Infrared Small Target Detection via Dynamic Image Structure Evolution

Abstract

Infrared small target detection (IRSTD) is a challenging task, due to the scarce target feature, complex background interferences, and poor image quality. The existing studies handle the IRSTD problem by making specific distribution assumptions of target and background, which incurs two issues. First, the specific assumptions of background cannot always hold. Second, the distorted target distributions contaminated by heavy clutters and noise are difficult to model statically. This paper handles the problems from the discriminative and dynamic perspectives, and proposes an original mechanism named dynamic image structure evolution (DISE) and an DISE-derived single-frame IRSTD framework. First, we resolve IRSTD by a discriminative model without assuming specific background distributions, which is based on a mathematical definition of structure singularity modeling the ideal target structure. Second, to decouple the target distributions from interferences, DISE guides the distorted target to reveal potential structure singularity while suppress the interference signal through iterative procedures of structure collapse, intensity settlement, and collapse convergence. The three functional procedures of DISE perform their own duties regarding target enhancement and background suppression. Moreover, an innovative chain mechanism is introduced to propagate the structure field. Experiments on real data sets demonstrate the superiority of DISE against the state-of-the-art IRSTD methods.