NONSTATIONARY ITERATED THRESHOLDING ALGORITHMS FOR IMAGE DEBLURRING
Publication in refereed journal


Times Cited
Web of Science16WOS source URL (as at 04/08/2020) Click here for the latest count
Altmetrics Information
.

Other information
AbstractWe propose iterative thresholding algorithms based on the iterated Tikhonov method for image deblurring problems. Our method is similar in idea to the modified linearized Bregman algorithm (MLBA) so is easy to implement. In order to obtain good restorations, MLBA requires an accurate estimate of the regularization parameter a which is hard to get in real applications. Based on previous results in iterated Tikhonov method, we design two nonstationary iterative thresholding algorithms which give near optimal results without estimating a. One of them is based on the iterative soft thresholding algorithm and the other is based on MLBA. We show that the nonstationary methods, if converge, will converge to the same minimizers of the stationary variants. Numerical results show that the accuracy and convergence of our nonstationary methods are very robust with respect to the changes in the parameters and the restoration results are comparable to those of MLBA with optimal a.
All Author(s) ListHuang J, Donatelli M, Chan R
Journal nameInverse Problems and Imaging
Year2013
Month8
Day1
Volume Number7
Issue Number3
PublisherAMER INST MATHEMATICAL SCIENCES
Pages717 - 736
ISSN1930-8337
eISSN1930-8345
LanguagesEnglish-United Kingdom
Keywordsimage deblurring; Iterated Tikhonov; linearized Bregman iteration; onstationary parameter sequence; soft thresholding
Web of Science Subject CategoriesMathematics; Mathematics, Applied; MATHEMATICS, APPLIED; Physics; Physics, Mathematical; PHYSICS, MATHEMATICAL

Last updated on 2020-05-08 at 04:43