Image Recovery by Decomposition with Component-Wise Regularization
Image Recovery by Decomposition with Component-Wise Regularization
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DOI：
10.1587/transfun.e95.a.2470
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Shunsuke Ono;T. Miyata;I. Yamada;K. Yamaoka
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文献类型：
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作者：
Shunsuke Ono;T. Miyata;I. Yamada;K. Yamaoka
解决图像恢复问题需要使用一些有效的正则化基于先验信息相对于未知的原始图像。自然地，我们可以假设图像被建模为光滑、边缘和纹理组件的总和。为了获得高质量的恢复图像，需要对每个单独的组件进行适当的正则化。本文提出了一种同时进行分解和恢复的图像恢复技术。我们将图像恢复描述为一个非光滑凸优化问题，并设计了一种基于交替方向乘法器（ADMM）的迭代方案来有效地逼近其全局最小值。实验结果表明，所提出的图像恢复技术优于目前最先进的方法。关键词：图像恢复，分解，正则化，稀疏性，凸优化，交替方向乘法器（ADMM）
Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method. key words: image recovery, decomposition, regularization, sparsity, convex optimization, alternating direction method of multipliers (ADMM)