High-quality maritime infrared images are crucial for accurate object detection, classification, and segmentation in maritime environments. However, maritime infrared images are often degraded by various types of noise, including non-uniform noise and detector non-uniformity-induced fixed-pattern noise (e.g., vertical stripe noise), which pose significant challenges for the aforementioned high-level vision tasks. A novel network, termed SWDNet (Spatial–Wavelet Joint Denoising Network), is proposed to jointly model spatial- and wavelet-domain features, enabling the effective enhancement of maritime infrared image quality while preserving fine image details. Two parallel sub-networks with distinct architectures are employed to extract complementary information for maritime infrared image denoising. In the upper branch, hierarchical spatial attention aggregation (HSAA) modules are employed at multiple scales to extract spatial features and adaptively assign importance weights to different spatial locations. The lower branch employs a Haar-based DWT for sub-band decomposition, a pixel-grouped self-attention module for boundary refinement, and parallel multi-scale horizontal convolutions to suppress vertical stripe noise in the HL sub-band. Finally, the directional edge enhancement (DEE) module employs learnable Sobel operators in conjunction with multi-layer convolutions to effectively extract and enhance directional edge features. Experimental results demonstrate that, compared with state-of-the-art methods, the proposed SWDNet achieves superior denoising performance on both synthetic and real maritime infrared datasets.
Xu et al. (2026) studied this question.