L0-Regularized Intensity and Gradient Prior for Deblurring Text Images and beyond

Jinshan Pan, Zhe Hu, Zhixun Su, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

194 Citations (Scopus)


We propose a simple yet effective L0 -regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is based on distinctive properties of text images, with which we develop an efficient optimization algorithm to generate reliable intermediate results for kernel estimation. The proposed algorithm does not require any heuristic edge selection methods, which are critical to the state-of-the-art edge-based deblurring methods. We discuss the relationship with other edge-based deblurring methods and present how to select salient edges more principally. For the final latent image restoration step, we present an effective method to remove artifacts for better deblurred results. We show the proposed algorithm can be extended to deblur natural images with complex scenes and low illumination, as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art image deblurring methods.

Original languageEnglish
Article number7448477
Pages (from-to)342-355
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number2
Publication statusPublished - 2017 Feb 1

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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