Infrared Small Target Detection via Nonnegativity-Constrained Variational Mode Decomposition

Abstract

Infrared small target detection is one of the key techniques in the infrared search and track system. Frequency differences among target, background, and noise are often important information for target detection. In this letter, a nonnegativity-constrained variational mode decomposition (NVMD) method is proposed. Unlike the traditional frequency-domain methods, the proposed method can adaptively decompose the input signal into several separated band-limited subsignals, with the nonnegativity constraint. First, a bandpass filter is used as a preprocessing step. Second, by exploring the frequency and nonnegativity properties of the small target, the NVMD model is constructed. The potential target subsignal can be obtained by solving the NVMD model. By performing threshold segmentation on the potential target subsignal, we can obtain the detection result of the infrared small target. Experiments on six real infrared image sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression. Additionally, the proposed method shows strong robustness under various backgrounds.