<p>Fundamental constraints in imaging physics make the direct acquisition of high-resolution hyperspectral images inherently difficult. To address this limitation, single hyperspectral image super-resolution (SHSR) has consequently arisen as a viable method. However, existing super-resolution methods based on Convolutional Neural Networks (CNNs) and Transformers often face limitations such as restricted receptive fields and high computational costs. Mamba, with its global modeling capability and linear computational complexity, has shown remarkable performance in various computer vision tasks. This makes it particularly promising for hyperspectral image super-resolution. To better leverage these capabilities, we propose Spatial Mamba Block (SAMB) and Spectral Mamba Block (SEMB) tailored for hyperspectral image processing. Specifically, SAMB extracts spatial features by scanning along the spatial dimension, while SEMB learns frequency-domain spectral features through Discrete Wavelet Transform Mamba Block (DWMB). Additionally, the designed Spectral Mamba Module (SPEM) further enhances spectral learning by conducting dual-spectral-dimension scanning. Extensive experimental results demonstrate that our proposed method outperforms several state-of-the-art approaches, highlighting its effectiveness in hyperspectral image super-resolution tasks.</p>