A non-local regularization strategy for image deconvolution
A non-local regularization strategy for image deconvolution
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DOI：
10.1016/j.patrec.2008.08.004
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发表时间：
2008-12-01
影响因子：
5.1
通讯作者：
Mignotte, Max
中科院分区：
文献类型：
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作者：
Mignotte, Max
在本文中，我们提出了一种贝叶斯框架下的非均匀复原(去卷积)模型，该模型利用了Buade等人最近提出的具有吸引力的自然图像模型中的非参数自适应先验分布。[Buade，A.，Coll，B.，Morel，J.-M.，2005.对图像去噪算法进行了综述，并提出了一种新的去噪算法。暹罗多尺度模型。西穆尔。(暹罗光盘。J.)，4(2)。490-530]用于纯去噪应用。这表示可接受的恢复解决方案可能是表现出高度冗余度的图像。换句话说，该先验将支持具有相似像素邻域配置的解决方案(即，恢复的图像)。为了使这种恢复是无监督的，我们采用了L曲线方法(最初定义为Tikhonov型正则化)来估计我们的正则化参数。本文报道的实验说明了这种方法的潜力，并证明了这种正则化恢复策略的性能与基准测试中使用经典局部先验(或正则化项)的现有最佳最先进方法相比具有竞争力。(C)2008爱思唯尔B.V.保留所有权利。
In this paper, we propose an inhomogeneous restoration (deconvolution) model under the Bayesian framework exploiting a non-parametric adaptive prior distribution derived from the appealing and natural image model recently proposed by Buades et al. [Buades, A., Coll, B., Morel, J.-M., 2005. A review of image denoising algorithms, with a new one. SIAM Multiscale Model. Simul. (SIAM Interdisc. J.), 4(2). 490-530] for pure denoising applications. This prior expresses that acceptable restored solutions are likely the images exhibiting a high degree of redundancy. In other words, this prior will favor solutions (i.e., restored images) with similar pixel neighborhood configurations. In order to render this restoration unsupervised, we have adapted the L-curve approach (originally defined for Tikhonov-type regularizations), for estimating Our regularization parameter. The experiments herein reported illustrate the potential of this approach and demonstrate that this regularized restoration strategy performs competitively compared to the best existing state-of-the art methods employing classical local priors (or regularization terms) in benchmark tests. (C) 2008 Elsevier B.V. All rights reserved.