An alternating extragradient method for total variation-based image restoration from Poisson data
An alternating extragradient method for total variation-based image restoration from Poisson data
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DOI：
10.1088/0266-5611/27/9/095001
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发表时间：
2011-09-01
期刊：
影响因子：
2.1
通讯作者：
Ruggiero, V.
中科院分区：
文献类型：
--
作者：
Bonettini, S.;Ruggiero, V.
变分模型是从受泊松噪声影响的数据中恢复边缘保持图像的有效工具。本文论述了总变分和超曲面正则化结合Kullbach Leibler散度作为数据保真度函数。我们提出了一种迭代方法，基于交替extragradiation计划，这是能够解决，在一个数值稳定的方式，原始-对偶制定的全变分和超曲面正则化问题。该方法适合一般光滑鞍点问题，可以自适应地计算步长参数，从而在较温和的假设下证明了格式的收敛性。在数值实验中，我们着重研究了人工光顺参数对全变分和超曲面正则化的影响。一组图像去噪和去模糊问题的实验进行，以评估该平滑参数的稳定性所提出的方法和恢复图像的功能的影响。
Variational models are a valid tool for edge-preserving image restoration from data affected by Poisson noise. This paper deals with total variation and hypersurface regularization in combination with the Kullbach Leibler divergence as a data fidelity function. We propose an iterative method, based on an alternating extragradient scheme, which is able to solve, in a numerically stableway, the primal-dual formulation of both total variation and hypersurface regularization problems. In this method, tailored for general smooth saddle-point problems, the stepsize parameter can be adaptively computed so that the convergence of the scheme is proved under mild assumptions. In the numerical experience, we focus the attention on the artificial smoothing parameter that makes different the total variation and hypersurface regularization. A set of experiments on image denoising and deblurring problems is performed in order to evaluate the influence of this smoothing parameter on the stability of the proposed method and on the features of the restored images.