DSN-RD: Domain-Statistics-Driven Residual Diffusion for Data-Efficient, Compute-Aware Super-Resolution in Frequency-Shifted Imagery

Abstract—Diffusion models are well-suited for image super-resolution (SR), but suffer from challenges in domains with non-natural image frequency statistics, limited paired training data, and limited sampling budget. Adversarial methods can handle some of these conditions, but can suffer from instability during training and augmentation dependence. We introduce DSN-RD, a conditional residual-diffusion image super-resolution method built with the following insight: compute only a few domain statistics once and use them in three ways: (1) as domain shift and data scarcity prior for the diffusion model (radially averaged residual frequency spectrum), (2) as a mechanism to avoid spurious detail in flat regions (stability without a discriminator), and (3) as a sampling budget allocation to image tiles based on their level of detail using DDIM. A common label is used to condition on augmentation and on real vs. simulated pairs, and is identity at inference. We describe the model, training and inference methods and present a pre-defined comparison and ablation methodology along with decision rules. No experiments have been performed and thus all potential advantages are presented as hypotheses. We also point out that the normalization used is equivalent to a transformation of the standard DDPM and thus any results are likely to be empirical rather than theoretical. Additionally, further research is needed into related work in order to verify novelty.