Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation

NeurIPS 2018 Wenqi RenJiawei ZhangLin MaJinshan PanXiaochun CaoWangmeng ZuoWei LiuMing-Hsuan Yang

In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework. The proposed neural network is motivated by the low-rank property of pseudo-inverse kernels... (read more)

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