Han J.,
Moradi, S.,
Wang W.,
Li N.,
Zhao Q.,
Luo Z.,
Nejatifar, S.,
Abedi, A.,
Ghamarani, A.,
Asanjarani, F. Frontiers in Marine Science (22967745)pp. 373-386
Infrared (IR) small dim target detection under complex background is crucial in many fields, such as maritime search and rescue. However, due to the interference of high brightness background, complex edges/corners and random noises, it is always a difficult task. Especially, when a target approaches a high brightness background area, the target will be easily submerged. In this paper, a new contrast method framework named hybrid contrast measure (HCM) is proposed, it consists of two main modules: the relative global contrast measure (RGCM) calculation, and the small patch local contrast weighting function. In the first module, instead of using some neighboring pixels as benchmark directly during contrast calculation, the sparse and low rank decomposition method is adopted to get the global background of a raw image as benchmark, and a local max dilation (LMD) operation is applied on the global background to recover edge/corner information. A Gaussian matched filtering operation is applied on the raw image to suppress noises, and the RGCM will be calculated between the filtered image and the benchmark to enhance true small dim target and eliminate flat background area simultaneously. In the second module, the Difference of Gaussians (DoG) filtering is adopted and improved as the weighting function. Since the benchmark in the first module is obtained globally rather than locally, and the patch size in the second module is very small, the proposed algorithm can avoid the problem of the targets approaching high brightness backgrounds and being submerged by them. Experiments on 14 real IR sequences and one single frame dataset show the effectiveness of the proposed algorithm, it can usually achieve better detection performance compared to the baseline algorithms from both target enhancement and background suppression point of views. Copyright © 2025 Han, Moradi, Wang, Li, Zhao and Luo.
