UID-GAN: Unsupervised Image Deblurring via Disentangled Representations

Recent advances in deep convolutional neural networks (DCNNs) and generative adversarial networks (GANs) have significantly improved the performance of single image blind deblurring algorithms. However, most of the existing algorithms require paired training data. In this paper, we present an unsupervised method for single-image deblurring without paired training images. We introduce a disentangled framework to split the content and blur features of a blurred image, which yields improved deblurring performance. To handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the restored results match the original images. We also add a perceptual loss to further mitigate the artifacts. For natural image deblurring, we introduce a color loss to reduce color distortions in outputs. Extensive experiments on both domain-specific and natural image deblurring show the proposed method achieves competitive results compared to recent state-of-the-art deblurring approaches.