Non-blind image deconvolution using deep dual-pathway rectifier neural network

Abstract

Recently deep neural networks have been successfully used for natural image deconvolution. Whereas the existing methods usually involve an inversion of the blur followed by a denoising step. In this paper we propose a pure learning approach to learn a mapping from a blurred patch to a clean patch directly with a deep dual-pathway rectifier neural network. The experimental results show that our approach outperform the state-of-the-art methods on non-blind image deconvolution within reasonable training time. By analyzing the learned representations, we empirically show that our model works by efficiently detecting the blurry input patterns and then reconstructing the clean patch with the corresponding dictionary atoms.