Image processing method for equipment first-aid repair under field condition, server and processor

Abstract

The invention is suitable for the technical field of image processing, and provides an image processing method for equipment repair under field conditions, a server and a processor. The method comprises the following steps: receiving a compressed video image sent by portable maintenance auxiliary equipment, and decoding the compressed video image to obtain a decoded video image; identifying a main focus area image and a defocus area image of the decoded video image; performing image fusion based on the main focusing area image and the defocusing area image to obtain a fused video image; and performing enhancement processing on the fused video image to obtain a final enhanced image. According to the method provided by the invention, the video image acquired on site can be processed, and a more real-time and clearer image is provided for remote technical support.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and more particularly to an image processing method, a server, and a processor for emergency repair of equipment under field conditions. Background Art

[0002] When using portable maintenance auxiliary equipment to repair equipment, problems that cannot be solved during the maintenance process at the maintenance site require remote technical guidance from rear experts based on video images from the maintenance site. This requires that the video of the maintenance site be transmitted to the equipment maintenance information center.

[0003] However, in the field, the equipment at the equipment maintenance site has off-road mobility and is not fixed in position, so the maintenance site is random and widely distributed. In addition, the space inside the equipment cabin at the maintenance site is small, the depth of field is complex, and the lighting conditions are insufficient. In the field, the wireless communication network channel is limited and the amount of data transmitted is limited, resulting in unstable video image quality. The video transmitted to the equipment maintenance information center is not clear enough and cannot provide better maintenance suggestions.

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

[0005] In order to overcome the problems existing in the related technology, the embodiment of the present application provides an image processing method, a server and a processor for emergency repair of equipment under field conditions, which can process the video images collected on-site and provide more real-time and clear images for remote technical support.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides an image processing method for emergency repair of equipment under field conditions, which is applied to an equipment maintenance information center, including:

[0008] receiving a compressed video image sent by the portable maintenance auxiliary device, decoding the compressed video image, and obtaining a decoded video image;

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

[0010] Perform image fusion based on the main focus area image and the defocus area image to obtain a fused video image;

[0011] The fused video image is enhanced to obtain the final enhanced image.

[0012] In a possible implementation manner of the first aspect, performing image fusion based on the main focus area image and the defocus area image to obtain a fused video image includes:

[0013] The main focus area image and the defocus area image are processed using a deep convolutional neural network to obtain a primary processed image;

[0014] Morphological filtering and small block filtering are used to optimize the primary processed image to obtain the secondary processed image;

[0015] The secondary processed images are spatially weighted averaged and fused to obtain a fused video image.

[0016] In a possible implementation of the first aspect, performing enhancement processing on the fused video image includes:

[0017] The fused video image is enhanced by an image enhancement method based on the perceptual excitation model and gradient attenuation.

[0018] In a possible implementation of the first aspect, performing enhancement processing on the fused video image based on the image enhancement method based on the perceptual excitation model and the gradient attenuation includes:

[0019] Convert the pixel space of the fused video image into the XYZ brightness space to obtain a single-channel brightness image;

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

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

[0022] The color of the single-channel enhanced image is restored through the image color saturation control function to obtain the final enhanced image.

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

[0024] The final enhanced image is stored and sent to a remote maintenance information center.

[0025] In a second aspect, an embodiment of the present application provides an image processing method for emergency repair of equipment under field conditions, which is applied to a portable maintenance auxiliary device, including:

[0026] Collect video images of the maintenance site;

[0027] Compressing the video image of the maintenance site to obtain a compressed video image;

[0028] The compressed video image is sent to the equipment maintenance information center.

[0029] In a possible implementation manner of the second aspect, compressing a video image of a maintenance site to obtain a compressed video image includes:

[0030] Performing color channel separation on the video image of the maintenance site to obtain a first intermediate image;

[0031] Perform YUV encoding on the first intermediate image to obtain a second intermediate image; the second intermediate image is a YUV-encoded image;

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

[0033] In a possible implementation manner of the second aspect, determining a compressed video image based on the second intermediate image using a differential pulse code modulation technique includes:

[0034] Determine predicted values ​​of Y, U, and V channels of the second intermediate image by using differential pulse code modulation technology to perform 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] According to 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] In a third aspect, an embodiment of the present application provides a server that executes an image processing method for emergency repair of equipment under field conditions as described in the first aspect.

[0039] In a fourth aspect, an embodiment of the present application provides a processor that executes an image processing method for emergency repair of equipment under field conditions as described in the second aspect.

[0040] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0041] In the embodiment of the present application, considering the limitation of wireless communication network channels under field conditions, the video image is first compressed to reduce the amount of transmitted data and improve transmission efficiency, and the decoding process ensures that the compressed image is not distorted during transmission. In order to cope with the unstable video image quality caused by the small space in the equipment cabin, complex depth of field, insufficient lighting conditions, etc. under field conditions, by identifying the main focus area image and the defocus area image, and then performing image fusion, it is helpful 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, and improving the readability of the image in complex environments, which helps to more reasonably allocate and utilize limited computing resources under field conditions, and ensure the stability and reliability of image processing.

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

[0043] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

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

[0046] Figure 2 is a schematic diagram of the interaction between a portable maintenance auxiliary device and an equipment maintenance information center under field conditions provided by an embodiment of the present application;

[0047] Figure 3 It is a schematic diagram of a portable maintenance auxiliary device provided by an embodiment of the present application communicating with a device maintenance information center via a 4G wireless communication network;

[0048] Figure 4 It is a schematic diagram of the process of image compression processing provided by an embodiment of the present application;

[0049] Figure 5 This is a flow chart of further processing of a compressed image by a device maintenance information center end provided in an embodiment of the present application;

[0050] Figure 6 is a schematic diagram of the process of image fusion provided by an embodiment of the present application;

[0051] Figure 7 It is a schematic diagram of a process for enhancing a video image provided by an embodiment of the present application;

[0052] Figure 8 It is a schematic diagram of the structure of a controller provided in one embodiment of the present application. DETAILED DESCRIPTION

[0053] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

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

[0055] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0056] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0057] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0058] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0059] Figure 1 This is a schematic diagram of an application scenario of an image processing method for equipment emergency repair under field conditions provided by an embodiment of the present application, referring to Figure 1 In response to the audio and video acquisition and transmission needs of equipment maintenance sites under field conditions, portable maintenance equipment is used to collect and transmit video of the maintenance site. Portable maintenance equipment can be used to perform real-time maintenance on field equipment with long-range precision strike capabilities and autonomous navigation and guidance functions. The video of the maintenance site is connected to the Internet through a 4G wireless network base station, so as to quickly access the equipment maintenance information center, store audio and video images in the server of the maintenance information center, and can also interact with the remote maintenance information center to provide remote expert technical support for on-site maintenance personnel.

[0060] Compared with the maintenance site at the rear base, the equipment at the equipment maintenance site under field conditions has better off-road mobility and is not fixed in position, so the maintenance site is random and widely distributed. Under field conditions, the location of the equipment maintenance site is remote, there are no available wired network resources, and it is far away from the maintenance center, making it difficult to lay network cables. Even if network cables are laid, it will cost high costs and maintenance fees. Portable auxiliary maintenance equipment needs to be moved inside the launch vehicle according to the electronic equipment being repaired, and will also be carried outside the vehicle by the inspection and maintenance personnel, so the image transmission system integrated in the portable auxiliary maintenance equipment is required to be flexible and easy to carry, so as to facilitate mobile monitoring.

[0061] In the above application scenarios, the space in the equipment cabin at the maintenance site is small, the depth of field is complex, the lighting conditions are insufficient, the wireless communication network channels are limited, the quality of the obtained video images is unstable, and it is difficult to obtain clear images. Therefore, in order to solve the technical problems of image transmission and image processing at the above equipment maintenance site under field conditions, the present invention provides an image processing method for equipment emergency repair under field conditions.

[0062] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0063] Figure 2 is a schematic diagram of the interaction between a portable maintenance auxiliary device and an equipment maintenance information center under field conditions provided by an embodiment of the present application, with reference to Figure 2 , the image processing method for equipment repair under field conditions is described in detail as follows:

[0064] The present application embodiment provides an image processing method for emergency repair of equipment under field conditions, which is applied to a portable maintenance auxiliary device, including:

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

[0066] Exemplarily, the 4G wireless communication network at the equipment 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] The portable maintenance auxiliary equipment at the equipment maintenance site communicates with the equipment maintenance information center through the 4G wireless communication network as shown in the figure Figure 3 As shown. The portable maintenance auxiliary equipment with 4G wireless communication function is the front-end equipment of the entire network system. The 4G wireless communication module is based on an embedded processor and is composed of a camera module and a 4G module. It has the characteristics of simple hardware circuit, high cost performance, and convenient mobile monitoring. It mainly completes image acquisition, image compression and 4G wireless transmission. The use of portable maintenance auxiliary equipment to collect video images during the maintenance process at the equipment maintenance site not only avoids the use and constraints of wired cables, but also gives full play to the advantages of wireless networks. Maintenance personnel can carry portable maintenance equipment for mobile monitoring, which ensures the flexibility of image acquisition at the maintenance site and greatly saves resources.

[0068] Step 102: compress the video image of the maintenance site to obtain a compressed video image.

[0069] For example, since the front end of the portable maintenance assistance device uses an embedded processor, it is limited in computing and storage capabilities. At the same time, the 4G wireless network signal is weak under field conditions and the stable transmission of data is easily disturbed. Therefore, the video images collected at the maintenance site are compressed on the portable maintenance assistance device to effectively reduce the size of the video image file, thereby saving storage space, enabling the device to store more video image data and reducing the transmission bandwidth requirement for video images. Even in the case of weak signals, the data transmission efficiency and stability can be improved, and more computing and storage resources can be freed up to support other functions, such as real-time video analysis, image recognition, etc., thereby further improving the practicality and intelligence 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 be connected to the 4G cloud server through a 4G wireless network base station, thereby quickly accessing the equipment maintenance information center, storing video images in the server of the equipment maintenance information center, and interacting with the remote maintenance information center. Since the 4G wireless network signal has a wide coverage area and is not limited by remote areas in the wild and the maintenance distance range, the maintenance information network can be accessed at any location on the maintenance site, meeting the needs of image transmission on 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 transmitted data 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 the video image of the maintenance site is specifically introduced, and step 102 includes:

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

[0075] Step 1022, YUV-encode the first intermediate image to obtain a second intermediate image; the second intermediate image is a YUV-encoded image.

[0076] Step 1023, based on the second intermediate image, using differential pulse code modulation technology, determine the compressed video image.

[0077] Exemplarily, step 1023 specifically includes:

[0078] The portable maintenance auxiliary device uses differential pulse code modulation technology to determine the predicted values ​​of the Y, U, and V channels of the second intermediate image for predictive coding. The portable maintenance auxiliary device calculates 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. According to the calculated absolute image 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 coding method based on the YUV space performs image compression processing on the video image to be compressed collected at the maintenance site, such as Figure 4As shown, first, step 1021 separates the color channels of the captured image, step 1022 selects the YUV coding sampling method according to the pixel value distribution structure of each channel to complete the coding of the three components Y, U, and V, and step 1023 then uses DPCM coding to perform initial prediction of the quantization order m=1 for the three channel values, and corrects the quantization order according to the image entropy IE of the absolute residual of the image. If IE>2, the image is compressed, otherwise the quantization order m=m+1 is increased, that is, the image entropy of the image residual is small, indicating that the current quantization order may not be sufficient to accurately represent the image details, so it is necessary to increase the quantization order to improve the coding accuracy until the image effect meets the requirements, determine the final quantization order, and complete the whole process of image compression.

[0080] See also Figure 2 and Figure 5 After the image is compressed at the front end of the portable maintenance auxiliary device, the equipment maintenance information center further processes the compressed image. The wired local area network of the equipment maintenance information center at the rear connects multiple communication servers, management servers, data storage servers and application servers. Through the communication server, the maintenance information center can receive real-time maintenance process video images of the maintenance site obtained 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 of the information center, improve the efficiency of maintenance plan formulation, and facilitate maintenance task management. The application server can be used to decode video images, image fusion and other processing. The management server can schedule and manage multiple tasks of the maintenance information center. The embodiment of the present application provides an image processing method for equipment emergency repair under field conditions, which is applied to the equipment maintenance information center, including:

[0081] Step 201: receiving a compressed video image sent by a portable maintenance auxiliary device, and decoding the compressed video image to obtain a decoded video image.

[0082] Exemplarily, the portable maintenance auxiliary device transmits the compressed video image to the remote server of the maintenance information center through the TCP protocol. The remote server application completes the reception, image decoding and format conversion of the video image.

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

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

[0085] Since the inspection and maintenance of equipment are mainly carried out in the equipment cabin, the space inside the cabin is small, the lighting conditions are poor, and the video images obtained at the maintenance site are not clear enough due to the limitation of the camera hardware of the maintenance auxiliary equipment. At the same time, due to the influence of the lens focus depth, the non-focus area of ​​the image is blurred and the image information cannot be recognized, which seriously affects the observation and guidance of the remote experts on the maintenance site. Therefore, the received video images are fused in the maintenance information center to obtain clear video images of the maintenance site.

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

[0087] For example, in order to cope with the unstable and uneven video image quality caused by the small space in the equipment cabin, complex depth of field, insufficient lighting conditions, etc. under field conditions, identifying the main focus area image and the defocused area image and then performing image fusion can help 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, and improve the readability of the image in complex environments. It helps to more reasonably allocate and utilize limited computing resources under field conditions, ensuring the stability and reliability of image processing.

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

[0089] Step 2031, using a deep convolutional neural network to process the main focus area image and the defocus area image to obtain a primary processed image.

[0090] Exemplarily, a deep convolutional neural network can be first trained with high-quality image blocks and blur blocks, and then the main focus area and the defocused area can be quickly and preliminarily located and divided through the trained deep convolutional neural network. Generally, the main focus area image is the near focus area image, and the defocused area is the far focus area image. Among them, the clear part of the main focus area image can be extracted by the first deep convolutional neural network trained for the main focus area, and the clear part of the defocused area image can be extracted by the second deep convolutional neural network trained for the defocused area.

[0091] Step 2032, morphological filtering and small block filtering are used to optimize the primary processed image to obtain a secondary processed image.

[0092] Exemplarily, consistency detection means such as morphological filtering and small block filtering are used to optimize the primary processed image as the initial decision graph to obtain the secondary processed image.

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

[0094] Exemplarily, the spatial weighted average fusion is a pixel-level weighted average fusion.

[0095] In this embodiment, the first-level processing uses DCNN to achieve fast and accurate area division, and 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 small space, complex depth of field, and insufficient lighting conditions in the equipment cabin 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, in step 204, the fused video image is enhanced, including:

[0097] The fused video image is enhanced by an image enhancement method based on the perceptual excitation model and gradient attenuation.

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

[0099] The pixel space of the fused video image is converted to the XYZ brightness space to obtain a single-channel brightness image.

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

[0101] The relative brightness map is adaptively attenuated in the image brightness gradient domain, and reconstructed after attenuation to obtain a single-channel enhanced image. The adaptive attenuation is performed in the image brightness gradient domain to ensure that the detail information is not obliterated.

[0102] The color of the single-channel enhanced image is restored through the image color saturation control function to obtain the final enhanced image.

[0103] In this embodiment, the fused image is enhanced by an image enhancement method based on a perceptual excitation model and gradient attenuation to obtain a final image, thereby further solving the problem that the space in the equipment cabin is narrow, there are many obstructions, and the limited light source in the vehicle cannot illuminate the entire dim environment in the cabin, resulting in unstable illumination distribution of the captured image, which may be a low-illuminance image with poor image quality.

[0104] In one embodiment, an image processing method for emergency repair of equipment 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 contact the remote maintenance center and remote experts through the Internet. The network is based on Web technology. The network structure at this level adopts the browser / server (H / S) architecture. The remote maintenance information center includes a video image server, and the PC and mobile terminal of the remote expert are used as clients. They can view the real-time maintenance process at the maintenance site and give maintenance expert opinions, and provide remote guidance on maintenance.

[0106] It can be seen that the image processing method for equipment emergency repair under field conditions proposed by the present invention takes into account the limitations of wireless communication network channels under field conditions. First, the video image is compressed to reduce the amount of transmitted data and improve transmission efficiency. The decoding process ensures that the compressed image is not distorted during transmission. In order to cope with the unstable and uneven quality of video images caused by the small space in the equipment cabin, complex depth of field, insufficient lighting conditions, etc. under field conditions, by identifying the main focus area image and the defocused area image, and then performing image fusion, it is helpful 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, and improving the readability of the image in complex environments. It is helpful to more reasonably allocate and utilize limited computing resources under field conditions, and ensure the stability and reliability of image processing.

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

[0108] One embodiment provides a processor that executes an image processing method for emergency repair of equipment under field conditions applied to a portable maintenance auxiliary device as in the above-mentioned embodiment.

[0109] The present application also provides a controller, see 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 be run on the at least one processor 710, and when the processor 710 executes the computer program, the steps in any of the above method embodiments are implemented.

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

[0111] Those skilled in the art will understand that Figure 8 These are merely examples of controllers and do not constitute a limitation of the controller, which may include more or fewer components than those shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0112] The processor 710 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0113] The memory 720 may be an internal storage unit of the controller or an external storage device of the controller, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 720 is used to store computer programs and other programs and data required by the server and the processor. The memory 720 may also be used to temporarily store data that has been output or is to be output.

[0114] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

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

[0116] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0117] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An image processing method for equipment repair in the field, applied to equipment maintenance information center, characterized in that: include: receiving a compressed video image sent by the portable maintenance auxiliary device, and decoding the compressed video image to obtain a decoded video image; identifying a main focus area image and a defocus area image of the decoded video image; Performing image fusion based on the main focus area image and the defocus area image to obtain a fused video image; The fused video image is enhanced to obtain a final enhanced image.

2. The image processing method for equipment repair in the field as claimed in claim 1, characterized in that: The performing image fusion based on the main focus area image and the defocus area image to obtain a fused video image includes: Processing the main focus area image and the defocus area image using a deep convolutional neural network to obtain a primary processed image; Optimizing the primary processed image by using morphological filtering and small block filtering to obtain a secondary processed image; The secondary processed images are spatially weighted averaged and fused to obtain the fused video image.

3. The image processing method for equipment emergency repair in the field as claimed in claim 1, characterized in that: The step of performing enhancement processing on the fused video image comprises: The fused video image is enhanced by an image enhancement method based on a perceptual excitation model and gradient attenuation.

4. The image processing method for equipment repair in the field as claimed in claim 3, characterized in that: The image enhancement method based on the perceptual excitation model and gradient attenuation performs enhancement processing on the fused video image, including: Convert the pixel space of the fused video image into an XYZ brightness space to obtain a single-channel brightness image; Mapping the image brightness of the channel brightness image to the entire brightness domain through a perceptual excitation model to obtain a relative brightness map; The relative brightness map is subjected to brightness adaptive attenuation in the image brightness gradient domain, and is reconstructed after attenuation to obtain a single-channel enhanced image; The color of the single-channel enhanced image is restored by an image color saturation control function to obtain a final enhanced image.

5. The image processing method for equipment repair in the field as claimed in claim 1, characterized in that: Also includes: The final enhanced image is stored and sent to a remote maintenance information center.

6. An image processing method for equipment repair in the field, characterized in that: Applicable to portable maintenance aids, including: Collect video images of the maintenance site; Compressing the video image of the maintenance site to obtain a compressed video image; The compressed video image is sent to a device maintenance information center.

7. The image processing method for equipment repair in the field as claimed in claim 6, characterized in that: The step of compressing the video image of the maintenance site to obtain a compressed video image includes: Performing color channel separation on the video image of the maintenance site to obtain a first intermediate image; Performing YUV encoding on the first intermediate image to obtain a second intermediate image; the second intermediate image is a YUV-encoded image; Based on the second intermediate image, the compressed video image is determined using differential pulse code modulation technology.

8. The image processing method for equipment repair in the field as claimed in claim 7, characterized in that: The step of determining the compressed video image based on the second intermediate image by using a differential pulse code modulation technique includes: Determine predicted values ​​of Y, U, and V channels of the second intermediate image by using differential pulse code modulation technology to perform predictive coding; Calculating an absolute image residual between a current pixel value and a predicted value; the absolute image residual is used to encode the current pixel value; Dynamically adjusting the quantization order of the current pixel value according to the calculated absolute residual of the image to determine a final quantization order; Based on the final quantization order, the corresponding compressed video image is determined.

9. A server, characterized in that: Execution includes the image processing method for emergency repair of equipment under field conditions as described in any one of claims 1 to 5.

10. A processor, characterized in that: Execution includes the image processing method for emergency repair of equipment under field conditions as described in any one of claims 6 to 8.

Images