Synthetic and Real-World Image Restoration with Controlled Vision-Language Models

This work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild, addressing the problem of diffusion models failing to recover high-quality outputs when applied to real-world scenarios with unknown, complex, out-of-distribution degradations.