Mamba-enhanced Local Attention Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (SR) technology can provide more accurate data support for environmenremove tal monitoring, disaster assessment, and other fields. Recently, Mamba-based methods have achieved excellent results in the SR field, outperforming Convolutional Neural Networks (CNNs) and Transformers. However, these methods may suffer from texture misalignment and distortion since the scanning mechanism in Mamba may destroy the inherent two-dimensional spatial continuity of the images. Moreover, the texture details may be blurred or lost due to the limited ability of its state transition paradigm to model high-frequency details. To adremove dress the above two drawbacks, we propose a new Mamba-Enhanced Local Attention network (MELA). It utilizes Mamba and overlapping window attention mechanisms to achieve global-local feature complementarity, effecremove tively enhancing high-frequency details and adaptively modeling feature associations. Specifically, we use the proposed Local Self-Attention Vision Mamba (LSViM) in MELA to enhance spatial modeling capabilities while maintaining computational efficiency through the synergy of the cross-scanning layer and the attention mechremove anism. Experimental results on 4 widely used remote sensing datasets demonstrate that MELA outperforms the existing methods, and obtains high-resolution images with clearer edges and stronger feature continuity.