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首页> 外文期刊>Journal of visual communication & image representation >Nonconvex higher-order regularization based Rician noise removal with spatially adaptive parameters
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Nonconvex higher-order regularization based Rician noise removal with spatially adaptive parameters

机译：具有空间自适应参数的基于非凸高阶正则化的Rician噪声去除

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摘要

In this article, we introduce a class of variational models for the restoration of images that are polluted by Rician noise and/or blurring. The novel energy functional consists of a convex fidelity term and a nonconvex higher-order regularization term. The regularization term enables us to efficiently denoise piecewise smooth images, by alleviating the staircasing effects that appear in total variation based models, and to preserve details and edges. Furthermore, we incorporate our nonconvex higher-order model with spatially adaptive regularization parameters; this further improves restoration results by sufficiently smoothing homogeneous regions while conserving edge parts. To handle the nonconvexity and nonsmoothness of our models, we adopt the iteratively reweighted l(1) algorithm, and the alternating direction method of multipliers. This results in fast and efficient algorithms for solving our proposed models. Numerical experiments demonstrate the superiority of our models over the state-of-the-art methods, as well as the effectiveness of our algorithms. (C) 2015 Elsevier Inc. All rights reserved.
机译：在本文中，我们介绍了一类变分模型，用于恢复受Rician噪声和/或模糊污染的图像。新颖的能量泛函由凸保真度项和非凸高阶正则化项组成。正则化项使我们能够通过减轻基于总体变化的模型中出现的阶梯效应来有效地对分段平滑图像进行去噪，并保留细节和边缘。此外，我们将非凸高阶模型与空间自适应正则化参数结合在一起；通过在保持边缘部分的同时充分平滑均匀区域，进一步改善了修复效果。为了处理模型的非凸性和非光滑性，我们采用迭代加权的l（1）算法和乘数的交替方向方法。这样就产生了快速有效的算法来求解我们提出的模型。数值实验证明了我们的模型优于最新方法的优越性，以及我们算法的有效性。 （C）2015 Elsevier Inc.保留所有权利。

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