WMRNet: Wavelet Mamba With Reversible Structure for Infrared Small Target Detection

Infrared small target detection (IRSTD) is of great practical significance in many real-world applications, such as maritime rescue and early warning systems, benefiting from the unique and excellent infrared imaging ability in adverse weather and low-light conditions. Nevertheless, segmenting small targets from the background remains a challenge. When the subsampling frequency during image processing does not satisfy the Nyquist criterion, the aliasing effect occurs, which makes it extremely difficult to identify small targets. To address this challenge, we propose a novel Wavelet Mamba with Reversible Structure Network (WMRNet) for infrared small target detection in this paper. Specifically, WMRNet consists of a Discrete Wavelet Mamba (DW-Mamba) module and a Third-order Difference Equation guided Reversible (TDE-Rev) structure. DW-Mamba employs the Discrete Wavelet Transform to decompose images into multiple subbands, integrating this information into the state equations of a state space model. This method minimizes frequency interference while preserving a global perspective, thereby effectively reducing background aliasing. The TDE-Rev aims to suppress edge aliasing effects by refining the target edges, which first processes features with an explicit neural structure derived from the second-order difference equations and then promotes feature interactions through a reversible structure. Extensive experiments on the public IRSTD-1k and SIRST datasets demonstrate that the proposed WMRNet outperforms the state-of-the-art methods.