MambaFuse: a novel framework for pansharpening

Pansharpening is the process of fusing a high-resolution panchromatic image with a low-resolution multispectral image to yield a high-resolution multispectral image with enhanced spacial detail. Efficiently preserving spatial detail in the output image and processing of large volumes of images emerges as the main concerns of image pansharpening. To address these challenges, we leverage the recently introduced state-space models, which have been proven as competitive alternative to convolution neural networks and Transformers. We adapt MambaIR, a recently proposed state-space model for image restoration, to the pansharpening task. To efficiently inject spacial details, we adopt an approach similar to the successful FusionNet pansharpening network, where the nonlinear injection model is extracted through a deep convolution network. Combining these two major contributions yield MambaFuse, our innovative deep learning approach for pansharpening. Extensive experimental evaluation, involving qualitative and quantitative assessment on two urban area satellite datasets, and comparison with recent deep learning approaches demonstrate the soundness of our proposed MambaFuse pansharpening approach. For reproducibility purposes, we provide the source code and the experimental setups of our models at: code https://github.com/faridbi/ MambaFuse.