IRSRMamba: Infrared Image Super-Resolution via Mamba-Based Wavelet Transform Feature Modulation Model

Infrared image super-resolution (IRSR) is challenging due to weak structures and textures. While Mamba-based state-space models (SSMs) efficiently model long-range dependencies, their inherent blockwise processing disrupts spatial consistency, limiting direct IRSR applicability. We propose IRSRMamba, a novel Mamba-based framework overcoming this limitation via tailored structural and textural preservation. Integrated into a Mamba backbone, our key innovations are the follwing: 1) wavelet transform feature modulation (WTFM), enhancing multiscale frequency-aware feature extraction to mitigate block-induced coherence loss and 2) an SSMs-based semantic consistency loss, enforcing cross-block alignment to restore fragmented context. IRSRMamba achieves superior global–local fusion, structural coherence, and fine-detail preservation. Experiments show state-of-the-art (SOTA) peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual quality on infrared (IR) benchmarks, as well as robust generalization to remote sensing. This work establishes Mamba-based architectures as highly promising for high-fidelity IR image enhancement. Code is available at https://github.com/yongsongH/IRSRMamba