IOVarNet: Inner–Outer Variation Synergy Network for Infrared Small Target Detection

Sparsity and weak characteristics of targets pose significant challenges in infrared small target detection (IRSTD). For convolutional neural network-based methods, the increase in semantic information during propagation is often accompanied by the degradation of spatial features, which hampers the performance of IRSTD. In this paper, we proposed the Inner-outer Variation Synergy Network (IOVarNet) for IRSTD, which explicitly enhances the spatial response of targets by reinforcing their structural representations across different layers of the network. Specifically, IOVarNet leverages the delicate Total Variation-inspired Module, which takes the form of a partial differential equation, and incorporates it through the Inner-outer Variational Synergy architecture to supplement the target’s structural information at both the inner and outer layers of the encoder and decoder. Besides, the Dual Space Attention mechanism was introduced to enhance the semantic distinction between the target and background, while fusing spatial features from different layers. Experimental validation was conducted on three public datasets (i.e., NUDT-SIRST, NUAA-SIRST, and IRSTD-1K), demonstrating the performance superiority of IOVarNet over other methods. Open-source implementations will be available at https://github.com/Shangwei-Deng/IOVarNet.