Generation Diffusion Degradation: Simple and Efficient Design for Blind Super-Resolution

In the domain of computer vision, blind super-resolution is a key area focused on generating high-resolution images with enhanced visual quality from low-resolution counterparts affected by indeterminate degradation factors. This area is primarily advanced through self-supervised learning techniques utilizing GANs. Despite their prominence, GAN-based methods encounter challenges including unstable training dynamics and limited diversity, compounded by the intricate necessity to configure degradation models to mimic various blur effects and noise types. Lately, denoising diffusion models have shown promising results in image restoration, yet their sampling efficiency constraints impede their deployment in real-time scenarios. This study introduces the Generation Diffusion Degradation (GDD) model, a novel and efficient technique for replicating image degradation by applying random Gaussian noise in a sequential manner via a parametric Markov chain, followed by a progressive reconstruction of the initial image through a U-net-based noise predictor. This method adeptly mirrors the inherent degradation distribution observed in actual degraded images. Furthermore, we present an innovative training strategy that utilizes a composite loss function to train the GDD model, ensuring stable training, improving the authenticity of the generated degraded images, and precisely reflecting the degradation patterns of target images. Extensive experimental analyses underscore the superior performance of the proposed GDD model, both in objective metrics and subjective visual quality. The code is available at https://github.com/lgylab/GDD.