Image processing method for equipment repair in field conditions, server and processor

Abstract

The application is suitable for the field of image processing, and provides an image processing method and a server and a processor for equipment repair under field conditions. The method comprises the following steps: receiving compressed video images sent by a portable maintenance auxiliary device, decoding the compressed video images to obtain decoded video images; identifying a main focus area image and a defocus area image of the decoded video images; performing image fusion based on the main focus area image and the defocus area image to obtain fused video images; and performing enhancement processing on the fused video images to obtain final enhanced images. The method can process the video images collected on site, and provide more real-time and clear images for remote technical support.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image processing method, server, and processor for emergency equipment repair under field conditions. Background Technology

[0002] When using portable maintenance auxiliary equipment for emergency repairs, problems that cannot be resolved on-site require remote technical guidance from experts based on video images from the repair site. This necessitates transmitting the video footage from the repair site to the equipment maintenance information center.

[0003] However, in the field, the equipment at the maintenance site is mobile and its location is not fixed, resulting in a random and widely distributed maintenance site. Furthermore, the equipment compartments at the maintenance site are confined, have complex depth of field, and insufficient lighting. In the field, wireless communication network channels are limited, resulting in limited data transmission capacity and unstable video image quality. The video transmitted to the equipment maintenance information center is not clear enough to provide better maintenance suggestions.

[0004] Therefore, how to process the video images captured on-site during image transmission to provide more real-time and clearer images for remote technical support is a problem that needs to be solved. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this application provides an image processing method, server and processor for equipment emergency repair under field conditions, which can process video images collected on site and provide more real-time and clearer images for remote technical support.

[0006] This application is achieved through the following technical solution:

[0007] In a first aspect, embodiments of this application provide an image processing method for emergency equipment repair under field conditions, applied in an equipment maintenance information center, including:

[0008] Receive compressed video images sent by portable maintenance auxiliary equipment, decode the compressed video images, and obtain decoded video images;

[0009] Identify the main focus area image and the defocus area image of the decoded video image;

[0010] Image fusion is performed based on the main focus area image and the defocus area image to obtain the fused video image;

[0011] The fused video images are then enhanced to obtain the final enhanced image.

[0012] In one possible implementation of the first aspect, image fusion is performed based on the main focus region image and the defocus region image to obtain a fused video image, including:

[0013] A deep convolutional neural network is used to process the main focus area image and the defocus area image to obtain a first-level processed image;

[0014] The first-stage processed image is optimized using morphological filtering and block filtering to obtain the second-stage processed image;

[0015] Spatial weighted average fusion is performed on the secondary processed images to obtain the fused video image.

[0016] In one possible implementation of the first aspect, the enhanced video image is subjected to enhancement processing, including:

[0017] An image enhancement method based on a perceptual excitation model and gradient decay is used to enhance the fused video images.

[0018] In one possible implementation of the first aspect, an image enhancement method based on a perceptual excitation model and gradient decay is used to enhance the fused video image, including:

[0019] The fused video image is converted from pixel space to XYZ luminance space to obtain a single-channel luminance image;

[0020] The image brightness of the channel brightness image is mapped to the entire brightness domain through a perceptual excitation model to obtain a relative brightness map;

[0021] The relative brightness map is adaptively attenuated in the image brightness gradient domain, and then reconstructed after attenuation to obtain a single-channel enhanced image.

[0022] The colors of the single-channel enhanced image are restored using an image color saturation control function to obtain the final enhanced image.

[0023] In one possible implementation of the first aspect, an image processing method for emergency equipment repair under field conditions further includes:

[0024] Store the final enhanced image and send it to the remote maintenance information center.

[0025] Secondly, embodiments of this application provide an image processing method for emergency equipment repair under field conditions, applied to portable maintenance auxiliary equipment, including:

[0026] Acquire video images of the maintenance site;

[0027] The video images from the repair site are compressed to obtain the compressed video images;

[0028] The compressed video images are sent to the equipment maintenance information center.

[0029] In one possible implementation of the second aspect, the video images of the maintenance site are compressed to obtain compressed video images, including:

[0030] Color channels are separated from the video images at the maintenance site to obtain the first intermediate image;

[0031] The first intermediate image is YUV encoded to obtain the second intermediate image; the second intermediate image is the YUV encoded image.

[0032] Based on the second intermediate image, the compressed video image is determined using differential pulse code modulation technology.

[0033] In one possible implementation of the second aspect, based on the second intermediate image, a compressed video image is determined using differential pulse code modulation (CPCM) technology, including:

[0034] Differential pulse code modulation technique is used to determine the predicted values ​​of the Y, U, and V channels of the second intermediate image for predictive coding;

[0035] Calculate the absolute image residual between the current pixel value and the predicted value; the absolute image residual is used to encode the current pixel value.

[0036] Based on the calculated absolute residual of the image, the quantization order of the current pixel value is dynamically adjusted to determine the final quantization order.

[0037] Based on the final quantization order, the corresponding compressed video image is determined.

[0038] Thirdly, embodiments of this application provide a server that executes an image processing method for emergency equipment repair under field conditions, as described in the first aspect.

[0039] Fourthly, embodiments of this application provide a processor that executes an image processing method for emergency equipment repair under field conditions, as described in the second aspect.

[0040] The beneficial effects of the embodiments in this application compared with the prior art are:

[0041] In this embodiment, considering the limitations of wireless communication network channels in outdoor conditions, video images are first compressed to reduce the amount of data transmitted and improve transmission efficiency. The decoding process ensures that the compressed image is not distorted during transmission. To address the unstable video image quality caused by the confined space, complex depth of field, and insufficient lighting conditions inside the equipment cabin in outdoor environments, the main focus area image and the defocused area image are identified, followed by image fusion. This helps to integrate the clear parts of the image and reduce blurred and out-of-focus areas. Further enhancement processing can improve the contrast, brightness, and color saturation of the image, making the image clearer and more vivid, improving the readability of the image in complex environments, and helping to allocate and utilize limited computing resources more rationally in outdoor conditions, ensuring the stability and reliability of image processing.

[0042] The beneficial effects of the third aspect mentioned above can be found in the relevant description in the first aspect mentioned above, and the beneficial effects of the fourth aspect mentioned above can be found in the relevant description in the second aspect mentioned above, and will not be repeated here.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram illustrating an application scenario of the image processing method for emergency equipment repair under field conditions provided in an embodiment of this application;

[0046] Figure 2 This is a schematic diagram illustrating the interaction between a portable maintenance auxiliary device and an equipment maintenance information center under field conditions, provided in one embodiment of this application.

[0047] Figure 3 This is a schematic diagram illustrating the communication between a portable maintenance auxiliary device provided in one embodiment of this application and an equipment maintenance information center via a 4G wireless communication network;

[0048] Figure 4 This is a schematic diagram of an image compression process provided in an embodiment of this application;

[0049] Figure 5 This is a schematic diagram of the process of further processing compressed images at the equipment maintenance information center terminal according to an embodiment of this application;

[0050] Figure 6 This is a schematic diagram of an image fusion process provided in an embodiment of this application;

[0051] Figure 7 This is a schematic diagram of a video image enhancement process provided in an embodiment of this application;

[0052] Figure 8 This is a schematic diagram of the controller provided in one embodiment of this application. Detailed Implementation

[0053] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0054] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0055] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0056] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0057] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0058] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0059] Figure 1 This is a schematic diagram illustrating an application scenario of an image processing method for emergency equipment repair under field conditions, as provided in an embodiment of this application. (Refer to...) Figure 1 To address the need for audio and video acquisition and transmission at equipment maintenance sites in field conditions, portable maintenance equipment is used to capture and transmit video footage from the maintenance site. This portable equipment can be used for real-time on-site maintenance of equipment with long-range precision strike capabilities and autonomous navigation and guidance functions. The video from the maintenance site is connected to the internet via a 4G wireless network base station, allowing for rapid access to the equipment maintenance information center. The audio and video images are stored on the information center's server, and the equipment can also interact with a remote maintenance information center, providing remote expert technical support to on-site maintenance personnel.

[0060] Compared to maintenance sites at rear bases, equipment maintenance sites in the field offer greater off-road mobility and are not fixed in location, resulting in a more random and widely distributed maintenance environment. In the field, maintenance sites are often remote, lacking available wired network resources, and far from maintenance centers, making network cabling difficult and costly, even if implemented, incurring high maintenance expenses. Portable auxiliary maintenance equipment needs to be moved within the launch vehicle and carried by maintenance personnel, requiring an image transmission system integrated into the portable auxiliary maintenance equipment that is flexible, portable, and facilitates mobile monitoring.

[0061] In the above application scenarios, the space inside the equipment compartment at the maintenance site is small, the depth of field is complex, the lighting conditions are insufficient, and the wireless communication network channels are limited, resulting in unstable video image quality and difficulty in obtaining clear images. Therefore, in order to solve the technical problems of image transmission and image processing at the above-mentioned equipment maintenance site under field conditions, this invention provides an image processing method for emergency equipment repair under field conditions.

[0062] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] Figure 2 This is a schematic diagram illustrating the interaction between a portable maintenance auxiliary device and an equipment maintenance information center under field conditions, provided in one embodiment of this application. (Refer to...) Figure 2 The image processing method for emergency equipment repair under field conditions is described in detail below:

[0064] This application provides an image processing method for emergency equipment repair under field conditions, applied to portable maintenance auxiliary equipment, including:

[0065] Step 101: Collect video images of the maintenance site.

[0066] For example, the 4G wireless communication network on-site serves as the front end of the image transmission system, avoiding the use and constraints of wired cables and increasing the flexibility and scalability of the image acquisition and transmission system.

[0067] A diagram illustrating the communication between portable maintenance auxiliary equipment at the equipment maintenance site and the equipment maintenance information center via a 4G wireless communication network is shown below. Figure 3 As shown, the portable maintenance auxiliary device with 4G wireless communication capability is the front-end device of the entire network system. The 4G wireless communication module, with an embedded processor at its core, consists of a camera module and a 4G module, featuring simple hardware circuitry, high cost-effectiveness, and ease of mobile monitoring. It primarily performs image acquisition, image compression, and 4G wireless transmission. Using this portable maintenance auxiliary device to acquire video images during the equipment maintenance process at the site avoids the use and constraints of wired cables, fully utilizes the advantages of wireless networks, and allows maintenance personnel to carry the portable device for mobile monitoring, ensuring flexibility in image acquisition at the maintenance site and significantly saving resources.

[0068] Step 102: Compress the video images from the maintenance site to obtain compressed video images.

[0069] For example, since portable maintenance auxiliary equipment uses an embedded processor at the front end, its computing and storage capabilities are limited. At the same time, the 4G wireless network signal is weak in the field, and the stable transmission of data is easily interfered with. Therefore, the portable maintenance auxiliary equipment performs image compression processing on the video images collected at the maintenance site, which effectively reduces the size of the video image files, thereby saving storage space. This allows the equipment to store more video image data, reduces the bandwidth requirements for video image transmission, and improves data transmission efficiency and stability even in the case of weak signals. It also frees up more computing and storage resources to support other functions, such as real-time video analysis and image recognition, thereby further improving the practicality and intelligence level of the equipment.

[0070] Step 103: Send the compressed video image to the equipment maintenance information center.

[0071] For example, all portable maintenance auxiliary devices based on wireless networks can connect to a 4G cloud server via a 4G wireless network base station, thereby quickly accessing the equipment maintenance information center. Video images are stored on the server in the equipment maintenance information center, and interaction with the remote maintenance information center is also possible. Because 4G wireless network signals have wide coverage and are not limited by remote areas or maintenance distances, they can access the maintenance information network from any location on the maintenance site, meeting the needs for image transmission at the equipment maintenance site under field conditions.

[0072] This embodiment takes into account the limitations of wireless communication network channels under field conditions. First, the video image is compressed to reduce the amount of data transmitted and improve transmission efficiency. The decoding process ensures that the compressed image is not distorted during transmission.

[0073] In one embodiment, the process of compressing video images at the maintenance site is specifically described, and step 102 includes:

[0074] Step 1021: Perform color channel separation on the video image of the maintenance site to obtain the first intermediate image.

[0075] Step 1022: Perform YUV encoding on the first intermediate image to obtain the second intermediate image; the second intermediate image is the YUV encoded image.

[0076] Step 1023: Based on the second intermediate image, the compressed video image is determined using differential pulse code modulation technology.

[0077] For example, step 1023 specifically includes:

[0078] The portable maintenance auxiliary equipment uses differential pulse code modulation (DCM) to determine the predicted values ​​of the Y, U, and V channels of the second intermediate image for predictive coding. The portable maintenance auxiliary equipment calculates the image absolute residual between the current pixel value and the predicted value; this image absolute residual is used to encode the current pixel value. Based on the calculated image absolute residual, the quantization order of the current pixel value is dynamically adjusted to determine the final quantization order. Based on the final quantization order, the corresponding compressed video image is determined.

[0079] In this embodiment, the DPCM compression encoding method based on the YUV space is used to compress video images to be compressed at the maintenance site, such as... Figure 4As shown, firstly, step 1021 separates the color channels of the acquired image. Secondly, step 1022 selects the YUV encoding sampling method according to the pixel value distribution structure of each channel to complete the encoding of the three components Y, U, and V. Thirdly, step 1023 uses DPCM encoding to perform initial prediction of the quantization order m=1 for the three channel values. The quantization order is corrected according to the image entropy IE of the absolute residual of the image. If IE>2, the image compression is completed. Otherwise, the quantization order m=m+1 is increased. That is, if the image entropy of the image residual is small, it means that the current quantization order may not be sufficient to accurately represent the image details. Therefore, it is necessary to increase the quantization order to improve the encoding accuracy until the image effect meets the requirements. Finally, the final quantization order is determined, and the entire process of image compression is completed.

[0080] See Figure 2 and Figure 5 After the image is compressed at the front end of the portable maintenance auxiliary equipment, the equipment maintenance information center further processes the compressed image. The wired local area network of the equipment maintenance information center connects to multiple communication servers, management servers, data storage servers, and application servers. Through the communication servers, the maintenance information center can receive real-time video images of the maintenance process at the maintenance site acquired by multiple portable maintenance auxiliary devices and can forward video images to multiple remote expert clients. The data storage server can build a database of massive maintenance data for the information center, improving the efficiency of maintenance plan formulation and facilitating maintenance task management. The application server can be used to decode video images, perform image fusion, and other processing. The management server can schedule and manage multiple tasks of the maintenance information center. This application embodiment provides an image processing method for emergency equipment repair under field conditions, applied to an equipment maintenance information center, including:

[0081] Step 201: Receive the compressed video image sent by the portable maintenance auxiliary equipment, decode the compressed video image, and obtain the decoded video image.

[0082] For example, portable maintenance auxiliary equipment transmits compressed video images to a remote server in the maintenance information center via the TCP protocol. The remote server application handles the reception, decoding, and format conversion of the video images.

[0083] Step 202: Identify the main focus area image and the defocus area image of the decoded video image.

[0084] Step 203: Perform image fusion based on the main focus area image and the defocus area image to obtain the fused video image.

[0085] Since equipment testing and maintenance are primarily conducted inside the equipment compartment, which is confined by limited space and poor lighting, and further hampered by hardware limitations of the maintenance auxiliary equipment's cameras, the video images acquired at the maintenance site lack clarity. Additionally, the depth of focus of the lenses affects the clarity of out-of-focus areas, making image information unrecognizable and severely impacting remote experts' observation and guidance of the maintenance site. Therefore, the maintenance information center fuses the received video images to obtain clear video images of the maintenance site.

[0086] Step 204: Enhance the fused video image to obtain the final enhanced image.

[0087] For example, to address the unstable and uneven video image quality caused by the confined space inside the equipment cabin, complex depth of field, and insufficient lighting conditions in the field, the main focus area image and the defocused area image are identified, and then image fusion is performed. This helps to integrate the clear parts of the image and reduce the blurred and out-of-focus areas. Further enhancement processing can improve the contrast, brightness, and color saturation of the image, making the image clearer and more vivid, improving the readability of the image in complex environments, and helping to allocate and utilize limited computing resources more rationally in field conditions, ensuring the stability and reliability of image processing.

[0088] In one embodiment, the specific process of image fusion based on the main focus region image and the defocus region image is described. The image fusion method adopts a two-stage processing fusion structure, which has the characteristics of simplicity, good real-time performance, and fast fusion processing speed. Figure 6 As shown. Step 203 includes:

[0089] Step 2031: Use a deep convolutional neural network to process the main focus area image and the defocus area image to obtain a first-level processed image.

[0090] For example, a deep convolutional neural network can be trained using high-quality image patches and blurred patches. Then, the trained deep convolutional neural network can be used to quickly and initially locate and divide the main focus region and the defocused region. Generally, the main focus region image is the near-focus region image, and the defocused region image is the far-focus region image. Specifically, a first deep convolutional neural network trained for the main focus region can be used to extract the sharp parts of the main focus region image, and a second deep convolutional neural network trained for the defocused region image can be used to extract the sharp parts of the defocused region image.

[0091] Step 2032: morphological filtering and block filtering are used to optimize the first-level processed image to obtain the second-level processed image.

[0092] For example, consistency detection techniques such as morphological filtering and block filtering are used to optimize the first-level processed image, which serves as the initial decision map, to obtain the second-level processed image.

[0093] Step 2033: Perform spatial weighted average fusion on the secondary processed images to obtain the fused video image.

[0094] For example, spatial weighted average fusion is pixel-level weighted average fusion.

[0095] In this embodiment, the first-level processing utilizes DCNN to achieve fast and accurate region segmentation, while the second-level processing performs fast and effective detail enhancement on this basis. The combination of the two can significantly improve the processing efficiency of the entire image fusion process. Considering the confined space, complex depth of field, and insufficient lighting conditions inside the equipment compartment at the maintenance site, this two-level fusion method has good adaptability and practicality for different types of images and different scenes.

[0096] In one embodiment, step 204 involves enhancing the fused video image, including:

[0097] An image enhancement method based on a perceptual excitation model and gradient decay is used to enhance the fused video images.

[0098] For example, such as Figure 7 As shown, an image enhancement method based on a perceptual excitation model and gradient decay is used to enhance the fused video image, including:

[0099] The fused video image is converted from pixel space to XYZ luminance space to obtain a single-channel luminance image.

[0100] The image brightness of the channel brightness image is mapped to the entire brightness domain through a perceptual excitation model to obtain a relative brightness map.

[0101] The relative brightness map is adaptively attenuated in the image brightness gradient domain, and then reconstructed to obtain a single-channel enhanced image. The adaptive attenuation in the image brightness gradient domain ensures that detailed information is not lost.

[0102] The colors of the single-channel enhanced image are restored using an image color saturation control function to obtain the final enhanced image.

[0103] This embodiment uses an image enhancement method based on a perceptual excitation model and gradient decay to enhance the fused image, obtaining the final image. This further solves the problem that due to the narrow space inside the equipment cabin, many obstructions, and the limited light source inside the vehicle, the lighting distribution of the captured image is unstable, which may result in a low-light image and poor image quality.

[0104] In one embodiment, an image processing method for emergency equipment repair under field conditions further includes: storing the final enhanced image and sending the final enhanced image to a remote maintenance information center.

[0105] The equipment maintenance information center can connect with remote maintenance centers and remote experts via the internet. This network is based on web technology, and its architecture uses a browser / server (H / S) structure. The remote maintenance information center includes a video image server, while remote experts' PCs and mobile terminals act as clients, allowing them to view the real-time maintenance process at the site and receive expert advice, providing remote guidance for the maintenance.

[0106] As can be seen, the image processing method for equipment repair under field conditions proposed in this invention takes into account the limitations of wireless communication network channels in field conditions. First, it compresses the video image to reduce the amount of data transmitted and improve transmission efficiency. The decoding process ensures that the compressed image is not distorted during transmission. To address the unstable and uneven video image quality caused by the confined space, complex depth of field, and insufficient lighting conditions inside the equipment cabin in field conditions, the method identifies the main focus area image and the defocused area image, and then performs image fusion. This helps to integrate the clear parts of the image and reduce blurry and out-of-focus areas. Further enhancement processing can improve the contrast, brightness, and color saturation of the image, making the image clearer and more vivid, improving the readability of the image in complex environments, and helping to allocate and utilize limited computing resources more rationally under field conditions, ensuring the stability and reliability of image processing.

[0107] One embodiment provides a server that executes an image processing method for emergency equipment repair under field conditions, as described in the above embodiment, applied to an equipment maintenance information center.

[0108] One embodiment provides a processor that executes an image processing method for emergency equipment repair under field conditions, as described in the above embodiments and applied to portable maintenance auxiliary equipment.

[0109] This application also provides a controller, see [link to relevant documentation] Figure 8 The controller 700 may include at least one processor 710 and a memory 720, wherein the memory 720 stores a computer program that can run on the at least one processor 710, and the processor 710 executes the computer program to implement the steps in any of the above method embodiments.

[0110] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 720 and executed by processor 710 to complete the present application. One or more modules / units may be a series of computer program segments capable of performing a specific function, which describe the execution process of the computer program in controller 700.

[0111] Those skilled in the art will understand that Figure 8 This is merely an example of a controller and does not constitute a limitation on the controller. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0112] The processor 710 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0113] The memory 720 can be an internal storage unit of the controller or an external storage device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a Flash Card. The memory 720 is used to store computer programs, as well as other programs and data required by the server and processor. The memory 720 can also be used to temporarily store data that has been output or will be output.

[0114] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0115] The image processing method for equipment repair under field conditions provided in this application embodiment can be applied to controllers such as computers, tablets, laptops, netbooks, and personal digital assistants (PDAs). This application embodiment does not impose any restrictions on the specific type of controller.

[0116] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0117] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An image processing method for emergency equipment repair under field conditions, applied in an equipment maintenance information center, characterized in that... include: Receive compressed video images sent by portable maintenance auxiliary equipment, decode the compressed video images to obtain decoded video images; Identify the main focus area image and the defocus area image of the decoded video image; Image fusion is performed based on the main focus area image and the defocus area image to obtain the fused video image; The fused video image is then enhanced to obtain the final enhanced image; The enhancement processing of the fused video image includes: The fused video image is enhanced using an image enhancement method based on a perceptual excitation model and gradient decay. The image enhancement method based on the perceptual excitation model and gradient decay enhances the fused video image, including: The fused video image is converted from pixel space to XYZ luminance space to obtain a single-channel luminance image; The image brightness of the channel brightness image is mapped to the entire brightness domain through a perceptual excitation model to obtain a relative brightness map; The relative brightness map is subjected to adaptive brightness attenuation in the image brightness gradient domain, and then reconstructed after attenuation to obtain a single-channel enhanced image. The colors of the single-channel enhanced image are restored using an image color saturation control function to obtain the final enhanced image; The step of image fusion based on the main focus area image and the defocus area image to obtain the fused video image includes: The main focus area image and the defocus area image are processed using a deep convolutional neural network to obtain a first-level processed image; wherein, the deep convolutional neural network is used to extract the sharp parts of the main focus area image and the defocus area image; The first-level processed image is optimized using morphological filtering and block filtering to obtain the second-level processed image; The secondary processed images are spatially weighted averaged and fused to obtain the fused video image.

2. The image processing method for emergency equipment repair under field conditions as described in claim 1, characterized in that, Also includes: The final enhanced image is stored and then sent to the remote maintenance information center.

3. A server, characterized in that, The image processing method for emergency equipment repair under field conditions, as described in any one of claims 1 to 2, is executed.

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