Papers › Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization
Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization
Ji Li, Weixi Wang, Yuesong Nan, Hui Ji
When taking a picture, any camera shake during the shutter time can result in a blurred image. Recovering a sharp image from the one blurred by camera shake is a challenging yet important problem. Most existing deep learning methods use supervised learning to train a deep neural network (DNN) on a dataset of many pairs of blurred/latent images. In contrast, this paper presents a dataset-free deep learning method for removing uniform and non-uniform blur effects from images of static scenes. Our method involves a DNN-based re-parametrization of the latent image, and we propose a Monte Carlo Expectation Maximization (MCEM) approach to train the DNN without requiring any latent images. The Monte Carlo simulation is implemented via Langevin dynamics. Experiments showed that the proposed method outperforms existing methods significantly in removing motion blur from images of static scenes.
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