CCST: Lightweight Criss-Cross Swin Transformer for Remote Sensing Image Super-Resolution under Multi-Degradation Scenarios

Transformer-based methods excel in remote sensing image super-resolution (RSISR) due to their ability to capture abundant self-similarity textures and structural information, which are critical for restoring high-resolution details. However, the core component of these methods, the self-attention mechanism, requires substantial computational resources, resulting in a restriction of deployment on resource-constrained remote sensing devices. The redundant feature extraction and insufficient refinement of local features also cause the significant RSISR performance drop in multi-degradation scenarios where RSI often suffers from optical blur and transmission noise caused by adverse imaging environments. To mitigate the above limitations, we present a Lightweight Criss-Cross Swin Transformer (CCST) for Blind Remote Sensing Image Super-Resolution. Concretely, we introduce criss-cross encoding into the self-attention mechanism to establish criss-cross feature correlations of each pixel in an RSI. Along with the shifted window mechanism, repeated criss-cross encoding indirectly builds global feature dependencies. Compared to the conventional self-attention mechanism that spans the entire window, the proposed criss-cross self-attention mechanism drastically reduces computational overhead and feature redundancy while capturing more global information by using large-size windows. Furthermore, to enhance the ability of the RSISR model to extract local features while expanding its receptive field, we incorporate a gated feed-forward tiny network to integrate multi-scale features and introduce gated coefficient learning to adaptively filter redundant counterparts. Experimental results show that our CCST achieves superior performance with lower computational demand compared to state-of-the-art Transformer-based SR algorithms and traditional RSISR methods in multi-degradation scenarios.