DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration
This work addresses the need for flexible and interpretable image restoration methods for applications in computer vision, though it appears incremental as it builds on existing unfolding-based approaches.
The paper tackles the problem of All-In-One image restoration by proposing DRM-IR, a dynamic reference modeling paradigm that adapts to various degradation types, achieving state-of-the-art performance on multiple benchmark datasets.
Existing All-In-One image restoration (IR) methods usually lack flexible modeling on various types of degradation, thus impeding the restoration performance. To achieve All-In-One IR with higher task dexterity, this work proposes an efficient Dynamic Reference Modeling paradigm (DRM-IR), which consists of task-adaptive degradation modeling and model-based image restoring. Specifically, these two subtasks are formalized as a pair of entangled reference-based maximum a posteriori (MAP) inferences, which are optimized synchronously in an unfolding-based manner. With the two cascaded subtasks, DRM-IR first dynamically models the task-specific degradation based on a reference image pair and further restores the image with the collected degradation statistics. Besides, to bridge the semantic gap between the reference and target degraded images, we further devise a Degradation Prior Transmitter (DPT) that restrains the instance-specific feature differences. DRM-IR explicitly provides superior flexibility for All-in-One IR while being interpretable. Extensive experiments on multiple benchmark datasets show that our DRM-IR achieves state-of-the-art in All-In-One IR.