Un-supervised learning for blind image deconvolution via Monte-Carlo sampling

Deep learning has been a powerful tool for solving many inverse imaging problems. The majority of existing deep-learning-based solutions are supervised on an external dataset with many blurred/latent image pairs. Recently, there has been an increasing interest on developing dataset-free deep learning methods for image recovery without any prerequisite on external training dataset, including blind deconvolution. This paper aims at developing an un-supervised learning method for blind image deconvolution, which does not call any training sample yet provides very competitive performance. Based on the re-parametrization of latent image using a deep network with random weights, this paper proposed to approximate the maximum-a posteriori estimator of the blur kernel using the Monte-Carlo (MC) sampling method. The MC sampling is efficiently implemented by using dropout and random noise layer, which does not require conjugate model as traditional variational inference does. Extensive experiments on popular benchmark datasets for blind image deconvolution showed that the proposed method not only outperformed existing non-learning methods, but also noticeably outperformed existing deep learning methods, including both supervised and un-supervised ones.