IRSRMamba：通过基于 Mamba 的小波变换特征调制模型实现红外图像超分辨率
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
AI总结：
针对红外图像超分辨率中的长程依赖与多尺度特征难题，提出 IRSRMamba，通过小波变换特征调制和 SSM 语义一致性损失改善重建质量，并在多项指标上超越现有方法。
摘要：
红外图像超分辨率需要长程依赖建模和多尺度特征提取，以应对同质背景、弱边缘和稀疏纹理等挑战。尽管基于 Mamba 的状态空间模型（SSM）以线性复杂度在全局依赖建模方面表现出色，但其分块处理会破坏空间一致性，从而限制其在红外图像重建中的有效性。我们提出 IRSRMamba，这是一种新颖框架，融合用于多尺度适应的小波变换特征调制，以及基于 SSM 的语义一致性损失，以恢复碎片化的上下文信息。该设计增强了全局—局部特征融合、结构连贯性和精细细节保持，同时减轻分块导致的伪影。在基准数据集上的实验表明，IRSRMamba 在 PSNR、SSIM 和感知质量方面优于最先进方法。本工作确立了基于 Mamba 的架构作为高保真红外图像增强的有前景方向。代码可在 https://github.com/yongsongH/IRSRMamba 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
