Motion Blur Kernel Estimation via Deep Learning

Xiangyu Xu, Jinshan Pan, Yu Jin Zhang, Ming Hsuan Yang

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering-based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges. Facilitated by the learned sharp edges, the proposed deblurring algorithm does not require any coarse-to-fine strategy or edge selection, thereby significantly simplifying kernel estimation and reducing computation load. Extensive experimental results on challenging blurry images demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of visual quality and run-time.

Original languageEnglish
Article number8039224
Pages (from-to)194-205
Number of pages12
JournalIEEE Transactions on Image Processing
Volume27
Issue number1
DOIs
Publication statusPublished - 2018 Jan

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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