Research on the multi-scale low rank method and its optimal parameters selection strategy for infrared small target detection.

The multi-scale low rank method is now a novel technology for the target detection in an image, in which the pattern of the sub-image block partition plays a crucially important role for the performance of the target detection. In this paper, we propose an efficient multi-scale low rank method for the infrared small target detection. The main work is that we systematically analysis the principle of how to select the optimal parameters such as the shape, size and number of the image block to obtain an efficient decomposed combinatorial form which is appropriate for the infrared small target detection. Then, we propose an efficient strategy to determine the optimal iterative times adaptively for ADMM's loop iteration process, by which the more accurate results can be obtained and the running speed is improved. Finally, we fuse the infrared small target images with three optimal scales to preserve the small target information. The experimental results show that the presented strategy on the selection of optimal parameter and adaptive iterative method can guarantee the effectiveness of small target extraction and save the running time. [ABSTRACT FROM AUTHOR]