面优化方法还包括：
    获取降噪操作后的目标画面的亮度分布图；
    对所述降噪操作后的目标画面中亮度值小于设定值的区域，进行局部亮度调节。
  8. 一种画面优化装置，其包括：
    相关画面获取模块，用于获取目标画面以及对应的多张参考画面；其中所述目标画面和所述参考画面为同一区域的相关画面；
    区域划分模块，用于按设定区域尺寸，将所述目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
    对比模块，用于基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；以及
    优化模块，用于基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合，以对所述目标画面进行降噪操作。
  9. 根据权利要求8所述的画面优化装置，其中所述对比模块用于使用n个设定缩放比例，根据所述目标画面生成n个目标缩小画面，并根据所述参考画面生成n个参考缩小画面；将第n级设定缩放比例的目标缩小画面的像素灰阶与第n级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域；其中第m级设定缩放比例大于m-1级设定缩放比例，m、n均为正整数；在第n级设定缩放比例的目标缩小画面与第n级设定缩放比例的参考缩小画面的对应区域中，将上一级设定缩放比例的目标缩小画面的像素灰阶与上一级设定缩放比例的参考缩小画面的像素灰阶进行比较，获取上一级设定缩放比例的目标缩小画面与上一级设定缩放比例的参考缩小画面的对应区域，重复步骤C直至获取第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域；在第一级设定缩放比例的目标缩小画面与第一级设定缩放比例的参考缩小画面的对应区域中，将目标画面中每个目标画面对齐区域的像素灰阶与每个参考画面的像素灰阶进行比较，获取目标画面中每个目标画面对齐区域在每个参考画面中对应的参考画面对齐区域。
  10. 根据权利要求8所述的画面优化装置，其中多个目标画面对齐区域的区域形状相同，相邻的目标画面对齐区域的重叠区域大于等于所述目标画面对齐区域的区域面积的50%。
  11. 根据权利要求8所述的画面优化装置，其中所述目标画面和所述参考画面为设定时间内对同一区域的连拍画面或设定时间内的显示同一区域的多个连续的视频画面帧。
  12. 根据权利要求8所述的画面优化装置，其中所述优化模块用于基于所述目标画面对齐区域与每个参考画面对应的参考画面对齐区域的相似度，生成对应的参考画面的叠加融合权重；基于所述参考画面的叠加融合权重，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合。
  13. 根据权利要求12所述的画面优化装置，其中所述优化模块具体用于对所述目标画面相应的目标画面对齐区域进行离散傅里叶变换，获取所述目标画面对齐区域的目标傅里叶频谱；对所述参考画面的参考画面对齐区域进行离散傅里叶变换，获取所述参考画面的参考画面对齐区域的参考傅里叶频谱；使用所述参考画面的叠加融合权重以及参考画面对齐区域的参考傅里叶频谱，对所述目标画面对齐区域的目标傅里叶频谱进行加权叠加，以得到叠加融合后的目标画面对齐区域的目标傅里叶频谱；对所述叠加融合后的目标画面对齐区域的目标傅里叶频谱进行离散傅里叶逆变换，得到叠加融合后的目标画面对齐区域。
  14. 根据权利要求8所述的画面优化装置，其中所述优化模块还用于获取降噪操作后的目标画面的亮度分布图；对所述降噪操作后的目标画面中亮度值小于设定值的区域，进行局部亮度调节。
  15. 一种计算机可读存储介质，其内存储有处理器可执行指令，该处理器通过执行所述指令提供一种画面优化方法，包括：
    获取目标画面以及对应的多张参考画面；其中所述目标画面和所述参考画面为同一区域的相关画面；
    按设定区域尺寸，将所述目标画面划分为多个目标画面对齐区域，相邻的目标画面对齐区域具有重叠区域；
    基于所述目标画面以及所述参考画面的像素灰阶，获取目标画面中每个目标画面对齐区域，在每个参考画面中对应的参考画面对齐区域以及与对应参考画面对齐区域的相似度；以及
    基于所述相似度，使用多个参考画面的参考画面对齐区域对所述目标画面相应的目标画面对齐区域进行叠加融合，以对所述目标画面进行降噪操作。
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