Digital slice splicing method and device, electronic equipment and storage medium
By performing quality assessment and rescanning on the target area of the sliced and stitched image, the image blurring problem in the existing technology is solved, the stitched image quality is improved, and scanning time is saved.
Patent Information
- Application Number
- CN202410352002.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-26
- Publication Date
- 2025-09-26
AI Technical Summary
There is a problem of image blur in the existing slice stitching solution.
By obtaining a scanned image of the target area of the slice, the image quality is evaluated using a pre-trained image quality assessment model. When the image is blurred, the target area is rescanned to obtain a high-quality scanned image of the target slice and perform stitching.
The quality of slice stitching images is improved, and only the target area is rescanned, saving slice scanning time.
Smart Images
Figure CN120707375A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of image processing technology, and in particular to a digital slice splicing method, device, electronic device and storage medium. Background Art
[0002] With the development of image processing technology, digital slice image stitching technology came into being.
[0003] In the process of implementing the present invention, it was found that there are at least the following technical problems in the prior art: in the existing slice splicing solution, the slice splicing image has image quality problems such as blurring. Summary of the Invention
[0004] The present invention provides a digital slice splicing method, device, electronic device and storage medium to improve the quality of slice splicing images.
[0005] According to one aspect of the present invention, a digital slice stitching method is provided, comprising:
[0006] Acquire a slice scanning image of a target area of the slice;
[0007] Inputting the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice;
[0008] When the quality assessment result indicates that the slice image is blurred, rescanning the target area of the slice to obtain a target slice scan image of the target area of the slice;
[0009] The target slice scan images are stitched together to obtain a slice stitching image.
[0010] According to another aspect of the present invention, a digital slice splicing device is provided, comprising:
[0011] A target area image acquisition module is used to acquire a slice scanning image of a target area of a slice;
[0012] An image quality assessment module is used to input the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice;
[0013] a target area rescanning module, configured to rescan the target area of the slice to obtain a target slice scan image of the target area of the slice when the quality assessment result indicates that the slice image is blurred;
[0014] The scanning image stitching module is used to stitch the target slice scanning images to obtain a slice stitching image.
[0015] According to another aspect of the present invention, an electronic device is provided, comprising:
[0016] at least one processor;
[0017] and a memory communicatively coupled to the at least one processor;
[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the digital slice stitching method described in any embodiment of the present invention.
[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the digital slice stitching method according to any embodiment of the present invention when executed.
[0020] The technical solution of the embodiment of the present invention obtains a slice scan image of the target area of the slice, and then inputs the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice; when the quality assessment result is that the slice image is blurred, the target area of the slice is rescanned to obtain a target slice scan image of the target area of the slice, and then splicing is performed based on the target slice scan image to obtain a slice splicing image. The above technical solution obtains a high-quality target slice scan image by rescanning the target area of the slice image, and then uses the high-quality target slice scan image for splicing, thereby improving the image quality of the slice splicing image. In addition, only the target area is rescanned, rather than the entire area of the slice, which saves slice scanning time.
[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0023] Figure 1 This is a flow chart of a digital slice splicing method provided according to the first embodiment of the present invention;
[0024] Figure 2 This is a flow chart of a digital slice splicing method provided according to the second embodiment of the present invention;
[0025] Figure 3 This is a flow chart of a digital slice splicing method provided according to the third embodiment of the present invention;
[0026] Figure 4 This is a structural diagram of a digital slice splicing device provided according to a fourth embodiment of the present invention;
[0027] Figure 5 It is a structural schematic diagram of an electronic device for implementing the digital slice splicing method according to an embodiment of the present invention. DETAILED DESCRIPTION
[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.
[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.
[0030] Example 1
[0031] Figure 1This is a flow chart of a digital slice stitching method provided in the first embodiment of the present invention. This embodiment is applicable to the case of automatic stitching of digital slice images. The method can be executed by a digital slice stitching device. The digital slice stitching device can be implemented in the form of hardware and / or software. The digital slice stitching device can be configured in a terminal and / or a server. Figure 1 As shown, the method includes:
[0032] S110 , obtaining a slice scanning image of a target area of the slice.
[0033] The slice scan image refers to an image obtained by scanning a target area of a slice using a scanner. The target area can be one or more columns of the slice, or one or more rows of the slice. The column width or row width can be customized and is not specifically limited here.
[0034] Specifically, the slice scan image of the target area of the slice can be retrieved from a preset storage path of the electronic device, or the slice scan image of the target area of the slice can be obtained in real time from a scanner connected to the electronic device for communication, which is not specifically limited here.
[0035] S120 , inputting the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice.
[0036] In this embodiment, the image quality assessment model is a neural network model capable of evaluating the quality of slice scan images. Specifically, a slice scan image of a target region of a slice can be used as input data for the image quality assessment model. The image quality assessment model then outputs a quality assessment result based on the slice scan image of the target region of the slice. The quality assessment result may include, but is not limited to, whether the slice image is blurry or clear.
[0037] In an embodiment of the present disclosure, the training steps of the image quality assessment model include: obtaining a slice scan sample image and a quality label corresponding to the slice scan sample image, wherein the quality label is that the slice image is clear or the slice image is blurred; based on the slice scan sample image and the quality label corresponding to the slice scan sample image, the initial neural network is trained to obtain a trained image quality assessment model.
[0038] The initial neural network can be a neural network model of any network architecture and is not specifically limited herein. It should be noted that by training the initial neural network with a large number of slice scan sample images, an image quality assessment model capable of automatically assessing image quality can be obtained, providing a reference basis for local redrawing.
[0039] Optionally, the initial neural network is a residual neural network; accordingly, the initial neural network is trained based on the slice scan sample image and the quality label corresponding to the slice scan sample image to obtain a trained image quality assessment model, including: inputting the slice scan sample image into the residual neural network, the residual neural network outputs a quality prediction result, determining a model loss value based on the quality prediction result and the quality label corresponding to the slice scan sample image, and adjusting the network parameters of the residual neural network based on the model loss value until the model training stop condition is met, thereby obtaining a trained image quality assessment model.
[0040] It should be noted that compared with traditional neural networks, residual neural networks have better deep network construction capabilities, which can avoid gradient diffusion and gradient explosion caused by too deep network layers, thereby effectively improving the accuracy of image quality assessment of image quality assessment models.
[0041] In some optional embodiments, the clarity of the slice scan image may be determined by a Tenengrad gradient method, a Laplacian gradient method, or a variance method, that is, a quality assessment result corresponding to the slice scan image of the target region of the slice is determined.
[0042] S130 : When the quality assessment result indicates that the slice image is blurred, rescan the target area of the slice to obtain a target slice scan image of the target area of the slice.
[0043] It should be noted that the embodiment of the present disclosure performs a targeted rescan of the target area, and there is no need to rescan the entire area of the slice, thus saving slice scanning time.
[0044] S140 , performing stitching based on the target slice scan images to obtain a slice stitching image.
[0045] Specifically, the target slice scan image and other slice scan images can be spliced together according to the splicing displacement parameter to obtain a slice stitching image, wherein the splicing displacement parameter refers to the distance parameter that each slice scan image needs to be moved, and the splicing displacement parameter is used to adjust the position of each slice stitching image.
[0046] In some optional embodiments, when the quality assessment result shows that the slice image is clear, the slice scan images of the target area of the slice are spliced to obtain a slice splicing image, and the slice splicing image is displayed on the client.
[0047] The technical solution of the embodiment of the present invention obtains a slice scan image of the target area of the slice, and then inputs the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice; when the quality assessment result is that the slice image is blurred, the target area of the slice is rescanned to obtain a target slice scan image of the target area of the slice, and then splicing is performed based on the target slice scan image to obtain a slice splicing image. The above technical solution obtains a high-quality target slice scan image by rescanning the target area of the slice image, and then uses the high-quality target slice scan image for splicing, thereby improving the image quality of the slice splicing image. In addition, only the target area is rescanned, rather than the entire area of the slice, which saves slice scanning time.
[0048] Example 2
[0049] Figure 2 A flowchart of a digital slice stitching method provided in the second embodiment of the present invention, the method of this embodiment can be combined with the various optional schemes in the digital slice stitching method provided in the above embodiments. The digital slice stitching method provided in this embodiment has been further optimized. Optionally, the rescanning of the target area of the slice to obtain a target slice scan image of the target area of the slice includes: performing multi-layer scanning on the target area of the slice to obtain multi-layer slice rescan images of the target area of the slice; comparing the clarity of each layer of slice rescan images, and determining the slice rescan image with the highest clarity as the target slice scan image of the target area of the slice.
[0050] like Figure 2 As shown, the method includes:
[0051] S210 , obtaining a slice scanning image of a target area of the slice.
[0052] S220 , inputting the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice.
[0053] S230. When the quality assessment result indicates that the slice image is blurred, perform multi-layer scanning on the target area of the slice to obtain multi-layer slice rescan images of the target area of the slice; compare the clarity of the slice rescan images of each layer, and determine the slice rescan image with the highest clarity as the target slice scan image of the target area of the slice.
[0054] Multi-layer scanning refers to scanning a slice at different focal lengths and / or layers so that the rescanned image of the slice can more comprehensively represent the content of the slice.
[0055] It should be noted that by determining the slice rescan image with the highest definition as the target slice scan image of the target slice area, the image quality of the slice scan image of the target area is improved, thereby improving the image quality of the slice stitching image.
[0056] Optionally, performing multi-layer scanning on the target area of the slice to obtain a multi-layer slice rescan image of the target area of the slice includes: performing multi-layer scanning on the target area of the slice at equal intervals to obtain a multi-layer slice rescan image of the target area of the slice; or performing multi-layer scanning on the target area of the slice along a direction in which image clarity increases to obtain a multi-layer slice rescan image of the target area of the slice.
[0057] For example, the target area can be the third row or other row area of the slice. If the quality assessment result of the third row of the slice indicates that the slice image is blurred, the third row of the slice is subjected to a multi-layer scan with equal spacing on the Z axis, or the third row of the slice is sequentially multi-layered scanned in a direction of increasing image clarity until a multi-layer scanning stop condition is met, thereby terminating the multi-layer scanning. The multi-layer scanning stop condition may be when the number of scans exceeds a preset number of scans, or when the clarity of the rescanned slice image exceeds a preset clarity threshold, which is not specifically limited here.
[0058] S240 , performing stitching based on the target slice scan images to obtain a slice stitching image.
[0059] The technical solution of the embodiment of the present invention performs multi-layer scanning on the target area of the slice to obtain multi-layer slice rescan images of the target area of the slice, and then compares the clarity of the slice rescan images of each layer, and determines the slice rescan image with the highest clarity as the target slice scan image of the target area of the slice, thereby improving the image quality of the slice scan image of the target area, and further improving the image quality of the slice stitching image.
[0060] Example 3
[0061] Figure 3This is a flowchart of a digital slice stitching method provided in Example 3 of the present invention. The method of this embodiment can be combined with the various optional schemes in the digital slice stitching method provided in the above embodiments. The digital slice stitching method provided in this embodiment is further optimized. Optionally, the rescanning of the target area of the slice to obtain a target slice scan image of the target area of the slice includes: refocusing the target area of the slice; when the refocusing is completed, rescanning the target area of the slice to obtain a target slice scan image of the target area of the slice.
[0062] like Figure 3 As shown, the method includes:
[0063] S310 , obtaining a slice scanning image of a target area of the slice.
[0064] S320: Input the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice.
[0065] S330. When the quality assessment result shows that the slice image is blurred, refocus the target area of the slice; when the refocusing is completed, rescan the target area of the slice to obtain a target slice scan image of the target area of the slice.
[0066] It should be noted that by refocusing the target area of the slice, the focus can be reselected, which helps to improve the image quality of the target slice scan image.
[0067] Optionally, refocusing the target area of the slice includes: acquiring a non-blank slice scan image of the target area of the slice; acquiring a target focus in the non-blank slice scan image, and focusing based on the target focus.
[0068] The non-blank slice scan image refers to a scan image in which a sample exists in the target area.
[0069] For example, after obtaining a non-blank slice scan image of a target area of a slice, one or more target focus points can be taken at preset intervals in the non-blank slice scan image, and then focusing and scanning are performed according to the target focus points to obtain the target focus points.
[0070] S340: performing stitching based on the target slice scan images to obtain a slice stitching image.
[0071] The technical solution of the embodiment of the present invention can achieve re-selection of focus by refocusing the target area of the slice, thereby helping to improve the image quality of the target slice scan image.
[0072] Example 4
[0073] Figure 4 This is a structural diagram of a digital slice splicing device provided by the fourth embodiment of the present invention. Figure 4 As shown, the device includes:
[0074] The target area image acquisition module 410 is used to acquire a slice scanning image of a target area of the slice;
[0075] An image quality assessment module 420 is configured to input the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice;
[0076] A target area rescanning module 430 is configured to rescan the target area of the slice to obtain a target slice scan image of the target area of the slice when the quality assessment result indicates that the slice image is blurred;
[0077] The scan image stitching module 440 is configured to stitch the target slice scan images to obtain a slice stitching image.
[0078] The technical solution of the embodiment of the present invention obtains a slice scan image of the target area of the slice, and then inputs the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice; when the quality assessment result is that the slice image is blurred, the target area of the slice is rescanned to obtain a target slice scan image of the target area of the slice, and then splicing is performed based on the target slice scan image to obtain a slice splicing image. The above technical solution obtains a high-quality target slice scan image by rescanning the target area of the slice image, and then uses the high-quality target slice scan image for splicing, thereby improving the image quality of the slice splicing image. In addition, only the target area is rescanned, rather than the entire area of the slice, which saves slice scanning time.
[0079] In some optional implementations, the target area rescanning module 430 includes:
[0080] a multi-layer scanning unit, configured to perform multi-layer scanning on the target area of the slice to obtain multi-layer slice rescanned images of the target area of the slice;
[0081] The clarity comparison unit is used to compare the clarity of the rescanned images of each slice layer, and determine the rescanned image of the slice with the greatest clarity as the target slice scan image of the target area of the slice.
[0082] In some optional implementations, the multi-layer scanning unit may also be specifically used for:
[0083] Performing multi-layer scanning at equal intervals on the target area of the slice to obtain multi-layer slice rescan images of the target area of the slice;
[0084] Alternatively, a multi-layer scan is performed on the target area of the slice along a direction in which the image clarity increases, to obtain a multi-layer slice rescan image of the target area of the slice.
[0085] In some optional implementations, the target area rescanning module 430 includes:
[0086] a refocusing unit, configured to refocus the target area of the slice;
[0087] The rescanning unit is configured to rescan the target area of the slice after the refocusing is completed, so as to obtain a target slice scan image of the target area of the slice.
[0088] In some optional implementations, the refocusing unit may further be specifically configured to:
[0089] Acquire a non-blank slice scanning image of the target area of the slice;
[0090] A target focus is acquired in the non-blank slice scan image, and focusing is performed based on the target focus.
[0091] In some optional implementations, the training process of the image quality assessment model includes:
[0092] A training sample acquisition module is used to acquire a slice scan sample image and a quality label corresponding to the slice scan sample image, wherein the quality label is whether the slice image is clear or the slice image is blurred;
[0093] The neural network training module is used to train the initial neural network based on the slice scan sample image and the quality label corresponding to the slice scan sample image to obtain a trained image quality assessment model.
[0094] In some optional implementations, the initial neural network is a residual neural network; accordingly, the neural network training module may also be specifically used to:
[0095] The slice scan sample image is input into the residual neural network, and the residual neural network outputs a quality prediction result. The model loss value is determined based on the quality prediction result and the quality label corresponding to the slice scan sample image. The network parameters of the residual neural network are adjusted based on the model loss value until the model training stop condition is met, thereby obtaining a trained image quality assessment model.
[0096] The digital slice splicing device provided in the embodiment of the present invention can execute the digital slice splicing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0097] Example 5
[0098] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.
[0099] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An I / O interface 15 is also connected to the bus 14.
[0100] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.
[0101] The processor 11 may be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the digital slice stitching method, which includes:
[0102] Acquire a slice scanning image of a target area of the slice;
[0103] Inputting the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice;
[0104] When the quality assessment result indicates that the slice image is blurred, rescanning the target area of the slice to obtain a target slice scan image of the target area of the slice;
[0105] The target slice scan images are stitched together to obtain a slice stitching image.
[0106] In some embodiments, the digital slice stitching method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the digital slice stitching method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the digital slice stitching method in any other suitable manner (e.g., via firmware).
[0107] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, 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), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0108] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.
[0109] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0110] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0111] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.
[0112] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.
[0113] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.
[0114] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.
Claims
1. A digital slice splicing method, characterized in that: include: Acquire a slice scanning image of a target area of the slice; Inputting the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice; When the quality assessment result indicates that the slice image is blurred, rescanning the target area of the slice to obtain a target slice scan image of the target area of the slice; The target slice scan images are stitched together to obtain a slice stitching image.
2. The method according to claim 1, characterized in that The rescanning of the target area of the slice to obtain a target slice scan image of the target area of the slice includes: Performing multi-layer scanning on the target area of the slice to obtain multi-layer slice rescan images of the target area of the slice; The clarity of the re-scanned images of each slice layer is compared, and the re-scanned image of the slice with the greatest clarity is determined as the target slice scan image of the target area of the slice.
3. The method according to claim 2, characterized in that The performing multi-layer scanning on the target area of the slice to obtain a multi-layer slice rescan image of the target area of the slice includes: Performing multi-layer scanning at equal intervals on the target area of the slice to obtain multi-layer slice rescan images of the target area of the slice; Alternatively, a multi-layer scan is performed on the target area of the slice along a direction in which the image clarity increases, to obtain a multi-layer slice rescan image of the target area of the slice.
4. The method according to claim 1, wherein The rescanning of the target area of the slice to obtain a target slice scan image of the target area of the slice includes: refocusing the target area of the slice; When the refocusing is completed, the target area of the slice is rescanned to obtain a target slice scan image of the target area of the slice.
5. The method according to claim 4, characterized in that The refocusing of the target area of the slice comprises: Acquire a non-blank slice scanning image of the target area of the slice; A target focus is acquired in the non-blank slice scan image, and focusing is performed based on the target focus.
6. The method according to claim 1, wherein The training steps of the image quality assessment model include: Acquire a slice scan sample image and a quality label corresponding to the slice scan sample image, wherein the quality label indicates whether the slice image is clear or blurred; Based on the slice scan sample images and the quality labels corresponding to the slice scan sample images, an initial neural network is trained to obtain a trained image quality assessment model.
7. The method according to claim 6, characterized in that The initial neural network is a residual neural network; Accordingly, the initial neural network is trained based on the slice scan sample image and the quality label corresponding to the slice scan sample image to obtain a trained image quality assessment model, including: The slice scan sample image is input into the residual neural network, and the residual neural network outputs a quality prediction result. The model loss value is determined based on the quality prediction result and the quality label corresponding to the slice scan sample image. The network parameters of the residual neural network are adjusted based on the model loss value until the model training stop condition is met, thereby obtaining a trained image quality assessment model.
8. A digital slice splicing device, characterized in that: include: A target area image acquisition module is used to acquire a slice scanning image of a target area of a slice; An image quality assessment module is used to input the slice scan image of the target area of the slice into a pre-trained image quality assessment model to obtain a quality assessment result corresponding to the slice scan image of the target area of the slice; a target area rescanning module, configured to rescan the target area of the slice to obtain a target slice scan image of the target area of the slice when the quality assessment result indicates that the slice image is blurred; The scanning image stitching module is used to stitch the target slice scanning images to obtain a slice stitching image.
9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the digital slice stitching method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the digital slice stitching method according to any one of claims 1 to 7 when executed.