Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration

All-in-one image restoration aims to handle multiple degradation types using one model. We propose a simple pipeline for all-in-one blind image restoration to Restore Anything with Masks (RAM). We focus on the image content itself by utilizing the MIM to extract intrinsic image information rather than distinguishing degradation types like other methods. Our pipeline consists of two stages: masked image pre-training and fine-tuning with mask attribute conductance. We design a simple masking pre-training approach tailored to all-in-one image restoration, boosting networks to focus more on extracting image content priors from any degradation, which turns out to be a more balanced (between different restoration tasks) and stronger performance. To bridge the gap of input integrity while preserving learned image priors as much as possible, we selectively fine-tuned a small portion of the layers. Specifically, the importance of each layer is ranked by the proposed Mask Attribute Conductance (MAC), and the layers with higher contributions are selected for finetuning. Extensive quantitative and qualitative experiments demonstrate that our method achieves state-of-the-art performance. Our code and model will be released.