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.

该研究提出了一种基于动态参考建模范式的高效 All-In-One 图像复原方法，该方法采用一对相互纠缠的基于参考的最大后验概率推断，实现任务自适应的降噪建模和模型图像还原，同时提供了显式的灵活性和可解释性。