Dual multi-scale filter with SSS and GW for infrared small target detection

Abstract Multi-scale analysis is a powerful tool in the field of signal processing. In this paper, we propose an efficient small target detection algorithm that is mainly based on the dual multi-scale filters which work sequentially. The algorithm consists of two stages: at the first stage, Spectrum Scale-Space (SSS) is used as the pre-process procedure to obtain the multi-scale saliency maps, which can suppress the low frequency background noise and make the target region prominently at different scale levels. As a result, the more detail information and feature information can be exhibited in the different decomposition image level. After then, the least information entropy is used as the criterion to select the optimal salient map out; At the second stage, the Gabor wavelets (GW) algorithm is utilized to suppress the high frequency noise remained in the optimal salient map and match the feature of size and direction of small target at different scales and angles, and next, to ensure the robustness of the target detection, Non-negative Matrix Factorization (NMF) is applied to fuse all the GW multi-scale images into one optimal target image, which is the final output of the presented method. Experimental results show that, compared with the contrast method, the proposed algorithm has high SCRG and high correct target detection rate, and works well in different types of complex backgrounds.