TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution

Remote Sensing Image Super-Resolution (RSISR) aims to reconstruct high-resolution (HR) remote sensing (RS) images from low-resolution (LR) RS images inputs by overcoming the physical limitations of imaging systems. As a specialized task in the RS domain, RSISR provides critical support for downstream fine-grained ground object interpretation. Traditional RSISR methods face three major technical shortcomings: First, since wide-area RS images exhibit highly complex ground object characteristics, it is challenging to extract effective multi-scale features for SR reconstruction from their blurred representations. Second, the inherent imaging constraints of RS images lead to insufficient prior information, resulting in poor semantic consistency between SR reconstructions and real scenes. Third, existing SR methods struggle to balance the inherent trade-off between geometric accuracy and visual quality. To address these challenges, this paper proposes Texture Transfer Residual Denoising Dual Diffusion Model (TTRD3), a diffusion model (DM)-based reference-based super-resolution (RefSR) framework. Specifically, the Multi-scale Feature Aggregation Block (MFAB) is designed to extract spatially heterogeneous features with scale variations through parallel heterogeneous convolutional kernels. The Sparse Texture Transfer Guidance (STTG) is developed to mine multi-scale sparse texture features from high-resolution reference (Ref) images of similar scenes. The Residual Denoising Dual Diffusion Model (RDDM) framework is constructed to synergistically optimize geometric accuracy and visual quality by integrating the deterministic modeling of residual diffusion with the diverse generation of noise diffusion. Experiments on multi-source RS datasets demonstrate that TTRD3 significantly outperforms existing state-of-the-art (SOTA) methods in both geometric accuracy and visual quality. Our code and model are available at https://github.com/LED-666/TTRD3.