Multichannel image deconvolution by total variation regularization

Tony F. Chan; Chiu-Kwong T. Wong

doi:10.1117/12.284189

24 October 1997 Multichannel image deconvolution by total variation regularization

Tony F. Chan, Chiu-Kwong T. Wong

Proceedings Volume 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII; (1997) https://doi.org/10.1117/12.284189
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

The total variational (TV) regularization method was first proposed for gray scale images and was extended 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 demonstrate the effectiveness of the TV regularization method. In this paper, we present some preliminary results on multichannel blind deconvolution with TV regularization.

Citation Download Citation

Tony F. Chan and Chiu-Kwong T. Wong "Multichannel image deconvolution by total variation regularization", Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); https://doi.org/10.1117/12.284189

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