DDFSRM: Denoising Diffusion Fusion Model for Line-Scanning Super-Resolution

Abstract

Line-scanning super-resolution (LSSR) provides a new way to improve the spatial resolution of images. To further improve its super-resolution (SR) performance boosted by deep learning, a new denoising diffusion fusion super-resolution model (DDFSRM) is proposed in this paper. Considering the reconstruction optimization problem in LSSR is ill-posed, we first build a model-based fusion SR guidance and take the diffusion model sampling mean as an implicit prior learned from data to constrain the optimization model, which improves the model's accuracy. Then, the solution of the model is embedded in the iterative process of diffusion sampling. Finally, a posterior sampling model based on the denoising diffusion probabilistic model for LSSR task is obtained to achieve a good balance between denoising and SR capabilities by combining explicit and implicit priors. Both simulated and real experiments show that DDFSRM outperforms other state-of-the-art SR methods in both qualitative and quantitative evaluation.