D3BSR: Blind Super-Resolution via Diffusion-Based Disentangled Degradation Representation

Existing Blind Super-Resolution (BSR) methods are mostly trained on artificial synthetic degradation data pairs or rely on specific degradation priors, which lead to poor performance due to the trained degradation mismatch between other unknown complex degradations in real-world scenarios. To tackle this problem, we propose a novel Diffusion-based Disentangled Degradation representation method for BSR, dubbed D3BSR, which disentangles arbitrary unknown degradation into structure and texture degradations to enhance perception and fidelity quality individually. Specifically, the structure degradation is optimized by degradation distribution transition with a self-supervised collaborative learning strategy to recursively minimize the perception error. The texture degradation is restored through posterior sampling controlled by a fidelity coefficient to leverage rich texture priors encapsulated in a pre-trained diffusion model for preserving fidelity. The degraded image is super-resolved using an analytical solution with the pseudo-inverse of the structural and texture degradation, which achieves a controllable trade-off between perception and fidelity and does not rely on any degradation priors or extra-supervised training. Extensive experiments on the nine heavily degraded synthetic and real-world natural and face datasets demonstrate that our D3BSR outperforms SOTA methods on the diverse metrics in reconstruction faithfulness and perceptual quality.