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

Ji Li, YUESONG NAN, Hui Ji

Abstract: Deep learning has been a powerful tool for solving many inverse imaging problems. The majority of existing deep-learning-based solutions are super- vised on an external dataset with many blurred/latent image pairs. Recently, there has been an increasing interest on developing dataset-free deep learn- ing 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. Exten- sive 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.