Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (21): 176-182.

### Small target detection in foggy image combined with low-rank and structured sparse

MA Jie, YANG Nan, ZHANG Xiudan

1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
• Online:2018-11-01 Published:2018-10-30

### 结合低秩和结构化稀疏的大雾图像小目标检测

1. 河北工业大学 电子信息工程学院，天津 300401

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.