Learning a discriminative prior for blind image deblurring

Lerenhan Li, Jinshan Pan, Wei Sheng Lai, Changxin Gao, Nong Sang, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review


We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred ones. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domainspecific image deblurring approaches.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2018 Mar 8

All Science Journal Classification (ASJC) codes

  • General

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