Remote Sensing Image Super-Resolution for Imbalanced Textures: A Texture-Aware Diffusion Framework

Generative diffusion priors have recently achieved state-of-the-art performance in Natural Image Super-Resolution, demonstrating a powerful capability to synthesize photorealistic details. However, their direct application to Remote Sensing Image Super-Resolution (RSISR) reveals significant shortcomings. Remote sensing images present a unique challenge: ground objects often exhibit globally stochastic yet locally clustered patterns. This characteristic leads to highly imbalanced texture distributions, posing a significant hurdle to the model's spatial perception. To address this, we propose TexADiff, a novel framework that begins by estimating a Relative Texture Density Map (RTDM) that reflects the underlying texture distribution. TexADiff then leverages this RTDM in three synergistic ways: as an explicit spatial conditioning to guide the diffusion process, as a loss modulation term to prioritize texture-rich regions, and as a dynamic adapter for the sampling schedule. These modifications are designed to endow the model with explicit texture-aware capabilities. Experiments demonstrate that TexADiff achieves superior quantitative metrics. Furthermore, qualitative results show that our model generates faithful high-frequency details while effectively suppressing texture hallucinations. This improved reconstruction quality also results in significant gains in downstream task performance.