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

• URL: http://arxiv.org/abs/2307.07688v2
• Date: Thu, 30 Nov 2023 07:19:34 GMT
• Title: DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration
• Authors: Yuanshuo Cheng, Mingwen Shao, Yecong Wan, Chao Wang
• Abstract summary: This work proposes an efficient Dynamic Reference Modeling paradigm (DRM-IR) DRM-IR consists of task-adaptive degradation modeling and model-based image restoring. Experiments on multiple benchmark datasets show that our DRM-IR achieves state-of-the-art in All-In-One IR.
• Score: 5.573836220587265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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