Multichannel image deconvolution by total variation regularization

Tony F. Chan*, C. K. Wong

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


The total variational (TV) regularization method was first proposed in14 for gray scale images and was extended in1 for vector valued images. In this work, we apply the TV regularization method to solve the multichannel image deconvolution problem. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images. In this paper, a fast iterative method is developed to solve the deconvolution problem. Our method involves solving linear systems and the conjugate gradient method is applied in which Fourier transform type preconditioners are used to speed up the convergence rate. Numerical experiments will demonstrate the effectiveness of the TV regularization method. In this paper, we will present some preliminary results on multichannel blind deconvolution with TV regularization.

Original languageEnglish (US)
Pages (from-to)358-366
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1997
Externally publishedYes
EventAdvanced Signal Processing: Algorithms, Architectures and Implementations VII - San Diego, CA, United States
Duration: Jul 28 1997Jul 30 1997


  • Intra and inter-channel degradation
  • Multichannel image restoration
  • Preconditioned conjugate gradient method
  • Total variation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Multichannel image deconvolution by total variation regularization'. Together they form a unique fingerprint.

Cite this