期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 22, 期 12, 页码 4996-5009出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2013.2281420
关键词
Infrared image; small target detection; low-rank matrix recovery
资金
- National Natural Science Foundation of China [61102131, 61373114, 11131006]
- Natural Science Foundation Project of Chongqing [CSTC 2010BB2411]
- Chongqing Distinguished Youth Foundation [CSTC 2011jjjq40002]
The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.
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