Multi-Domain Adaptation for Motion Deblurring

Motion deblurring is an important topic in the field of image enhancement, which has widespread applications including video surveillance, object detection, etc. Many algorithms are designed for motion deblurring and achieve remarkable performance. However, mainstream motion blur datasets are collected under normal weather and illuminance conditions, i.e., normal domain, ignoring their variations. As a result, current methods perform poorly in dynamic real-world scenes. To address these issues, we study the work in two aspects. First, we collect the real-world motion blur dataset with a well-designed collection device from various angles, focal lengths, and street scenes. Considering its domain is single, it is augmented via a Domain Transfer Strategy (DTS) to construct a Multi-Domain dataset (MD dataset), expanding the domains of the collected dataset. Second, we propose a Multi-Domain Adaptive Deblur Network (MDADNet) with two modules. The one is the Domain Adaptation (DA) module that exploits domain invariant features to stabilize the performance of the MDADNet in multiple domains. The other is the Meta Deblurring (MDB) module that employs the auxiliary branch to enhance the deblurring ability. It also enables the MDADNet to update parameters during the testing stage, improving the generalizations of the MDADNet. Extensive experimental results demonstrate that the MD-trained methods significantly strengthen the motion deblurring ability in multiple domains. Particularly, the proposed MDADNet achieves state-of-the-art performance on the MD dataset and public motion blur datasets.