CN113326721A - Image blur detection method and device based on sliding window re-blur - Google Patents
Image blur detection method and device based on sliding window re-blur Download PDFInfo
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- CN113326721A CN113326721A CN202010578148.9A CN202010578148A CN113326721A CN 113326721 A CN113326721 A CN 113326721A CN 202010578148 A CN202010578148 A CN 202010578148A CN 113326721 A CN113326721 A CN 113326721A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Abstract
The invention provides an image blur detection method and device based on sliding window re-blur and a computer readable storage medium, and belongs to the technical field of image processing and analysis. Judging the palm area of the acquired image; performing re-blurring processing on the target image to obtain a re-blurred image; converting the target image and the re-blurred image into a gray-scale image; respectively carrying out edge detection on the two obtained gray level images to obtain respective edge images; respectively carrying out sliding window blocking processing on the two obtained edge images, and calculating the structural similarity of the two edge images; and comparing the structural similarity with a preset threshold, if the similarity is greater than or equal to the preset threshold, judging that the target image is clear, otherwise, judging that the target image is fuzzy. In a word, the image blur detection method based on sliding window re-blur provided by the invention can extract image edge information at one time aiming at an embedded application scene, freely set the size of the sliding window and meet the requirement of real-time property on the basis of ensuring the detection accuracy.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to an image blur detection method and device based on sliding window re-blur.
Background
In the use process of the kindergarten morning examination robot product of the company and the use process of the kindergarten morning examination robot product, due to the field environment difference, lively and active children and the like, the condition that the palm images of the children for herpes detection are acquired by the camera often appears fuzzy distortion, and the efficiency and the accuracy of background herpes detection are seriously influenced. In order to solve the above problems, it is necessary to implement a fast and effective image blur detection method that can be executed on a front-end embedded platform and is used for real-time filtering of palm images.
In inventions 201710518661.7 and 201810137970.4, image segmentation and re-blurring techniques are used that are similar to those of the present patent. In both methods, the original image is divided into a plurality of sub-blocks after being subjected to fuzzy processing, Canny edge information of the sub-blocks is extracted for similarity comparison, and in the method, Sobel edge information is directly extracted from the whole image, and then window sliding and partitioning are performed for similarity comparison.
Disclosure of Invention
The existing fuzzy detection method is difficult to consider both real-time performance and accuracy, and particularly on embedded platforms such as robots and the like, the problem of limited computing resources is particularly prominent. The method provided by the invention can be used for extracting the image edge information at one time aiming at an embedded application scene, reasonably setting the size of the sliding window and meeting the requirement of real-time property on the basis of ensuring the detection accuracy.
The technical scheme of the image blur detection method based on sliding window re-blur provided by the invention specifically comprises the following steps:
the specific technical scheme is as follows:
step S1: judging a palm area of the acquired image, and if the image contains the palm area, extracting an image of the palm area as a target image to be investigated;
step S2: performing re-blurring processing on the target image to obtain a re-blurred image;
step S3: converting the target image and the re-blurred image into a gray-scale image;
step S4: respectively carrying out edge detection on the two obtained gray level images to obtain respective edge images;
step S5: respectively carrying out sliding window blocking processing on the two obtained edge images, and calculating the structural similarity of the two edge images;
step S6: comparing the structural similarity with a preset threshold, if the similarity is greater than or equal to the preset threshold, judging that the target image is clear, otherwise, judging that the target image is fuzzy;
looping through steps S1 to S6 completes the blur detection of the palm image.
Preferably, the extraction method of step S1 is embodied as detecting the palm through a specially trained deep YOLOV3 neural network.
Preferably, the specific method for edge detection is as follows:
adopting a Sobel operator, utilizing two groups of matrixes of 3x3 contained in the Sobel operator to respectively correspond to the transverse direction and the longitudinal direction, and performing plane convolution on the Sobel operator and the image to obtain the gray gradients of the transverse direction and the longitudinal direction of each pixel point;
taking the square sum of the horizontal and longitudinal gray gradients and then squaring to obtain a comprehensive gradient;
the edge image can be obtained after compressing the image into 8-bit gray scale values.
Preferably, when the sliding window blocking processing is performed for blocking, the size of the sliding window and the sliding step length are set to obtain a plurality of sub-blocks, and the variance of each sub-block is calculated.
Preferably, the similarity value is calculated by: and subtracting the corresponding variances of each sub-block of the edge image, then taking an absolute value, and averaging to obtain a similarity value.
The invention also provides a device of the image blur detection method based on sliding window re-blur, which is characterized by comprising the following steps:
a memory having computer program code stored thereon; and
a processor configured to execute the computer program code to perform the above-described method.
The invention also provides a computer-readable storage medium having stored thereon computer program code which, when executed, performs the above-described method.
Compared with the prior art, the image blur detection method based on sliding window re-blur provided by the invention has the key points of re-blur and edge detection of the palm image, sliding window blocking operation and similarity calculation.
By using the method in the patent, real-time detection of whether the palm image on the embedded platform is fuzzy can be realized, the clear picture can reach 97% of passing rate, and the fuzzy picture can be filtered out by 55%.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
fig. 1 is a schematic flow chart of an application example of the image blur detection method based on sliding window re-blur provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, 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.
Fig. 1 is a schematic flow chart of an application example of the image blur detection method based on sliding window re-blur provided by the present invention. The specific technical scheme is as follows:
step S1-1: and extracting a palm area image. Detecting the palm through a specially trained deep neural network, extracting the palm from the image with the palm, and extracting an image H of the palm area.
Step S1-2: the extracted palm region image H is subjected to a re-blurring process. And performing re-blurring processing on the image H by adopting Gaussian smoothing filtering to obtain a re-blurred image B.
Step S1-3: the palm area image H is converted into a grayscale image X.
Step S1-4: the re-blurred image B is converted into a grayscale image Y.
Step S1-5: edge detection is performed on the grayscale image X. The Sobel operator is one of the most important operators in image edge detection, and comprises two groups of 3x3 matrixes which respectively correspond to the transverse direction and the longitudinal direction and are subjected to plane convolution with the image, so that transverse and longitudinal gray gradients can be obtained. The gradients in two directions can be obtained for each pixel point, the sum of squares of the gradients is taken and then the square is taken to obtain a comprehensive gradient, and the comprehensive gradient is compressed into 8-bit gray values to obtain an edge image P.
Step S1-6: edge detection is performed on the grayscale image Y. The same operation as S1-5 is performed to obtain an edge image Q.
Step S1-7: and performing sliding window blocking processing on the edge image P. The size of the sliding window and the sliding step length can be set according to needs during partitioning, so that a plurality of sub-blocks are obtained, and the variance of each sub-block is calculated.
Step S1-8: and performing sliding window blocking processing on the edge image Q, and calculating the variance of each sub-block.
Step S1-9: and calculating the structural similarity of the images. And subtracting the variances of all corresponding sub-blocks in the edge image P and Q, then taking an absolute value, and averaging to obtain a similarity value.
Step S1-10: and comparing the structural similarity with a preset threshold, if the similarity is greater than or equal to the preset threshold, judging that the target image is clear, otherwise, judging that the target image is fuzzy.
The invention also provides a device of the image blur detection method based on sliding window re-blur, which is characterized by comprising the following steps:
a memory having computer program code stored thereon; and
a processor configured to execute the computer program code to perform the above-described method.
The present invention may be methods, apparatus, systems and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therein for carrying out aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (7)
1. An image blur detection method based on sliding window re-blur is characterized in that: the specific technical scheme is as follows:
judging a palm area of the acquired image, and if the image contains the palm area, extracting an image of the palm area as a target image to be investigated;
performing re-blurring processing on the target image to obtain a re-blurred image;
converting the target image and the re-blurred image into a gray-scale image;
respectively carrying out edge detection on the two obtained gray level images to obtain respective edge images;
respectively carrying out sliding window blocking processing on the two obtained edge images, and calculating the structural similarity of the two edge images;
and comparing the structural similarity with a preset threshold, if the similarity is greater than or equal to the preset threshold, judging that the target image is clear, otherwise, judging that the target image is fuzzy.
2. The sliding window re-blur based image blur detection method according to claim 1, characterized by: the method for extracting the palm area image is to detect the palm through a specially trained deep YOLOV3 neural network.
3. The sliding window re-blur based image blur detection method according to claim 1, characterized by: the specific method for edge detection is as follows:
adopting a Sobel operator, utilizing two groups of matrixes of 3x3 contained in the Sobel operator to respectively correspond to the transverse direction and the longitudinal direction, and performing plane convolution on the Sobel operator and the image to obtain the gray gradients of the transverse direction and the longitudinal direction of each pixel point;
taking the square sum of the horizontal and longitudinal gray gradients and then squaring to obtain a comprehensive gradient;
and compressing the image into 8-bit gray value to obtain an edge image.
4. The sliding window re-blur based image blur detection method according to claim 1, characterized by: when the sliding window blocking processing blocking is carried out, the size of the sliding window and the sliding step length are set to obtain a plurality of sub-blocks, and the variance of each sub-block is calculated.
5. The sliding window re-blur based image blur detection method according to claim 1, characterized by: the similarity value calculation method comprises the following steps: and subtracting the corresponding variances of each sub-block of the edge image, then taking an absolute value, and averaging to obtain a similarity value.
6. An apparatus of an image blur detection method based on sliding window re-blurring, comprising:
a memory having computer program code stored thereon; and
a processor configured to execute the computer program code to perform the method of any of claims 1 to 5.
7. A computer readable storage medium having stored thereon computer program code which, when executed, performs the method of any of claims 1 to 5.
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