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Adapting the Diffusion Probabilistic Model (DPM) for direct imagesuper-resolution is wasteful, given that a simple Convolutional Neural Network(CNN) can recover the main low-frequency content. Therefore, we presentResDiff, a novel Diffusion Probabilistic Model based on Residual structure forSingle Image Super-Resolution (SISR). ResDiff utilizes a combination of a CNN,which restores primary low-frequency components, and a DPM, which predicts theresidual between the ground-truth image and the CNN-predicted image. Incontrast to the common diffusion-based methods that directly use LR images toguide the noise towards HR space, ResDiff utilizes the CNN's initial predictionto direct the noise towards the residual space between HR space andCNN-predicted space, which not only accelerates the generation process but alsoacquires superior sample quality. Additionally, a frequency-domain-based lossfunction for CNN is introduced to facilitate its restoration, and afrequency-domain guided diffusion is designed for DPM on behalf of predictinghigh-frequency details. The extensive experiments on multiple benchmarkdatasets demonstrate that ResDiff outperforms previous diffusion-based methodsin terms of shorter model convergence time, superior generation quality, andmore diverse samples.

ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution