Learning to Deblur Images with Exemplars

Jinshan Pan, Wenqi Ren, Zhe Hu, Ming Hsuan Yang

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Human faces are one interesting object class with numerous applications. While significant progress has been made in the generic deblurring problem, existing methods are less effective for blurry face images. The success of the state-of-the-art image deblurring algorithms stems mainly from implicit or explicit restoration of salient edges for kernel estimation. However, existing methods are less effective as only few edges can be restored from blurry face images for kernel estimation. In this paper, we address the problem of deblurring face images by exploiting facial structures. We propose a deblurring algorithm based on an exemplar dataset without using coarse-to-fine strategies or heuristic edge selections. In addition, we develop a convolutional neural network to restore sharp edges from blurry images for deblurring. Extensive experiments against the state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. In addition, we show that the proposed algorithms can be applied to image deblurring for other object classes.

Original languageEnglish
Article number8353166
Pages (from-to)1412-1425
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Volume41
Issue number6
DOIs
Publication statusPublished - 2019 Jun 1

All Science Journal Classification (ASJC) codes

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

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