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所属单位：电子信息工程学院

发表刊物：IET IMAGE PROCESSING

关键字：object detection infrared imaging principal component analysis small target detection reweighted infrared patch-image model infrared small target detection sparse background edges background estimation reweighted nuclear norm nontarget sparse points reweighted robust principal component analysis problem inexact augmented Lagrangian multiplier method background clutter suppression reweighted l(1) norm

摘要：To further improve the effect of infrared small target detection, a reweighted infrared patch-image model is proposed. First, the authors point out that the nuclear norm in the infrared patch-image model could easily leave some sparse background edges in the target patch-image, leading to an inaccurate background estimation. Then, to overcome this defect, the reweighted nuclear norm is adopted to constrain the background patch-image, which could preserve the background edges better. Considering that some non-target sparse points could not be suppressed by only using l(1) norm, the authors introduce the reweighted l(1) norm to further enhance the sparsity of target image. Finally, the proposed model is formulated as a reweighted robust principal component analysis problem and solved by the inexact augmented Lagrangian multiplier method. Extensive experiments show that the proposed model outperforms the other six competitive methods in suppressing background clutter and detecting target.

ISSN号：1751-9659

是否译文：否

发表时间：2018-01-01

合写作者：郭军,戴一冕

通讯作者：吴一全