Single hyperspectral image super-resolution (SHSR) aims to restore high-resolution images from low-resolution hyperspectral images. Recently, the Visual Mamba model has achieved an impressive balance between performance and computational efficiency. However, due to its 1D scanning paradigm, the model may suffer from potential artifacts during image generation. To address this issue, we propose HSRMamba. While maintaining the computational efficiency of Visual Mamba, we introduce a strip-based scanning scheme to effectively reduce artifacts from global unidirectional scanning. Additionally, HSRMamba uses wavelet decomposition to alleviate modal conflicts between high-frequency spatial features and low-frequency spectral features, further improving super-resolution performance. Extensive experiments show that HSRMamba not only excels in reducing computational load and model size but also outperforms existing methods, achieving state-of-the-art results.

翻译：单幅高光谱图像超分辨率（SHSR）旨在从低分辨率高光谱图像中恢复高分辨率图像。近年来，Visual Mamba 模型在性能与计算效率之间取得了令人印象深刻的平衡。然而，由于其采用一维扫描范式，该模型在图像生成过程中可能存在潜在的伪影问题。为解决此问题，我们提出了 HSRMamba。该模型在保持 Visual Mamba 计算效率的同时，引入了一种基于条纹的扫描方案，以有效减少全局单向扫描产生的伪影。此外，HSRMamba 利用小波分解来缓解高频空间特征与低频光谱特征之间的模态冲突，从而进一步提升超分辨率性能。大量实验表明，HSRMamba 不仅在降低计算负载和模型参数量方面表现优异，其性能也超越了现有方法，达到了当前最优水平。