NONSTATIONARY ITERATED THRESHOLDING ALGORITHMS FOR IMAGE DEBLURRING
Publication in refereed journal

香港中文大學研究人員

引用次數
替代計量分析
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其它資訊
摘要We 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.
著者Huang J, Donatelli M, Chan R
期刊名稱Inverse Problems and Imaging
出版年份2013
月份8
日期1
卷號7
期次3
出版社AMER INST MATHEMATICAL SCIENCES
頁次717 - 736
國際標準期刊號1930-8337
電子國際標準期刊號1930-8345
語言英式英語
關鍵詞image deblurring; Iterated Tikhonov; linearized Bregman iteration; onstationary parameter sequence; soft thresholding
Web of Science 學科類別Mathematics; Mathematics, Applied; MATHEMATICS, APPLIED; Physics; Physics, Mathematical; PHYSICS, MATHEMATICAL

上次更新時間 2020-28-10 於 01:01