  Recently, research on denoising diffusion models has expanded its application
to the field of image restoration. Traditional diffusion-based image
restoration methods utilize degraded images as conditional input to effectively
guide the reverse generation process, without modifying the original denoising
diffusion process. However, since the degraded images already include
low-frequency information, starting from Gaussian white noise will result in
increased sampling steps. We propose Resfusion, a general framework that
incorporates the residual term into the diffusion forward process, starting the
reverse process directly from the noisy degraded images. The form of our
inference process is consistent with the DDPM. We introduced a weighted
residual noise, named resnoise, as the prediction target and explicitly provide
the quantitative relationship between the residual term and the noise term in
resnoise. By leveraging a smooth equivalence transformation, Resfusion
determine the optimal acceleration step and maintains the integrity of existing
noise schedules, unifying the training and inference processes. The
experimental results demonstrate that Resfusion exhibits competitive
performance on ISTD dataset, LOL dataset and Raindrop dataset with only five
sampling steps. Furthermore, Resfusion can be easily applied to image
generation and emerges with strong versatility. Our code and model are
available at https://github.com/nkicsl/Resfusion.
