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

• URL: http://arxiv.org/abs/2405.09873v2
• Date: Mon, 17 Feb 2025 01:24:21 GMT
• Title: IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
• Authors: Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Shinichiro Omachi,
• Abstract summary: IRSRMamba is a novel framework integrating wavelet transform feature modulation for multi-scale adaptation.<n>IRSRMamba outperforms state-of-the-art methods in PSNR, SSIM, and perceptual quality.<n>This work establishes Mamba-based architectures as a promising direction for high-fidelity IR image enhancement.
• Score: 7.842507196763463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared image super-resolution demands long-range dependency modeling and multi-scale feature extraction to address challenges such as homogeneous backgrounds, weak edges, and sparse textures. While Mamba-based state-space models (SSMs) excel in global dependency modeling with linear complexity, their block-wise processing disrupts spatial consistency, limiting their effectiveness for IR image reconstruction. We propose IRSRMamba, a novel framework integrating wavelet transform feature modulation for multi-scale adaptation and an SSMs-based semantic consistency loss to restore fragmented contextual information. This design enhances global-local feature fusion, structural coherence, and fine-detail preservation while mitigating block-induced artifacts. Experiments on benchmark datasets demonstrate that IRSRMamba outperforms state-of-the-art methods in PSNR, SSIM, and perceptual quality. This work establishes Mamba-based architectures as a promising direction for high-fidelity IR image enhancement. Code are available at https://github.com/yongsongH/IRSRMamba.

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