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Existing All-In-One image restoration (IR) methods usually lack flexiblemodeling on various types of degradation, thus impeding the restorationperformance. To achieve All-In-One IR with higher task dexterity, this workproposes an efficient Dynamic Reference Modeling paradigm (DRM-IR), whichconsists of task-adaptive degradation modeling and model-based image restoring.Specifically, these two subtasks are formalized as a pair of entangledreference-based maximum a posteriori (MAP) inferences, which are optimizedsynchronously in an unfolding-based manner. With the two cascaded subtasks,DRM-IR first dynamically models the task-specific degradation based on areference image pair and further restores the image with the collecteddegradation statistics. Besides, to bridge the semantic gap between thereference and target degraded images, we further devise a Degradation PriorTransmitter (DPT) that restrains the instance-specific feature differences.DRM-IR explicitly provides superior flexibility for All-in-One IR while beinginterpretable. Extensive experiments on multiple benchmark datasets show thatour DRM-IR achieves state-of-the-art in All-In-One IR.

DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration