Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution

Diffusion-based methods demonstrate significant potentialfor remote sensing image super-resolution at large scaling factors, partic-ularly in reference-based super-resolution (RefSR), where high-resolutionreference images provide critical fine-grained texture priors. However, ex-isting methods often suffer from a trade-off between over-reliance on ref-erence information, which leads to texture artifacts, and under-utilizationof such information, which results in insufficient detail recovery. To ad-dress these issues, we propose DS-DiT, a Decoupled Siamese DiffusionTransformer that decouples the interaction between low-resolution (LR)and reference (Ref) conditions within the attention mechanism. By al-lowing LR structural priors and Ref texture information to independentlyinteract with the noisy latent, the framework effectively mitigates com-petition between the two conditional sources. To further compensatefor the limited local modeling ability of global attention, we introduce aPatch-Level Weighting (PLW) module that adaptively modulates the fu-sion of conditional sources. In addition, the siamese architecture enablesan inference-time autoguidance strategy that exploits the prediction dis-crepancy between strong and weak Ref conditions to improve generationquality without additional training. Experimental results across multi-ple datasets and scaling factors show that DS-DiT outperforms existingmethods in both quantitative metrics and visual fidelity. The source codeis available at https://github.com/B1nary-L/DS-DiT.