Primal-dual algorithms for total variation based image restoration under Poisson noise
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 consider the problem of restoring images corrupted by Poisson noise. Under the framework of maximum a posteriori estimator, the problem can be converted into a minimization problem where the objective function is composed of a Kullback-Leibler (KL)-divergence term for the Poisson noise and a total variation (TV) regularization term. Due to the logarithm function in the KL-divergence term, the non-differentiability of TV term and the positivity constraint on the images, it is not easy to design stable and efficiency algorithm for the problem. Recently, many researchers proposed to solve the problem by alternating direction method of multipliers (ADMM). Since the approach introduces some auxiliary variables and requires the solution of some linear systems, the iterative procedure can be complicated. Here we formulate the problem as two new constrained minimax problems and solve them by Chambolle-Pock's first order primal-dual approach. The convergence of our approach is guaranteed by their theory. Comparing with ADMM approaches, our approach requires about half of the auxiliary variables and is matrix-inversion free. Numerical results show that our proposed algorithms are efficient and outperform the ADMM approach.
著者Wen YW, Chan RH, Zeng TY
期刊名稱Science China Mathematics
詳細描述To ORKTS: First online: 05 November 2015
出版年份2016
月份1
日期1
卷號59
期次1
出版社Springer Verlag (Germany)
頁次141 - 160
國際標準期刊號1674-7283
電子國際標準期刊號1869-1862
語言英式英語
關鍵詞alternating direction method of multipliers (ADMM); image restoration; minimax problem; Poisson noise; primal-dual; total variation (TV)
Web of Science 學科類別Mathematics; Mathematics, Applied

上次更新時間 2020-26-10 於 01:08