关键词: 小目标检测, 低秩, 结构化稀疏, 诱导范数

Abstract: The traditional low-rank sparse decomposition model can not be applied directly to a single image for target detection. And it ignores the spatial structure of the target pixels leading to the detection accuracy is not high. Aiming at these two problems, a small target detection algorithm in a single foggy image based on low-rank and structured sparse is proposed. Firstly, the original fog image is preprocessed to obtain the fog patch image composed of local sub-images, and the problem of small target detection is transformed into low-rank and sparse decomposition problem. Then, considering the spatial structure of the target pixels, the structured sparsity-inducing norm is introduced into matrix decomposition of the fog patch image to constrain the target. Finally, the patch images which are obtained by matrix decomposition are post-processed to obtain the background image and the target image. The experimental results on single foggy images show that the proposed algorithm ensures the integrity of the small target detection and improves the detection accuracy.

Key words: small target detection, low-rank, structured sparse, inducing norm