LTMHN: Learnable TransMamba Hybrid Network for Remote Sensing Image Superresolution

Remote sensing image superresolution technology can significantly improve the resolution of remote sensing images in practical applications of great significance. Remote sensing images are rich in detailed textures and multiscale structures. Therefore, reducing the computational complexity while maintaining high-quality detail recovery becomes a key challenge. Mamba relies on a state space model of linear complexity that effectively captures long-distance dependencies but lacks recovery of detailed textures. Transformer excels at capturing global context, but has high computational overhead due to quadratic complexity. Most current methods have difficulty balancing detail recovery capability and complexity. To address these challenges, we propose a dual-branch learnable TransMamba hybrid network (LTMHN) that fully integrates the strengths of state space model and attention mechanisms. Specifically, the learnable TransMamba block employs learnable adapter to dynamically fuse the attention branch with a linearly complex state space branch. This design preserves global context modeling capability while reducing computational cost. To enhance channelwise feature representation, the adaptive channel enhancement module (ACEM) highlights important feature channels and suppresses redundant ones, substantially improving channel utilization. Experimental results show that ACEM increases the average channel activation by approximately 60% compared to a conventional transformer. Furthermore, the learnable multiscale overlapping cross attention employs a multiscale information fusion strategy of neighboring windows to effectively enhance the recovery of detailed information such as image edges and textures. Extensive experiments on remote sensing datasets demonstrate our LTMHN could perform favorably against state-of-the-art transformer-based and Mamba-based methods, both qualitatively and quantitatively. In brief, LTMHN achieves an average PSNR improvement of approximately 0.11 dB over the advanced method MambaIR, but only accounts for 68% and 66% of its computational cost and parameters.