On the Role of Sparse and Redundant Representations in Image Processing
On the Role of Sparse and Redundant Representations in Image Processing
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DOI：
10.1109/jproc.2009.2037655
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发表时间：
2010-06-01
影响因子：
20.6
通讯作者：
Ma, Yi
中科院分区：
文献类型：
--
作者：
Elad, Michael;Figueiredo, Mario A. T.;Ma, Yi
在过去的几十年中，图像处理取得的进展可以归因于更好的图像内容建模和这些模型在相关应用中的明智部署。模型的这条路径从简单的l（2）-范数平滑跨越到鲁棒的，因此边缘保持的平滑度量（e. G.总变差），直到最近的模型，采用稀疏和冗余表示。在本文中，我们回顾了这一最新模型在图像处理中的作用，它的基本原理，以及与之相关的模型。事实证明，图像处理领域是最近在稀疏和冗余表示的理论和实践方面取得的进展的主要受益者之一。我们讨论了如何使用这些工具进行各种图像处理任务，并提出了几个应用程序，其中获得了最先进的结果。
Much of the progress made in image processing in the past decades can be attributed to better modeling of image content and a wise deployment of these models in relevant applications. This path of models spans from the simple l(2)-norm smoothness through robust, thus edge preserving, measures of smoothness (e. g. total variation), and until the very recent models that employ sparse and redundant representations. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. We discuss ways to employ these tools for various image-processing tasks and present several applications in which state-of-the-art results are obtained.