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The Research On Technology Of Infrared Small Target Detection Based On Compressive Sensing

Posted on:2016-11-11Degree:MasterType:Thesis
Country:ChinaCandidate:A D LiFull Text:PDF
GTID:2348330536967405Subject:Information and Communication Engineering
Abstract/Summary:PDF Full Text Request
Infrared target detection technology is crucial to information processing in IRST systems.The strict demand for processing time of large amount image data is critical,making require for higher computing and transmission ability.The infrared target detection algorithm in compressive domain achieves good performance with low required data storage.This paper took some in-depth research around the related technology of infrared target detection of infrared image in compressive domain,and obtained the certain research results.The structure of this dissertation is as follows:The chapter 2 introduces the characteristics of backgrounds,targets and noise in infrared images,and discusses the main process of targets detection.An introduction of basic concepts of compressive sensing and low-rank matrix recovery is made,for targets detection in compressive domain.The chapter 3 analyses the existing infrared target detection algorithm in compressive domain,which is difficult to estimate complex background parameters,and has high false dismissal probability when targets are close to their neighbors.The original infrared image is projected on a sensing matrix to obtain the measurement vector.The sparse target matrix and the low-rank background matrix can be recovered and separated simultaneously from the measurements based on low-rank and sparse matrix decomposition in compressive domain with adaptive parameter.The infrared small target detection is realized by threshold segmentation of statistical model of noise.Results indicate that the proposed method outperforms the previous method in both subjective and objective qualities with less data storage,and solves the false dismissal probability problem when targets are close to their neighbors.The chapter 4 analyses the compressive sensing model for infrared image sequence.The sparse target matrix and the low-rank background matrix can be recovered and separated simultaneously based on similarity-based clustering method.Results indicate that the proposed method outperforms the previous method in both subjective and objective qualities under complex infrared background,and solves the false dismissal probability problem for background targets.In general,the whole research in this dissertation has certain reference value for the further study the targets detection in compressive domain.
Keywords/Search Tags:infrared image, small target detection, compressive sensing, low-rank and sparse matrix decomposition
PDF Full Text Request
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