DriftRec: Adapting diffusion models to blind JPEG restoration
This addresses image quality degradation in JPEG compression for applications like photography and online media, offering a novel adaptation of diffusion models for restoration tasks.
The paper tackles blind JPEG restoration at high compression levels by adapting diffusion models, resulting in DriftRec, which escapes blurry outputs and recovers clean image distributions more faithfully than baselines and state-of-the-art methods, requiring only clean/corrupted image pairs and no corruption knowledge.
In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels. We propose an elegant modification of the forward stochastic differential equation of diffusion models to adapt them to this restoration task and name our method DriftRec. Comparing DriftRec against an $L_2$ regression baseline with the same network architecture and state-of-the-art techniques for JPEG restoration, we show that our approach can escape the tendency of other methods to generate blurry images, and recovers the distribution of clean images significantly more faithfully. For this, only a dataset of clean/corrupted image pairs and no knowledge about the corruption operation is required, enabling wider applicability to other restoration tasks. In contrast to other conditional and unconditional diffusion models, we utilize the idea that the distributions of clean and corrupted images are much closer to each other than each is to the usual Gaussian prior of the reverse process in diffusion models. Our approach therefore requires only low levels of added noise and needs comparatively few sampling steps even without further optimizations. We show that DriftRec naturally generalizes to realistic and difficult scenarios such as unaligned double JPEG compression and blind restoration of JPEGs found online, without having encountered such examples during training.