General choice of the regularization functional in regularized image restoration
General choice of the regularization functional in regularized image restoration
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DOI：
10.1109/83.382494
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发表时间：
1995-05
期刊：
影响因子：
--
通讯作者：
M. Kang;A. Katsaggelos
中科院分区：
文献类型：
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作者：
M. Kang;A. Katsaggelos
正则化参数的确定是正则化图像恢复中的一个重要问题，因为它控制着对数据的保真度和解的光滑性之间的权衡。在确定这一参数方面，已经制定了一些办法。在本文中，采用了一种新的范式，根据该范式，所需的先验信息是从上一个迭代步骤的可用数据中提取的，即，在每一步的部分恢复的图像。我们建议使用一个正则化功能，而不是一个恒定的正则化参数。研究了正则化泛函应满足的性质，并给出了正则化泛函的两种具体形式。提出了一种迭代算法来获得恢复图像。正则化函数的定义方面的恢复图像在每个迭代步骤，因此允许同时确定其值和退化图像的恢复。这两个建议的迭代自适应正则化泛函的结果与全局最小值的平滑功能，使其迭代优化不依赖于初始条件。算法的收敛性和实验结果。
The determination of the regularization parameter is an important issue in regularized image restoration, since it controls the trade-off between fidelity to the data and smoothness of the solution. A number of approaches have been developed in determining this parameter. In this paper, a new paradigm is adopted, according to which the required prior information is extracted from the available data at the previous iteration step, i.e., the partially restored image at each step. We propose the use of a regularization functional instead of a constant regularization parameter. The properties such a regularization functional should satisfy are investigated, and two specific forms of it are proposed. An iterative algorithm is proposed for obtaining a restored image. The regularization functional is defined in terms of the restored image at each iteration step, therefore allowing for the simultaneous determination of its value and the restoration of the degraded image. Both proposed iteration adaptive regularization functionals are shown to result in a smoothing functional with a global minimum, so that its iterative optimization does not depend on the initial conditions. The convergence of the algorithm is established and experimental results are shown.