Abstract: The present application provides a method and a device for estimating background motion of infrared image sequences and a storage medium. The method comprises: determining regions to be matched according to the maximum displacement of two adjacent frames of images in background motion, dividing the region to be matched into T sub-regions, and generating M groups of sub-window sets in the sub-regions randomly, and computing the background motion displacement corresponding to these sub-window sets by synchronous mutual information matching, then calculating the background motion displacement between two adjacent frames of images by a potential function voting method according to the background motion displacement corresponding to these sub-window sets. The method in the present application is based on image block matching, and does not depend on feature point extraction, so it has high robustness for background motion estimation of infrared image sequences with few feature points and noise interference.

Abstract: The present application provides a method and a device for estimating background motion of infrared image sequences and a storage medium. The method comprises: determining regions to be matched according to the maximum displacement of two adjacent frames of images in background motion, dividing the region to be matched into T sub-regions, and generating M groups of sub-window sets in the sub-regions randomly, and computing the background motion displacement corresponding to these sub-window sets by synchronous mutual information matching, then calculating the background motion displacement between two adjacent frames of images by a potential function voting method according to the background motion displacement corresponding to these sub-window sets. The method in the present application is based on image block matching, and does not depend on feature point extraction, so it has high robustness for background motion estimation of infrared image sequences with few feature points and noise interference.

Abstract: The present disclosure provides an image processing method and device, which are applied to the technical field of image processing, wherein the method comprises: dividing an infrared decompression image with block artifacts into image blocks distributed in a rectangular array; acquiring boundary gray information of the image blocks, and then determining boundary drop information of each image block and its adjacent image block, thereby obtaining drop information of the image block; constructing a constraint optimization objective function of the image based on the boundary drop information, the drop information and preset compensation information; determining a target compensation matrix according to the constraint optimization objective function, wherein each element value in the target compensation matrix is a target value of compensation information of an image block, the position of which in the image is the same as the position of the element value in the target compensation matrix.

Abstract: An retinal vessel image enhancement method comprises: constructing a blood vascular dictionary; applying Frangi-based filtering to retinal vessel images, deciding blood vessels in a second image sub-block belong to wide or thin vessels by directional filtering, and setting residual error weight and residual error threshold of a vascular region; calculating inner products between the second image sub-block and each first image sub-block, selecting a first image sub-block with maximum inner product, and calculating its corresponding sparse coefficient; calculating residual error image, and calculating the residual error of the vascular region according to the residual error weight of the vascular region, when the residual error is greater than the residual error threshold, the residual error image is set as a second image sub-block, repeating and calculating residual error; reconstructing the second image sub-block according to the sparse coefficient, then restructuring each reconstructed second image sub-block

Abstract: The present disclosure provides an image processing method and device, which are applied to the technical field of image processing, wherein the method comprises: dividing an infrared decompression image with block artifacts into image blocks distributed in a rectangular array; acquiring boundary gray information of the image blocks, and then determining boundary drop information of each image block and its adjacent image block, thereby obtaining drop information of the image block; constructing a constraint optimization objective function of the image based on the boundary drop information, the drop information and preset compensation information; determining a target compensation matrix according to the constraint optimization objective function, wherein each element value in the target compensation matrix is a target value of compensation information of an image block, the position of which in the image is the same as the position of the element value in the target compensation matrix.

Abstract: The disclosure provides a method for sea background modeling and suppression on high-resolution remote sensing sea images, the method comprises steps of segmentation, classification, calculation, modeling and suppression. The disclosure also provides a system for sea background modeling and suppression on high-resolution remote sensing sea images. The technical solution of the disclosure is provided in such way as to perform sea background suppression first and then detect the target, leading to large improvement of detection accuracy and reduction of false alarm.

Abstract: The present disclosure relates to a distributed video panoramic display system. The present disclosure collects video images of different areas through an existing camera network; the video images collected by each camera are converted into the same coordinate system by each video splitting node in a video splitting module; a plurality of paths of video image data are subjected to parallel splitting processing, so that a display area of each split video image block in a panoramic display unit respectively corresponds to a separate display screen; and each video reorganization node in a video reorganization module is used to perform reorganization processing on each received video image block, then each path of video image formed after the processing is output to the corresponding display screen of the panoramic display unit, and a panoramic video image is formed.

Abstract: A method of eliminating stripe noise for infrared images including: selecting a specific row of data from an to-be-filtered image Aim, and obtaining a stripe width N1 from information of a frequency spectrum of the data; intercepting (N1+1) new sequences of different lengths from a specific row sequence of the image according to the stripe width N1, and obtaining an optimal interception length N by analyzing information of amplitude spectrum of each new sequence; splitting the to-be-filtered image Aim with a size of M×Nn into a pair of optimum to-be-filtered sub-images Bim and Cim with a size of M×N according to the length N; obtaining two sub-images Bimf and Cimf after using a notch-comb filter to filter the two optimum to-be-filtered sub-images Bim and Cim; carrying out a brightness adjustment and a combination process to the two sub-image Bimf and Cimf to obtain an image Aimf.

Abstract: An retinal vessel image enhancement method comprises: constructing a blood vascular dictionary; applying Frangi-based filtering to retinal vessel images, deciding blood vessels in a second image sub-block belong to wide or thin vessels by directional filtering, and setting residual error weight and residual error threshold of a vascular region; calculating inner products between the second image sub-block and each first image sub-block, selecting a first image sub-block with maximum inner product, and calculating its corresponding sparse coefficient; calculating residual error image, and calculating the residual error of the vascular region according to the residual error weight of the vascular region, when the residual error is greater than the residual error threshold, the residual error image is set as a second image sub-block, repeating and calculating residual error; reconstructing the second image sub-block according to the sparse coefficient, then restructuring each reconstructed second image sub-block

Abstract: A method of eliminating stripe noise for infrared images including: selecting a specific row of data from an to-be-filtered image Aim, and obtaining a stripe width N1 from information of a frequency spectrum of the data; intercepting (N1+1) new sequences of different lengths from a specific row sequence of the image according to the stripe width N1, and obtaining an optimal interception length N by analyzing information of amplitude spectrum of each new sequence; splitting the to-be-filtered image Aim with a size of M×Nn into a pair of optimum to-be-filtered sub-images Bim and Cim with a size of M×N according to the length N; obtaining two sub-images Bimf and Cimf after using a notch-comb filter to filter the two optimum to-be-filtered sub-images Bim and Cim; carrying out a brightness adjustment and a combination process to the two sub-image Bimf and Cimf to obtain an image Aimf.

Abstract: The disclosure provides a method for sea background modeling and suppression on high-resolution remote sensing sea images, the method comprises steps of segmentation, classification, calculation, modeling and suppression. The disclosure also provides a system for sea background modeling and suppression on high-resolution remote sensing sea images. The technical solution of the disclosure is provided in such way as to perform sea background suppression first and then detect the target, leading to large improvement of detection accuracy and reduction of false alarm.

Abstract: The present disclosure relates to a distributed video panoramic display system. The present disclosure collects video images of different areas through an existing camera network; the video images collected by each camera are converted into the same coordinate system by each video splitting node in a video splitting module; a plurality of paths of video image data are subjected to parallel splitting processing, so that a display area of each split video image block in a panoramic display unit respectively corresponds to a separate display screen; and each video reorganization node in a video reorganization module is used to perform reorganization processing on each received video image block, then each path of video image formed after the processing is output to the corresponding display screen of the panoramic display unit, and a panoramic video image is formed.