Multichannel image deconvolution by total variation regularization

Tony F. Chan; C. K. Wong

doi:10.1117/12.284189

Multichannel image deconvolution by total variation regularization

Tony F. Chan^*, C. K. Wong

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

9 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	358-366
Number of pages	9
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	3162
DOIs	https://doi.org/10.1117/12.284189
State	Published - 1997
Externally published	Yes
Event	Advanced Signal Processing: Algorithms, Architectures and Implementations VII - San Diego, CA, United States Duration: Jul 28 1997 → Jul 30 1997

Keywords

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

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10.1117/12.284189

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@article{99f27f4638a54aa8964e57ab99765532,

title = "Multichannel image deconvolution by total variation regularization",

abstract = "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.",

keywords = "Intra and inter-channel degradation, Multichannel image restoration, Preconditioned conjugate gradient method, Total variation",

author = "Chan, {Tony F.} and Wong, {C. K.}",

year = "1997",

doi = "10.1117/12.284189",

language = "English (US)",

volume = "3162",

pages = "358--366",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Advanced Signal Processing: Algorithms, Architectures and Implementations VII ; Conference date: 28-07-1997 Through 30-07-1997",

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TY - JOUR

T1 - Multichannel image deconvolution by total variation regularization

AU - Chan, Tony F.

AU - Wong, C. K.

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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.

KW - Intra and inter-channel degradation

KW - Multichannel image restoration

KW - Preconditioned conjugate gradient method

KW - Total variation

UR - http://www.scopus.com/inward/record.url?scp=0031289914&partnerID=8YFLogxK

U2 - 10.1117/12.284189

DO - 10.1117/12.284189

M3 - Conference article

AN - SCOPUS:0031289914

SN - 0277-786X

VL - 3162

SP - 358

EP - 366

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Advanced Signal Processing: Algorithms, Architectures and Implementations VII

Y2 - 28 July 1997 through 30 July 1997

ER -

Multichannel image deconvolution by total variation regularization

Abstract

Keywords

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