MT: A Hybrid Mamba–Transformer for Remote Sensing Image Super-Resolution

Deep learning methods such as convolutional neural networks (CNNs) and Transformers have achieved remarkable progress in remote sensing image (RSI) super-resolution (SR). However, CNNs struggle to capture long-range dependencies effectively, while Transformers suffer from high computational overhead due to their quadratic complexity. To address these limitations, we propose a hybrid Mamba–Transformer (MT) framework that simultaneously enhances global modeling efficiency and local detail reconstruction. MT consists of two core components: a Focused Mamba Block (FMB) for global feature modeling and a PAB (Pixel-Adaptive Block) for pixel-level multi-scale enhancement. Within FMB, we introduce the Snake Vision State Space Module (SVSSM), equipped with a Snake Selective Scan Module (SSSM) to strengthen long-range dependency modeling while reducing redundant computations, significantly improving global modeling efficiency. Meanwhile, PAB adaptively aggregates multi-scale spatial information at the pixel level to more effectively restore fine-grained textures. Extensive experiments on multiple benchmark datasets demonstrate that MT consistently outperforms existing state-of-the-art (SOTA) methods. Notably, compared with the latest MambaIRv2, MT reduces parameters by 65.3%, decreases FLOPs by 68.6%, and achieves a 0.10 dB improvement in average PSNR, achieving a superior balance between performance and computational efficiency.