Photo-Realistic Image Restoration in the Wild with Controlled
  Vision-Language Models
Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön
Though diffusion models have been successfully applied to various image
restoration (IR) tasks, their performance is sensitive to the choice of
training datasets. Typically, diffusion models trained in specific datasets
fail to recover images that have out-of-distribution degradations. To address
this problem, this work leverages a capable vision-language model and a
synthetic degradation pipeline to learn image restoration in the wild (wild
IR). More specifically, all low-quality images are simulated with a synthetic
degradation pipeline that contains multiple common degradations such as blur,
resize, noise, and JPEG compression. Then we introduce robust training for a
degradation-aware CLIP model to extract enriched image content features to
assist high-quality image restoration. Our base diffusion model is the image
restoration SDE (IR-SDE). Built upon it, we further present a posterior
sampling strategy for fast noise-free image generation. We evaluate our model
on both synthetic and real-world degradation datasets. Moreover, experiments on
the unified image restoration task illustrate that the proposed posterior
sampling improves image generation quality for various degradations.