CN110705511A - Blurred image recognition method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a blurred image identification method, a blurred image identification device, blurred image identification equipment and a storage medium. The method comprises the following steps: carrying out image segmentation on an image to be recognized to obtain a plurality of sub-images; respectively carrying out fuzzy recognition on the plurality of sub-images to obtain a fuzzy confidence coefficient of each sub-image; and determining the fuzziness of the image to be recognized according to the fuzzy confidence coefficient of each sub-image. According to the identification method of the blurred image, the blurring degree of the image to be identified is determined according to the blurring confidence degrees of the sub-images, manual identification is not needed, and the blurred image is not needed to be identified after being zoomed to a certain size, so that the efficiency and the accuracy of blurred image identification can be improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of image recognition, and in particular relates to a method, a device, equipment and a storage medium for recognizing a blurred image.
Background
When a user takes a picture by using the terminal equipment, the taken picture is fuzzy due to the influence of external factors (such as hand shake and object movement), and the fuzzy pictures usually do not meet the requirements and need to be cleaned.
The existing method comprises the following steps: firstly, a fuzzy image is picked up and deleted by a manual identification method, and the method has certain subjectivity and high labor cost; secondly, the original image is zoomed to a certain size and then recognized, and the method has the defects that some blurred images are zoomed to a certain size and then become not blurred any more due to the improvement of resolution ratio, so that the original blurred images cannot be recognized, and the accuracy is low.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device, equipment and a storage medium for recognizing a blurred image, so as to realize recognition of the blurred image and improve efficiency and accuracy of recognition of the blurred image.
In a first aspect, an embodiment of the present disclosure provides a method for recognizing a blurred image, where the method includes:
carrying out image segmentation on an image to be recognized to obtain a plurality of sub-images;
respectively carrying out fuzzy recognition on the plurality of sub-images to obtain a fuzzy confidence coefficient of each sub-image;
and determining the fuzziness of the image to be recognized according to the fuzzy confidence coefficient of each sub-image.
In a second aspect, an embodiment of the present disclosure further provides an apparatus for recognizing a blurred image, where the apparatus includes:
the subimage acquisition module is used for carrying out image segmentation on the image to be identified to obtain a plurality of subimages;
the fuzzy confidence coefficient acquisition module is used for respectively carrying out fuzzy recognition on the plurality of sub-images to obtain the fuzzy confidence coefficient of each sub-image;
and the ambiguity determining module is used for determining the ambiguity of the image to be recognized according to the ambiguity confidence of each sub-image.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
one or more processing devices;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processing devices, the one or more processing devices are caused to implement the blurred image recognition method according to the embodiment of the present disclosure.
In a fourth aspect, the disclosed embodiment also provides a computer readable medium, on which a computer program is stored, where the computer program is executed by a processing device to implement the identification method of the blurred image according to the disclosed embodiment.
According to the embodiment of the disclosure, firstly, an image to be recognized is subjected to image segmentation to obtain a plurality of sub-images, then the plurality of sub-images are subjected to fuzzy recognition respectively to obtain the fuzzy confidence coefficient of each sub-image, and finally the fuzzy degree of the image to be recognized is determined according to the fuzzy confidence coefficient of each sub-image. According to the identification method of the blurred image, the blurring degree of the image to be identified is determined according to the blurring confidence degrees of the sub-images, manual identification is not needed, and the blurred image is not needed to be identified after being zoomed to a certain size, so that the efficiency and the accuracy of blurred image identification can be improved.
Drawings
Fig. 1 is a flowchart of a blurred image recognition method in a first embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a blurred image recognition apparatus in a second embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device in a third embodiment of the disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units. [ ordinal numbers ]
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Example one
Fig. 1 is a flowchart of a blurred image recognition method according to an embodiment of the present disclosure, where this embodiment is applicable to a case of recognizing a blurred image, and the method may be executed by a blurred image recognition apparatus, where the apparatus may be composed of hardware and/or software, and may be generally integrated in a device having a blurred image recognition function, where the device may be an electronic device such as a server, a mobile terminal, or a server cluster. As shown in fig. 1, the method specifically includes the following steps:
and step 110, carrying out image segmentation on the image to be recognized to obtain a plurality of sub-images.
The image may be segmented by meshing the image to be recognized to obtain sub-images of equal size. The mesh for segmenting the image to be recognized may be a set mesh, that is, any image to be recognized is divided by using the set mesh, wherein each sub-lattice in the set mesh may be a square or a rectangle with a certain aspect ratio (e.g., 1:2 or 2: 1). For example: assuming that the set grid is 5 × 5 and each sub-grid is a rectangular grid with an aspect ratio of 1:2, when any image to be recognized is segmented, 25 rectangular sub-images with an aspect ratio of 1:2 are obtained. The sub-images obtained by grid division are not overlapped, so that the segmentation speed can be improved, and the calculated amount can be reduced due to no overlapping of the sub-images, so that the identification efficiency of the blurred image is improved.
Optionally, the image may be segmented by intercepting the image to be recognized with a rectangular frame of a set size at a certain step length to obtain a plurality of sub-images. Wherein the step size is smaller than the length and width of the rectangular frame, so that there is an overlap area between adjacent sub-images. The size of the rectangular frame may be determined by the image size required by the blurred image recognition model, such as may be less than or equal to the size required by the blurred image recognition model. In the embodiment of the disclosure, because the overlapping regions exist between the adjacent sub-images, the overlapping regions can be recognized for many times when the sub-images are respectively subjected to fuzzy recognition, so that the accuracy of the fuzzy recognition can be improved.
Specifically, the image segmentation is performed on the image to be recognized, and the manner of obtaining the plurality of sub-images may be: adjusting an image to be identified into an image with a standard size; and performing grid division on the image with the standard size to obtain a plurality of sub-images.
Wherein the standard size may be a preset size. This is because the sizes of the pictures that may be taken by different types of terminal devices are different, and therefore the images to be recognized need to be uniformly adjusted to the images with the standard sizes, so as to facilitate subsequent processing.
And step 120, respectively carrying out fuzzy recognition on the plurality of sub-images to obtain the fuzzy confidence coefficient of each sub-image.
The fuzzy confidence is used to indicate the probability that the sub-image is the fuzzy image, and the value is a value between 0 and 1, for example, if the fuzzy confidence of a certain sub-image is 0.8, the probability that the sub-image is the fuzzy image is 80%.
In this embodiment, the way of performing fuzzy recognition on the plurality of sub-images may be to perform fuzzy recognition on the sub-images by using a neural network. Specifically, the process of performing fuzzy recognition on the plurality of sub-images to obtain the fuzzy confidence of each sub-image may be: and respectively inputting the plurality of sub-images into a preset fuzzy image recognition model to obtain the fuzzy confidence coefficient of each sub-image.
Wherein, the fuzzy image recognition model can be obtained based on a large number of sample pairs to set neural network training. Specifically, the process of training the blurred image recognition model may be to divide the blurred image into a plurality of sub-blurred images, and mark the plurality of sub-blurred images as a first sample; dividing the clear image into a plurality of sub-clear images, and marking the plurality of sub-clear images as second samples; and training the set neural network according to the first sample and/or the second sample to obtain a preset fuzzy image recognition model.
When the blurred image and the clear image are segmented, the blurred image and the clear image need to be adjusted to be images with standard sizes, and then grid division is carried out, so that a plurality of sub blurred images and a plurality of sub clear images are obtained. In this embodiment, when training the neural network, only the first sample composed of sub-blurred images may be used, only the second sample composed of sub-sharp images may be used, or both the first sample and the second sample may be used. And training the set neural network by using the first sample and/or the second sample until the neural network has the capability of preparing to recognize the blurred image, thereby obtaining the blurred image recognition model.
And step 130, determining the fuzziness of the image to be recognized according to the fuzzy confidence of each sub-image.
Specifically, after the fuzzy confidence coefficient of each sub-image is obtained, the fuzzy confidence coefficient of each sub-image is weighted according to a set weight value, and the fuzzy degree of the image to be recognized is obtained. The weight corresponding to each sub-image may be the same or different, and the weight of each sub-image may be preset. In this embodiment, the weight value of the sub-image in the central area of the image to be recognized is greater than the weight value of the sub-image in the edge area. This is because in practical situations, the key content of the image is generally in the central area of the image, and the identification of the blur degree of the central area of the image is more important than that of the edge area.
Optionally, after obtaining the blur degree of the image to be recognized, the method further includes the following steps: and if the fuzziness of the image to be recognized is greater than a first set threshold value, the image to be recognized is a blurred image.
In this embodiment, if the image to be recognized is a blurred image, the image may be deleted, and if the image is not a blurred image, the image may be retained.
Optionally, the method for determining the blur degree of the image to be recognized according to the blur confidence of each sub-image may further include: acquiring a subimage with the fuzzy confidence coefficient larger than a second set threshold value, and determining the subimage as a fuzzy subimage; and calculating the proportion of the blurred sub-images in all the sub-images, and if the proportion exceeds a third threshold value, determining the image to be recognized as a blurred image.
Wherein the second set threshold value may be set to any value between 80% and 90%. The third threshold value may be set to any value between 70% and 80%. Assuming that the second set threshold is set to 85%, where the confidence of blurring for a certain sub-image is 90%, the sub-image is a blurred sub-image. For example, assuming that one image to be recognized contains 25 sub-images, of which 20 are blurred sub-images, that is, the ratio of blurred sub-images to the total sub-image is 80%, and the third threshold is set to 65%, the image to be recognized is a blurred image.
According to the technical scheme of the embodiment, firstly, image segmentation is carried out on an image to be recognized to obtain a plurality of sub-images, then the plurality of sub-images are subjected to fuzzy recognition respectively to obtain the fuzzy confidence coefficient of each sub-image, and finally the fuzzy degree of the image to be recognized is determined according to the fuzzy confidence coefficient of each sub-image. According to the identification method of the blurred image, the blurring degree of the image to be identified is determined according to the blurring confidence degrees of the sub-images, manual identification is not needed, and the blurred image is not needed to be identified after being zoomed to a certain size, so that the efficiency and the accuracy of blurred image identification can be improved.
Example two
Fig. 2 is a schematic structural diagram of a blurred image recognition apparatus according to a second embodiment of the present disclosure. As shown in fig. 2, the apparatus includes: a sub-image acquisition module 210, a blur confidence acquisition module 220, and a blur level determination module 230.
The sub-image obtaining module 210 is configured to perform image segmentation on an image to be identified to obtain a plurality of sub-images;
the fuzzy confidence coefficient obtaining module 220 is configured to perform fuzzy recognition on the multiple sub-images respectively to obtain a fuzzy confidence coefficient of each sub-image;
and the ambiguity determining module 230 is configured to determine the ambiguity of the image to be recognized according to the ambiguity confidence of each sub-image.
Optionally, the sub-image obtaining module 210 is further configured to:
adjusting an image to be identified into an image with a standard size;
and performing grid division on the image with the standard size to obtain a plurality of sub-images.
Optionally, the fuzzy confidence obtaining module 220 is further configured to:
and respectively inputting the plurality of sub-images into a preset fuzzy image recognition model to obtain the fuzzy confidence coefficient of each sub-image.
Optionally, the method further includes: a blurred image recognition model acquisition module configured to:
dividing the blurred image into a plurality of sub-blurred images, and marking the plurality of sub-blurred images as first samples;
dividing the clear image into a plurality of sub-clear images, and marking the plurality of sub-clear images as second samples;
and training the set neural network according to the first sample and/or the second sample to obtain a preset fuzzy image recognition model.
Optionally, the ambiguity determining module 230 is further configured to:
carrying out weighted calculation on the fuzzy confidence coefficient of each subimage according to a set weight value to obtain the fuzzy degree of the image to be recognized; the weight value of the sub-image in the central area of the image to be identified is larger than that of the sub-image in the edge area.
Optionally, the method further includes a blurred image determination module, configured to:
and if the fuzziness of the image to be recognized is greater than a first set threshold value, the image to be recognized is a blurred image.
Optionally, the ambiguity determining module 230 is further configured to:
acquiring a subimage with the fuzzy confidence coefficient larger than a second set threshold value, and determining the subimage as a fuzzy subimage;
and calculating the proportion of the blurred sub-images in all the sub-images, and if the proportion exceeds a third threshold value, determining the image to be recognized as a blurred image.
The device can execute the methods provided by all the embodiments of the disclosure, and has corresponding functional modules and beneficial effects for executing the methods. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in all the foregoing embodiments of the disclosure.
EXAMPLE III
Referring now to FIG. 3, a block diagram of an electronic device 300 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like, or various forms of servers such as a stand-alone server or a server cluster. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, electronic device 300 may include a processing means (e.g., central processing unit, graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a read-only memory device (ROM)302 or a program loaded from a storage device 305 into a random access memory device (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program containing program code for performing a method for recommending words. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 305, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: carrying out image segmentation on an image to be recognized to obtain a plurality of sub-images; respectively carrying out fuzzy recognition on the plurality of sub-images to obtain a fuzzy confidence coefficient of each sub-image; and determining the fuzziness of the image to be recognized according to the fuzzy confidence coefficient of each sub-image.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the 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).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
According to one or more embodiments of the present disclosure, there is provided a blurred image recognition method including:
carrying out image segmentation on an image to be recognized to obtain a plurality of sub-images;
respectively carrying out fuzzy recognition on the plurality of sub-images to obtain a fuzzy confidence coefficient of each sub-image;
and determining the fuzziness of the image to be recognized according to the fuzzy confidence coefficient of each sub-image.
Further, performing image segmentation on the image to be recognized to obtain a plurality of sub-images, including:
adjusting an image to be identified into an image with a standard size;
and carrying out grid division on the image with the standard size to obtain a plurality of sub-images.
Further, the fuzzy recognition is respectively performed on the plurality of sub-images to obtain a fuzzy confidence of each sub-image, and the method comprises the following steps:
and respectively inputting the plurality of sub-images into a preset fuzzy image recognition model to obtain the fuzzy confidence coefficient of each sub-image.
Further, before the plurality of sub-images are respectively input into a preset blurred image recognition model, the method further includes:
dividing the blurred image into a plurality of sub-blurred images, and marking the plurality of sub-blurred images as first samples;
dividing the clear image into a plurality of sub-clear images, and marking the plurality of sub-clear images as second samples;
and training a set neural network according to the first sample and/or the second sample to obtain a preset fuzzy image recognition model.
Further, determining the fuzziness of the image to be recognized according to the fuzzy confidence of each sub-image, comprising:
carrying out weighted calculation on the fuzzy confidence coefficient of each subimage according to a set weight value to obtain the fuzzy degree of the image to be recognized; the weight value of the sub-image in the central area of the image to be identified is larger than that of the sub-image in the edge area.
Further, after obtaining the fuzziness of the image to be recognized, the method further comprises the following steps:
and if the fuzziness of the image to be recognized is greater than a first set threshold value, the image to be recognized is a blurred image.
Further, determining the fuzziness of the image to be recognized according to the fuzzy confidence of each sub-image, comprising:
acquiring a subimage with the fuzzy confidence coefficient larger than a second set threshold value, and determining the subimage as a fuzzy subimage;
and calculating the proportion of the blurred sub-images in all the sub-images, and if the proportion exceeds a third threshold value, determining the image to be recognized as a blurred image.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present disclosure and the technical principles employed. Those skilled in the art will appreciate that the present disclosure is not limited to the particular embodiments described herein, and that various obvious changes, adaptations, and substitutions are possible, without departing from the scope of the present disclosure. Therefore, although the present disclosure has been described in greater detail with reference to the above embodiments, the present disclosure is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present disclosure, the scope of which is determined by the scope of the appended claims.
Claims (10)
1. A blurred image recognition method is characterized by comprising the following steps:
carrying out image segmentation on an image to be recognized to obtain a plurality of sub-images;
respectively carrying out fuzzy recognition on the plurality of sub-images to obtain a fuzzy confidence coefficient of each sub-image;
and determining the fuzziness of the image to be recognized according to the fuzzy confidence coefficient of each sub-image.
2. The method of claim 1, wherein performing image segmentation on the image to be identified to obtain a plurality of sub-images comprises:
adjusting an image to be identified into an image with a standard size;
and carrying out grid division on the image with the standard size to obtain a plurality of sub-images.
3. The method of claim 1, wherein the fuzzy recognition is performed on the plurality of sub-images respectively to obtain a fuzzy confidence of each sub-image, and the fuzzy confidence comprises:
and respectively inputting the plurality of sub-images into a preset fuzzy image recognition model to obtain the fuzzy confidence coefficient of each sub-image.
4. The method according to claim 3, wherein the preset blurred image recognition model is obtained by:
dividing the blurred image into a plurality of sub-blurred images, and marking the plurality of sub-blurred images as first samples;
dividing the clear image into a plurality of sub-clear images, and marking the plurality of sub-clear images as second samples;
and training a set neural network according to the first sample and/or the second sample to obtain a preset fuzzy image recognition model.
5. The method of claim 1, wherein determining the blur level of the image to be recognized according to the blur confidence of each sub-image comprises:
carrying out weighted calculation on the fuzzy confidence coefficient of each subimage according to a set weight value to obtain the fuzzy degree of the image to be recognized; the weight value of the sub-image in the central area of the image to be identified is larger than that of the sub-image in the edge area.
6. The method according to claim 5, after obtaining the blur degree of the image to be recognized, further comprising:
and if the fuzziness of the image to be recognized is greater than a first set threshold value, the image to be recognized is a blurred image.
7. The method of claim 1, wherein determining the blur level of the image to be recognized according to the blur confidence of each sub-image comprises:
acquiring a subimage with the fuzzy confidence coefficient larger than a second set threshold value, and determining the subimage as a fuzzy subimage;
and calculating the proportion of the blurred sub-images in all the sub-images, and if the proportion exceeds a third threshold value, determining the image to be recognized as a blurred image.
8. An apparatus for recognizing a blurred image, comprising:
the subimage acquisition module is used for carrying out image segmentation on the image to be identified to obtain a plurality of subimages;
the fuzzy confidence coefficient acquisition module is used for respectively carrying out fuzzy recognition on the plurality of sub-images to obtain the fuzzy confidence coefficient of each sub-image;
and the ambiguity determining module is used for determining the ambiguity of the image to be recognized according to the ambiguity confidence of each sub-image.
9. An electronic device, characterized in that the electronic device comprises:
one or more processing devices;
storage means for storing one or more programs;
when executed by the one or more processing devices, cause the one or more processing devices to implement the method of identifying a blurred image as claimed in any of claims 1 to 7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by processing means, carries out the method of blurred image recognition according to any of claims 1-7.
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