Infrared Small-Target Detection Based on Holistic Interframe Interaction and Spatiotemporal Local Contrast Method

Abstract

Infrared (IR) small-target detection (ISTD) plays a crucial role in IR search and tracking systems. However, current detection methods are limited by the small-target size and low signal-to-noise ratio of IR imagery. Furthermore, motion features for target detection are difficult to extract using simple frame subtraction due to poor imaging conditions. Therefore, we focus on the holistic interframe interaction to enhance the temporal feature and propose a spatiotemporal local contrast method in this letter. First, the motion-enhanced density peak clustering (ME-DPC) is employed to determine the robust localization of candidate targets, in which the density feature maps are generated by the preprocessing of nonconsecutive three-frame difference after image registration. Second, to reliably exploit interframe interactions across both nonconsecutive and successive frames, a temporal-domain saliency map is computed based on local regions from successive frames. Moreover, a spatial-domain saliency map is obtained using a novel trilayer local contrast measure (TLLCM). By fusing results from both domains, the IR small targets are detected through adaptive threshold segmentation. The experimental results on four real sequences demonstrate that the proposed method can achieve better detection performance by target enhancement and background suppression than other spatiotemporal algorithms.