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Deblurring Face Images with Exemplars

  • Jinshan Pan
  • Zhe Hu
  • Zhixun Su
  • Ming-Hsuan Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)

Abstract

The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.

Keywords

Face Image Kernel Estimation Error Ratio Ringing Artifact Blur Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jinshan Pan
    • 1
  • Zhe Hu
    • 2
  • Zhixun Su
    • 1
  • Ming-Hsuan Yang
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.University of CaliforniaMercedUSA

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