CODNet: Infrared Small-Target Detection by Mitigating the Curse of Dimensionality

Infrared small-target detection (ISTD) presents significant challenges due to the blurred edges and low signal-to-noise ratio (SNR) of the targets, which often results in the loss of critical target features during convolution and downsampling. While researchers have explored complex architectures to preserve these features, such designs often come at the cost of increased computational cost. Our analysis reveals that the curse of dimensionality (COD) is a fundamental issue in ISTD. Due to the sparse information content of infrared small targets, they struggle to effectively utilize high-dimensional feature spaces, leading to a significant feature loss phenomenon. To address this, we propose the CODNet, which integrates spatial–channel shuffle (SCS) attention, dynamic gated channel attention (DGCA), and DCT-based frequency attention (DFA) in the UNet framework. SCS enhances feature representation through spatial attention and channel shuffling, making the feature space more compact and informative. DGCA performs selective channel compression to improve the feature SNR while reducing redundancy and sparsity. DFA incorporates frequency-domain features to supplement spatial representations, alleviating feature sparsity and improving information utilization. Experimental results demonstrate that the CODNet achieves state-of-the-art performance on four public datasets while maintaining relatively high operation efficiency.