Partial deconvolution with inaccurate blur kernel

Dongwei Ren, Wangmeng Zuo, David Zhang, Jun Xu, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: 1) a partial map in the Fourier domain for modeling kernel estimation error, and 2) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Original languageEnglish
Article number8071032
Pages (from-to)511-524
Number of pages14
JournalIEEE Transactions on Image Processing
Volume27
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Blind deconvolution
  • Blur kernel estimation
  • E-M algorithm
  • Image deblurring

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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