Total variation blind deconvolution

IEEE Transactions on Image ProcessingPublished 1 March 1998

Tony F. Chan, Chiu-Kwong Wong

DOI10.1109/83.661187

Source URL

Citations1,204

SJR quartileQ1

SJR score2.50

SNIP3.41

TL;DR

A blind deconvolution algorithm based on the total variational (TV) minimization method proposed is presented, and it is remarked that psf's without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.

Abstract

In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images 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.

Keywords

Computer ScienceEngineering

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