Deep non-blind deconvolution via generalized low-rank approximation

Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wangmeng Zuo, Wei Liu, Ming Hsuan Yang

Research output: Contribution to journalConference articlepeer-review

21 Citations (Scopus)

Abstract

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. Specifically, we first compute a generalized low-rank approximation to a large number of blur kernels, and then use separable filters to initialize the convolutional parameters in the network. Our analysis shows that the estimated decomposed matrices contain the most essential information of an input kernel, which ensures the proposed network to handle various blurs in a unified framework and generate high-quality deblurring results. Experimental results on benchmark datasets with noisy and saturated pixels demonstrate that the proposed deconvolution approach relying on generalized low-rank approximation performs favorably against the state-of-the-arts.

Original languageEnglish
Pages (from-to)297-307
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2018-December
Publication statusPublished - 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: 2018 Dec 22018 Dec 8

Bibliographical note

Funding Information:
This work is supported in part by the National Key R&D Program of China (Grant No. 2016YFC0801004), National Natural Science Foundation of China (No. 61802403, U1605252, U1736219, 61650202), Beijing Natural Science Foundation (No.4172068). W. Ren is supported in part by the Open Projects Program of National Laboratory of Pattern Recognition and the CCF-Tencent Open Fund. J. Pan is supported in part by the Natural Science Foundation of Jiangsu Province (No. BK20180471). M.-H. Yang is supported in part by the NSF CAREER Grant #1149783 and gifts from and NVIDIA.

Publisher Copyright:
© 2018 Curran Associates Inc..All rights reserved.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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