FoveaMamba: Structure-Conditioned State Evolution for Remote Sensing Image Super-Resolution

Remote-sensing image super-resolution requires recovering fine geometric details while modeling long-range spatial dependencies. An HR-reference-based structure-stratified analysis reveals substantial SSIM degradation from low- to high-structure regions across representative SR methods. We propose FoveaMamba, a structure-conditioned state-space network that incorporates multi-scale two-dimensional context into selective state evolution while retaining fixed directional traversal. Specifically, its Context-Guided State Space Module (CGSSM) uses the extracted context to modulate the parameterization of the discretization step $\Delta$. A stabilized high-frequency residual pathway provides complementary local detail compensation alongside long-range state-space propagation. Under a unified AID-training protocol, FoveaMamba achieves the highest PSNR on all six evaluated remote-sensing datasets and the highest SSIM on five. Structure-stratified comparisons indicate larger paired SSIM gains over selected baselines in high-structure regions, while internal mechanism analyses reveal greater network-averaged discretization-step modulation in these regions. Following DIV2K fine-tuning, FoveaMamba also attains the highest PSNR on four of the five evaluated natural-image benchmarks. Source code and trained models will be made publicly available.