161 results about "Infrared image processing" patented technology

An infrared image processing method, apparatus and device, and a storage medium are provided. The infrared image processing method includes: acquiring an infrared image; performing target detection on the infrared image to determine target objects within the infrared image and obtaining a target detection result that include a category of each of the target objects; determining a target object of interest from the target objects; determining a contour area of the target object of interest; performing true-color processing on the contour area to highlight the target object of interest. By highlighting the target object of interest in the infrared image through true-color processing, the target object of interest can be displayed more prominently with different colors and textures according to its category, so that the target object of interest can be presented more clearly in the infrared image clearer and easier for the human eye to observe and recognize.

The invention relates to the field of infrared image processing, in particular to an infrared blind pixel detection method based on temperature self-adaption and time-frequency domain synthesis, which comprises the following steps of: traversing an infrared image by using a sliding window to obtain gray values of pixel points around a current pixel point; carrying out image edge judgment by using a Sobel-Otsu algorithm based on working temperature dynamic adjustment, and reserving a non-image edge window to participate in blind pixel judgment; grouping pixel points in the window according to horizontal and vertical directions; calculating the alpha filtering value of the gray scale of each group of pixels; calculating the deviation between the gray value of each pixel in the window and the alpha filtering value of the corresponding row coordinate and column coordinate group; if the deviation is greater than an adaptive threshold, setting the blind pixel as a potential blind pixel, and constructing a potential blind pixel distribution matrix; a time domain accumulation method for pre-screening periodic noise based on FFT is adopted, periodic noise interference is eliminated through frequency domain analysis, and then high-probability blind elements are screened through time domain accumulation; and obtaining a final blind pixel detection result. The method can effectively cope with the working temperature change of the sensor and the noise interference in a complex environment, and remarkably improves the accuracy and anti-interference capability of blind pixel detection.

The invention provides an infrared image processing method and device, equipment and a storage medium, the method is applied to computer equipment deployed with an image processing system, and the method comprises the steps: obtaining a low-resolution infrared image, and dividing the scale type of the low-resolution infrared image through a recognition module to obtain the infrared image of each scale; performing feature extraction on the infrared image of each scale to obtain feature information of each scale; performing graphic transformation on the feature information through a fusion module to obtain high-frequency feature information and low-frequency feature information; performing feature fusion on the high-frequency feature information and the low-frequency feature information to obtain image features; the image features are restored through the sampling module to obtain the high-resolution infrared image, and the image quality of the redrawn infrared image is improved.

The invention relates to the technical field of infrared image processing, and discloses a method for removing infrared video artifacts in real time, which comprises the following steps of: selecting a target frame and N frames of infrared images before and after the target frame, and performing feature point extraction on the obtained original infrared images; carrying out feature point matching on two adjacent frames of infrared images to obtain a motion vector; obtaining an offset and an offset matrix of each pixel point in the infrared image based on the motion vector; calculating a remapping matrix of N frames of infrared images before and after the target frame and obtaining a remapping image; updating the temporary result of the final target frame and the weighting counter of each pixel; and the reconstruction result of the target frame is fused with the edge information atlas of the original infrared image to obtain a final target frame output infrared image. According to the method, N frames before and after the target frame are carried out simultaneously, when the number of the offset matrixes reaches 2N groups, the method enters the double-thread mode, the next target frame is processed simultaneously, and the method has the advantages of being high in processing efficiency and good in continuity and real-time performance.

The invention provides a non-disassembly measurement method based on the temperature rise characteristic of mining explosion-proof electrical equipment, and relates to the technical field of explosion-proof equipment image processing, and the method comprises the following steps: analyzing the heating and heat transfer of an electrical part of the explosion-proof equipment; determining the relationship between the surface temperature of the explosion-proof equipment and the temperature of internal electrical components; establishing a temperature back calculation formula of the equipment surface and the heat source, and establishing a mathematical relationship between the surface temperature and the internal heat source temperature by adopting a data fitting method through a preset number of simulation data and experimental data; installation and parameter setting of the thermal infrared imager: the thermal infrared imager is installed at a preset position, so that the thermal infrared imager can clearly capture the full view of the explosion-proof equipment; performing infrared image processing and data extraction; the implementation of non-disassembly temperature measurement specifically comprises the following steps: continuously updating surface temperature data acquired by an infrared thermal imager, and repeatedly substituting the surface temperature data into an inverse calculation formula for calculation, so as to realize real-time dynamic measurement of an internal temperature field of an electrical component, and provide a selectable path for a non-disassembly measurement method.

The invention discloses a physical omnidirectional antagonistic interference method for a 3d infrared target detection system, and belongs to the technical field of artificial intelligence safety and infrared image processing. The method comprises the following steps: firstly, carrying out three-dimensional modeling and UV texture expansion on a target vehicle, and constructing a patch based on particle information; then generating a multi-view infrared confrontation sample covering multiple groups of pitch angles, horizontal angles and observation distances by utilizing Pytorch3D rendering; further constructing a multi-target fitness function considering the minimization of the detection confidence and the minimization of the patch area, and jointly optimizing the position, the shape and the size of the patch through a self-adaptive particle swarm optimization algorithm; and finally, introducing an expectation transformation (EOT) mechanism, and simulating imaging noise and patch offset in complex environments such as rain, fog, sand and dust, so as to verify the environment robustness of the scheme. The designed flexible disturbance patch can be physically arranged on the surface of a vehicle, and full-view infrared interference detection is achieved.

The method for estimating the area of an irregular-shaped fire scene using an optoelectronic pod in the present invention belongs to the technical fields of navigation control algorithms and infrared image processing, and includes: S101: Preprocessing and background suppression are performed on the infrared thermal image, and the fire scene area in the image is identified according to the gray value of the image and the set temperature threshold; S102: Determine the slant range information of the fire scene area aligned with the optical axis of the pod; S103: Place the infrared thermal image in a rectangular coordinate system with the alignment point of the pod optical axis as the origin, and the fire scene area forms an irregular area in the rectangular coordinate system; S104: Calculate the area projected onto the actual ground for each pixel in the fire scene area according to the optical axis attitude, geographical location information, and slant range information, and add up the projected areas of each pixel to obtain the fire scene area on the actual ground. It can greatly improve the efficiency of forest fire prevention emergency rescue and the accuracy and real-time nature of fire situation reports, thereby achieving rapid analysis of forest fire situations and providing strong information support for forest fire fighting and emergency command.

The invention discloses an infrared vehicle detection adversarial patch generation method based on a particle swarm optimization algorithm, and belongs to the technical field of infrared image processing and security attack. The method comprises the following steps of: firstly, performing vehicle segmentation on an input infrared image by using an image segmentation model to obtain an accurate vehicle segmentation mask for positioning a subsequent patch; thirdly, initializing an adversarial patch according to the segmentation mask, inputting the adversarial patch into an infrared vehicle detection model (such as YOLOv5) for detection, and generating a preliminary adversarial sample; then, defining a new loss function, and optimizing the generated adversarial patch by minimizing the confidence score of a detector on the patch, so as to enhance the attack effect; and finally, iteratively optimizing the position and the shape of the patch by adopting an improved particle swarm optimization (PSO) algorithm in combination with a shape optimization module so as to maximize the attack success rate, and ensuring the robustness and the adaptability of the patch in a real environment in combination with an expectation transformation (EOT) technology. According to the method, the attack success rate of the infrared vehicle detector is remarkably improved, and an innovative and efficient solution is provided for attack resistance of the infrared vehicle detector.

The present invention belongs to the field of infrared system design technology, and in particular, relates to an infrared system, infrared imaging equipment and electronic equipment based on FPGA, including: a detector module, a dual FPGA processing module and a display module; the detector module is used to obtain infrared digital signals; the dual FPGA processing module includes an FPGA image preprocessing module and an FPGA image processing module; the FPGA image preprocessing module is used to perform image preprocessing on infrared digital signals to obtain target coded data; the FPGA image processing module is used to process the target coded data according to a preset image processing algorithm to obtain infrared image processing results; the display module is used to display infrared image processing results. The infrared system proposed in this application uses a dual FPGA chip as the main processor of the infrared system, combined with the software architecture of the high-resolution infrared system, which can achieve high-resolution infrared image processing while reducing the cost and power consumption of the infrared system.

The invention relates to the technical field of power failure prediction, and discloses a power equipment thermal failure monitoring and early warning system fused with infrared image processing. According to the invention, the infrared image acquisition module acquires and standardizes equipment temperature field data, and the electrical parameter synchronous acquisition module acquires load current and environment temperature in real time; the hot spot dynamic analysis module identifies and tracks an overheat area based on a dynamic threshold value, extracts spatio-temporal evolution characteristics of the overheat area, and comprehensively calculates a fault hot spot intensity factor capable of representing an early fault; the electrical fault diagnosis module starts deep diagnosis when the factor is abnormal, and accurately estimates the contact resistance value of the key part through a nonlinear recursive algorithm; and the comprehensive early warning module performs fusion evaluation on the multi-source information, and generates multi-level early warning information based on a layering strategy. According to the method, post-event alarm is converted into beforehand intervention, the sudden failure risk of the equipment is remarkably reduced, and accurate and reliable decision support is provided for predictive maintenance.

A system for monitoring a switchgear includes: an infrared camera for acquiring at least one infrared image of the switchgear; a processing unit for determining a pixel in the at least one infrared image associated with a hottest temperature, determining e pixels in the at least one infrared image associated with a temperature that is within a threshold temperature of the hottest temperature, and determining that a hot spot exists in the switchgear as a determination, the determination being a utilization of the determined pixels in the at least one infrared image; and an output unit for outputting an indication of a fault in the switchgear based on the determined hot spot.

The invention discloses a high-dynamic infrared image contrast enhancement method suitable for an aerospace background, and relates to the technical field of infrared image contrast enhancement, and the method comprises the steps: constructing an upper limit intermediate parameter and a lower limit intermediate parameter based on a mean value, a standard deviation and a cumulative distribution function of a gray scale probability distribution function of each frame of infrared image; the upper limit intermediate parameter is used for suppressing a highlight target occupying a small number of pixels; the lower limit intermediate parameter is used for suppressing a background occupying a large number of pixels; converting the upper limit intermediate parameter and the lower limit intermediate parameter into an upper limit gray threshold and a lower limit gray threshold; calculating a target mapping coefficient based on the upper and lower limit gray threshold values; constructing a mapping function based on the target mapping coefficient and the lower limit gray threshold; and based on the target infrared image and the mapping coefficient, obtaining the target infrared image after contrast enhancement so as to solve the problem that a traditional histogram equalization method has an over-enhanced background in infrared image processing and cannot adapt to infrared background changes.

The invention discloses a gas leakage detection method and device based on infrared image processing and a terminal, relates to the technical field of image processing, and mainly aims to solve the problem of low detection precision of existing gas leakage. Comprising the steps that when it is determined that a suspicious gas object exists based on foreground image pixels of a to-be-detected target area, a foreground difference matrix is constructed based on foreground image data, background image data and a self-adaptive threshold value, and the self-adaptive threshold value is determined based on a maximum value in a fitting curve of a difference frequency division mapping relation; constructing an average gray level signal matrix of the foreground difference matrix, and performing filtering processing on the average gray level signal matrix to obtain image data to be detected; and when the background image data passes the delay verification, performing weight fusion on the to-be-detected image data and the visible light image data to generate gas leakage detection result image data.

The present application relates to the field of infrared image processing, and discloses an infrared small target detection method and device based on image information entropy and multi-scale local contrast measure, comprising: (1) constructing a local weighted information entropy operator to obtain a local weighted information entropy image of an original infrared image; (2) using the weighted information entropy image and the structure tensor theory to construct a background prior and a target prior to obtain a local saliency prior; (3) using the original thermal infrared image to design a local contrast coefficient to obtain a multi-scale local contrast image; (4) using the multi-scale local contrast image and the corresponding structure tensor to design an information filter; and (5) fusing the local saliency prior and the information filter to realize the detection of the infrared small target. The present application comprehensively utilizes the weighted image entropy operator, the multi-scale local contrast measure and the structure tensor theory, and can effectively suppress the background, enhance the target and improve the detection performance of the infrared small target.

The invention provides a gun calibration method based on automatic bullet hole detection, a medium, equipment and a product, and relates to the technical field of infrared image processing, and the method comprises the steps: capturing an infrared image of a bullet hole in real time through an infrared thermal imager, carrying out the preprocessing, and constructing a bullet hole detection data set; constructing a bullet hole detection model based on the YOLOv5 network, adding a multi-scale feature fusion module in the YOLOv5 network, and fusing the shallow-layer features, the middle-layer features and the deep-layer features extracted by the backbone network into new deep-layer features; spatial pyramid pooling is replaced by hollow spatial pyramid pooling; inputting the bullet hole detection data set into the model to obtain bullet hole position data; according to the position data of the bullet hole, converting the coordinates of the bullet hole, and calculating the offset of the bullet hole; and according to the bullet hole offset, ballistic deviation is analyzed, and ballistic deviation is corrected. The method can break through environmental limitation, realizes real-time bullet hole detection, and improves gun calibration precision.

The invention provides an unmanned aerial vehicle image ship detection method and system based on FAST features and an HSO algorithm, and relates to the technical field of infrared image processing. The problems that a traditional method cannot efficiently position the ROI and is poor in threshold segmentation effect are solved. According to the technical key points, the method comprises the following steps: carrying out FAST feature detection on a target unmanned aerial vehicle image, and determining an interest region image according to a feature detection result; determining a candidate detection threshold value set for the interest area image by adopting an HSO algorithm, performing iterative optimization processing on the candidate detection threshold value set until a termination condition is met, and outputting a detection threshold value when the termination condition is met; and carrying out image segmentation on the region of interest image by adopting the detection threshold, and carrying out ship detection on the segmented image to obtain a ship detection result in the target unmanned aerial vehicle image. According to the method, the processing range can be greatly reduced, the target detection segmentation threshold search efficiency is improved, the noise immunity is enhanced, and an image target extraction technical system adaptive to multiple complex scenes is formed.

A system for monitoring a switchgear includes an infrared camera; a processing unit; and an output unit. The camera acquires a first infrared image. The processing unit determines a pixel in the first image with a maximum temperature and uses a second number of pixels to determine a temperature interval. The processing unit sorts the pixels in descending order of temperature to determine a threshold temperature and determines that a hot spot exists in the switchgear using the temperature interval for the first infrared image. The output unit is configured to output an indication of a fault in the switchgear when the determination has been made that a hot spot exists in the switchgear.

The invention relates to the technical field of infrared image processing, in particular to an infrared image non-uniformity vignetting correction method based on gradient prior, which comprises the following steps: acquiring a uniformly radiated infrared noise image of a target imaging system, and acquiring a gradient trend of infrared non-uniformity vignetting based on the infrared noise image; obtaining a real-time infrared image of the target imaging system, and obtaining a real-time gradient operator and a real-time gradient trend of the real-time infrared image based on the gradient trend; constructing an objective function based on the gradient trend, the real-time gradient operator and the real-time gradient trend, and solving the objective function to obtain an optimal value of the real-time gradient operator; and obtaining an optimal real-time gradient trend based on the real-time gradient operator optimal value, and correcting the real-time infrared image by using the optimal real-time gradient trend to obtain a target infrared image after non-uniformity vignetting correction. By using the method provided by the invention, the correction of the infrared non-uniform vignetting can be better realized.

The invention discloses a multi-scale infrared polarization image enhancement method based on MSD-PCNN, and relates to the technical field of infrared image processing, and the method comprises the steps: firstly, carrying out the multi-scale and multi-direction decomposition of an input intensity image I and a polarization feature map DoLP based on NSST, and obtaining a low-frequency sub-band and a high-frequency sub-band; secondly, according to the obtained low-frequency sub-band, designing a low-frequency sub-band fusion rule based on distance difference perception; and finally, according to the obtained high-frequency sub-band, designing a high-frequency sub-band fusion rule based on MSD-PCNN, and fully extracting texture and edge detail features. According to the MSD-PCNN-based multi-scale infrared polarization image enhancement method provided by the invention, redundant information interference is suppressed, the hierarchical definition of an image structure is improved, and meanwhile, the visual comfort and global naturalness of a fused image are also improved.

The invention relates to the technical field of infrared image processing, in particular to an autonomous inspection operation device and method for a control room, and solves the technical problem of low temperature detection precision in the prior art. The device comprises a data acquisition unit which is used for acquiring infrared image data of equipment in a control room; the infrared image data is used for representing an infrared time domain signal acquired based on infrared radiation reflection of the surface of the equipment in the control room; the signal processing unit is used for carrying out adaptive time-frequency transformation based on an infrared time-domain signal in the infrared image data to obtain a corresponding time-frequency matrix; the time-frequency matrix is used for representing energy distribution of infrared radiation on the surface of equipment in the control room at different times and different frequencies; and the inspection unit is used for separating the target heat radiation based on the time-frequency matrix and performing equipment temperature inspection according to the separated target heat radiation.

The present application relates to a kind of self-adapting wide gray scale range infrared image highlight target suppression method, belong to infrared image processing technical field.The present application utilizes the histogram information of original image, whether according to the decision function adaptive judgment enters highlight suppression algorithm processing flow according to statistical result, histogram of image is gathered and compressed in the flow, while using nonlinear mapping method, to achieve the purpose of effectively suppressing highlight target, so that highlight target and image background are effectively and reasonably displayed in display device.

The application discloses an ethylene cracking furnace tube infrared intelligent analysis system, which comprises, which are connected in sequence, an image acquisition module, a temperature monitoring module, a coking diagnosis module and a health management module; the image acquisition module acquires infrared images of cracking furnace tubes and burners by using an infrared thermal imaging online monitoring system, and establishes a cracking furnace tube infrared image database; the temperature monitoring module extracts and records the outer surface temperature of the ethylene cracking furnace tube from the processed infrared images of the ethylene cracking furnace tube by using an infrared image processing and recognition method; the coking diagnosis module combines the technologies of artificial intelligence and data mining, establishes a coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and realizes early warning and targeted tracking of the coking abnormal conditions; the health management module carries out health evaluation of the cracking furnace tube according to the results obtained by the coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and provides maintenance decision support. The application can realize integrated management of cracking furnace tube detection, diagnosis and maintenance.

Embodiments of the present application provide an image processing method and device for an infrared image and electronic equipment, and relate to the technical field of infrared image processing. The image processing method for the infrared image comprises obtaining a first difference image and a second difference image corresponding to a target infrared image to be processed, determining a detection result of the target infrared image on whether there is a horizontal stripe based on pixel values of each pixel point of the first difference image and the second difference image, obtaining the detection result in a time domain dimension and a space domain dimension, if the detection result in the time domain dimension and the detection result in the space domain dimension both represent that there is a horizontal stripe, performing image compensation on the target infrared image in the time domain dimension to obtain an intermediate image, performing image compensation on the intermediate image in the space domain dimension to obtain an image after removing the horizontal stripe corresponding to the target infrared image. It can be seen that the embodiments of the present application can effectively remove the horizontal stripe from the infrared image with the horizontal stripe.

The present invention belongs to the field of target recognition in infrared image processing and provides a high-time-sensitive target recognition method combined with boost segment detection to solve the problem of difficult recognition of high-time-sensitive targets. The main scheme includes: extracting the infrared radiation energy curve and space-time characteristic curve of the high-time-sensitive target, and performing outlier processing on these two types of time series signals to obtain the time series signal to be identified. Subsequently, the space-time characteristic curve is used to perform a first round of preliminary screening of non-boost segment targets. The two types of time series signals are then input into the main process of boost segment detection for a second round of screening to obtain high-time-sensitive targets containing boost segments, thereby realizing the detection of boost segments. Subsequently, key point detection is performed on the target containing boost segments to facilitate the extraction of the boost segment portion. Finally, a method of calculating waveform similarity and matching is used to classify and identify the high-time-sensitive targets containing boost segments.

The application discloses a non-uniformity correction method and system based on space domain and frequency domain features, relates to the technical field of infrared image processing, and corrects steps as follows: inputting a to-be-corrected image into a trained infrared image non-uniformity correction model to output a corrected image; model training steps are as follows: constructing a training data set based on a visible light data set; constructing an initial infrared image non-uniformity correction model, which adopts a U-Net structure to construct a non-uniformity correction backbone module, and the non-uniformity correction backbone module is provided with a space domain feature extraction module and a frequency domain feature extraction module; iteratively training the initial infrared image non-uniformity correction model by using the training data set to obtain the infrared image non-uniformity correction model. The application captures local pixel-level information through the space domain feature extraction module, combines global frequency spectrum features extracted by the frequency domain feature extraction module, realizes the synergistic effect of local noise suppression and global structure optimization, and improves the precision and robustness of non-uniformity correction.

The invention discloses an infrared video semantic segmentation method based on multi-granularity contrast learning, and belongs to the technical field of infrared image processing. The objective of the invention is to improve semantic segmentation precision without increasing reasoning time. The method comprises the following steps: carrying out time sequence consistent space enhancement transformation processing on an infrared video to obtain an annotation frame, and constructing a multi-granularity contrast learning framework by two sub-video clips with overlapped parts, namely adding three parallel multi-layer perceptron MLPs in front of a classification layer to form a context projection layer, a time sequence projection layer and a category projection layer; and on the basis of label supervision of the labeling frame, constructing a contrast learning self-supervision loss function from different granularities, wherein the contrast learning self-supervision loss function comprises a context generalization loss function, an in-video time sequence consistency loss function and a cross-video category discrimination loss function. Constructing a training set based on a difficult anchor point sampling strategy; and a trained infrared video semantic segmentation model based on multi-granularity contrast learning is obtained and is used for infrared video semantic segmentation.

The invention discloses a new energy automobile operation safety detection system and detection method, belongs to the field of new energy automobile detection, and aims to solve the problem of incomplete new energy automobile operation safety detection items in the prior art. The new energy automobile operation safety detection system comprises an OBD reading module, an electrical detection module, a direct current charging module, a charging detection module, a battery pack sealing detection module, an industrial personal computer, a host and a cloud server. According to the new energy vehicle operation safety detection system and detection method, the battery pack sealing detection module is arranged, the thermal imager and the convolutional neural network are utilized to perform sealing detection on the battery pack on the vehicle, the leakage type can be identified, and the detection accuracy is improved by utilizing an infrared image processing technology. The influence of a complex structure and a complex detection working condition is reduced, the recognition accuracy of the convolutional neural network model is improved, and then the battery pack sealing detection precision is improved.

The application discloses a kind of fan blade infrared monitoring system and method based on unmanned aerial vehicle inspection, it is related to image recognition technical field, solve the problem that the infrared image processing mode of fan blade in prior art is not enough to adapt to the temperature change of blade caused by varied environment.The infrared thermal imaging of fan blade is obtained by the infrared camera carried by unmanned aerial vehicle, the highest temperature and the lowest temperature of each infrared thermal imaging are calculated by temperature inversion, and then the temperature difference of fan blade is obtained, in addition, the infrared thermal imaging of each fan blade number in the same area is also obtained, and the highest temperature and the lowest temperature of each infrared thermal imaging are calculated, the temperature analysis set is constructed and the overall standard deviation is calculated to judge the dispersion degree of temperature in the area, and finally the health evaluation coefficient is established based on temperature difference and dispersion condition and other factors, to provide quantitative data reference for managers and decision makers, to facilitate subsequent management and decision-making work.

The invention provides an infrared image processing method and device, electronic equipment and a storage medium. The infrared image processing method comprises the steps of obtaining an infrared image, and obtaining a target area where an infrared target is located according to the infrared image; performing block processing on the infrared image so as to segment the infrared image into a plurality of image sub-blocks; obtaining the similarity between each image sub-block in the plurality of image sub-blocks and the target area; acquiring the area weight of each image sub-block in the plurality of image sub-blocks relative to the target area; obtaining a global background interference degree parameter according to the similarity degree and the area weight; and obtaining the interference degree of the background of the infrared image on the infrared target according to the global background interference degree parameter. According to the method, the global background interference degree parameter is obtained, the interference of the image background on infrared target detection is determined according to the size of the global background interference degree parameter, and the parameter or strategy of the detection algorithm is adjusted through the global background interference degree parameter, so that the accuracy and stability of infrared target detection are improved.

The application provides an infrared image processing method based on a space-time field and related equipment, and relates to the technical field of infrared image processing. The method comprises the following steps: acquiring continuous infrared images, constructing a unified background image, identifying key points of each target moving object and mapping the key points, determining an image sequence group according to a time sequence, and obtaining a complete motion trajectory curve according to the distribution of the key points. When a split-screen target infrared image is detected, the key points of each part are extracted after splitting, the two parts of the background are matched with the unified background image to determine an initial background area, the distance between the key points and the expected position predicted based on the trajectory, adjacent frames and motion rules is calculated, the current frame and the delayed frame are determined, and finally the complete sequence is obtained by processing the delayed frame. The method in the application can accurately repair the split-screen image in a dynamic scene, and ensure the time sequence coherence and integrity of the infrared image sequence.