Spatial Invertible Network With Mamba-Convolution for Hyperspectral Image Fusion

Due to the complementary characteristics of hyperspectral image (HSI) and multispectral image (MSI), fusing them can generate a high-resolution hyperspectral image (HR-HSI). However, existing convolutional neural network (CNN)- and Transformer-based methods still suffer from challenges such as spectral distortion, insufficient texture representation, and limited ability to integrate global-local information. To address these issues, we propose a spatial invertible network (SINet) with Mamba-convolution. First, we design the multiscale invertible spatial (MISpa) module to effectively model the spatial information of the source image. The MISpa module utilizes Haar wavelet decomposition to separate high and low-frequency spatial components. By integrating invertible neural network (INN), it enhances the extraction of discriminative spatial features while maintaining structural integrity. To mitigate the risk of excessive reliance on spatial information, which may lead to spectral distortion, we employ spectral compression (via average pooling and 3 × 3 convolution) to extract spectral information at each scale of the source image. Through this way, SINet establishes an effective trade-off between detail enhancement and spectral fidelity. Second, we propose a Mamba convolutional fusion module (MCFM) that integrates feature extraction capabilities of Mamba and CNN. This module is designed to adaptively enhance the complementarity between global and local features, thereby guiding the process of feature enhancement. Experiments on both simulated and real-world datasets from quantitative and qualitative perspectives to validate the superiority of the proposed method compared to existing approaches.