In this paper, we present an end-to-end unsupervised domain adaptation approach to image deblurring. This work focuses on learning and generalizing the complex latent space of the source domain and transferring the extracted information to the unlabeled target domain. While fully supervised image deblurring methods have achieved high accuracy on large-scale vision datasets, they are unable to well generalize well on a new test environment or a new domain. Therefore, in this work, we introduce a novel Bijective Maximum Likelihood loss for the unsupervised domain adaptation approach to image deblurring. We evaluate our proposed method on GoPro, RealBlur_J, RealBlur_R, and HIDE datasets. Through intensive experiments, we demonstrate our state-of-the-art performance on the standard benchmarks.
