基于低秩和重加权稀疏表示的红外弱小目标检测算法

doi:10.3969/j.issn.0255-8297.2023.05.003

Abstract

Abstract: The detection of infrared dim and small targets is one of the key technologies in the infrared warning system. It remains challenging to accurately, quickly, and robustly detect dim and small targets. This paper proposes an infrared dim and small target detection algorithm based on low-rank and reweighted sparse representation. The algorithm formulates a new optimization equation to more accurately describe the rank of the background matrix and utilizes the structure tensor to extract local prior information. Experimental results show that the proposed algorithm improves the accuracy, speed, and robustness of detecting dim and small infrared targets.

Key words: small target detection, infrared images, matrix decomposition, low rank sparse representation

CLC Number:

P751.1

YANG Yadong, HUANG Shengyi, TAN Yihua. Infrared Dim and Small Target Detection Algorithm Based on Low-Rank and Reweighted Sparse Representation[J]. Journal of Applied Sciences, 2023, 41(5): 753-765.

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Infrared Dim and Small Target Detection Algorithm Based on Low-Rank and Reweighted Sparse Representation

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