Total variation blind deconvolution

Tony F. Chan*, Chiu Kwong Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

849 Scopus citations

Abstract

In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed in [11]. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images [11] as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM) implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (psf). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the psf can be recovered under the presence of high noise level. Finally, we remark that psf's without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.

Original languageEnglish (US)
Pages (from-to)370-375
Number of pages6
JournalIEEE Transactions on Image Processing
Volume7
Issue number3
DOIs
StatePublished - Dec 1 1998
Externally publishedYes

Keywords

  • Image reconstruction
  • Iterative methods
  • Numerical methods
  • Piecewise linear techniques
  • Variational techniques, Blind deconvolution algorithms
  • Conjugate gradient method
  • Point spread functions, Algorithms

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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