Image Deblurring via Enhanced Low-Rank Prior

Wenqi Ren, Xiaochun Cao, Jinshan Pan, Xiaojie Guo, Wangmeng Zuo, Ming Hsuan Yang

Research output: Contribution to journalArticle

77 Citations (Scopus)


Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.

Original languageEnglish
Article number7473901
Pages (from-to)3426-3437
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number7
Publication statusPublished - 2016 Jul

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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