  We present MoE-DiffIR, an innovative universal compressed image restoration
(CIR) method with task-customized diffusion priors. This intends to handle two
pivotal challenges in the existing CIR methods: (i) lacking adaptability and
universality for different image codecs, e.g., JPEG and WebP; (ii) poor texture
generation capability, particularly at low bitrates. Specifically, our
MoE-DiffIR develops the powerful mixture-of-experts (MoE) prompt module, where
some basic prompts cooperate to excavate the task-customized diffusion priors
from Stable Diffusion (SD) for each compression task. Moreover, the
degradation-aware routing mechanism is proposed to enable the flexible
assignment of basic prompts. To activate and reuse the cross-modality
generation prior of SD, we design the visual-to-text adapter for MoE-DiffIR,
which aims to adapt the embedding of low-quality images from the visual domain
to the textual domain as the textual guidance for SD, enabling more consistent
and reasonable texture generation. We also construct one comprehensive
benchmark dataset for universal CIR, covering 21 types of degradations from 7
popular traditional and learned codecs. Extensive experiments on universal CIR
have demonstrated the excellent robustness and texture restoration capability
of our proposed MoE-DiffIR. The project can be found at
https://renyulin-f.github.io/MoE-DiffIR.github.io/.
