Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise

Understand

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.