Infrared small target detection method based on overcomplete sparse representation

Abstract

The invention relates to an infrared small target detection method based overcomplete sparse representation, belonging to the technical field of image processing. The method comprises the following steps of: generating a plurality of infrared target sample images by adopting a two-dimensional Gaussian model, and further constructing an infrared target overcomplete dictionary; dividing a test image into a plurality of sub-images, and respectively carrying out extraction representation coefficient treatment on each sub-image to obtain a representation coefficient of each sub-image under the overcomplete dictionary of the infrared target sample images; carrying out indexing treatment on each representation coefficient to obtain a sparse coefficient of each sub-image; and when the sparse coefficient of the sub-image is more than a threshold Tau, confirming a target exists in the sub-image and further acquiring the target position. The invention is easier to realize without training and can grasp internal geometrical characteristics of the target more effectively, that is to say, the representation coefficients of the sub-images have more remarkable difference under the dictionary, thereby being capable of suppressing the background better, protruding the target and acquiring the higher detection ratio.

Description

technical field [0001] The invention relates to a method in the technical field of image processing, in particular to an infrared small target detection method based on a super-complete sparse representation. Background technique [0002] With the rapid development of infrared detection technology and infrared sensor devices, the application of this technology in the fields of infrared astronomical observation, infrared remote sensing, infrared safety detection, and infrared medical imaging has expanded rapidly. Since the 1990s, infrared imaging technology has been used to assist search and rescue work in various environments. Infrared imaging uses the radiant energy of the target, because it has the ability to penetrate smoke, clouds, rain and snow, etc. It has strong anti-interference ability and can work at night. Therefore, in the field of traffic and security, the detection and tracking technology of small infrared targets has received more and more attention. However,...

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Problems solved by technology

[0004] After searching the existing literature, it was found that Liu Ruiming used PCA (Principal Component Analysis) and KPCA (Kernel Principal Component Analysis) to detect infrared small targets in 2009 (Ruiming Liu. "Eigentargets Versus Kernel Eigentargets: Detection of Infrared Point Targets Using Linear and Nonlinear Subspace Algorithms (feature target and kernel feature target: infrared point target detection method based on linear and nonlinear subspace)", Infrared Milli Terahz Waves30: 278-293.), this method trains a large number of samples, using the final The obtained target model transforms the target detection problem into a pattern classification problem, but the learning process of the target model makes the method very complicated and not very practical

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