Second-order cone programming methods for total variation-based image restoration
Second-order cone programming methods for total variation-based image restoration
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DOI：
10.1137/040608982
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发表时间：
2005-01-01
影响因子：
3.1
通讯作者：
Yin, WT
中科院分区：
文献类型：
--
作者：
Goldfarb, D;Yin, WT
在本文中，我们提出了基于Rudin， Osher和Fatemi （ROF）的总变差（TV）最小化框架的图像恢复优化算法。我们的方法将TV最小化表述为一个二阶锥程序，然后通过内点算法求解，该算法在实践（使用嵌套解剖和域分解）和理论上（即，它们在多项式时间内获得解）都很有效。除了原始的ROF最小化模型之外，我们还展示了如何将我们的方法应用于其他电视模型，包括那些无法通过基于pde的方法解决的模型。在不同图像集上的数值结果说明了我们方法的有效性。
In this paper we present optimization algorithms for image restoration based on the total variation (TV) minimization framework of Rudin, Osher, and Fatemi ( ROF). Our approach formulates TV minimization as a second- order cone program which is then solved by interior-point algorithms that are efficient both in practice ( using nested dissection and domain decomposition) and in theory ( i. e., they obtain solutions in polynomial time). In addition to the original ROF minimization model, we show how to apply our approach to other TV models, including ones that are not solvable by PDE-based methods. Numerical results on a varied set of images are presented to illustrate the effectiveness of our approach.