Learning multiscale sparse representations for image and video restoration
Learning multiscale sparse representations for image and video restoration
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DOI：
10.1137/070697653
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发表时间：
2008-01-01
影响因子：
1.6
通讯作者：
Elad, Michael
中科院分区：
文献类型：
--
作者：
Mairal, Julien;Sapiro, Guillermo;Elad, Michael
本文提出了一个框架，学习彩色图像和视频的多尺度稀疏表示与过完备字典。文献[M.阿哈伦，M。Elad和A. M. Bruckstein，IEEE Trans. Signal Process.，54（2006），pp. 4311-4322]，将用于灰度图像表示的稀疏字典学习公式化为优化问题，经由正交匹配追踪（OMP）和奇异值分解（SVD）有效地解决。在这项工作之后，我们提出了一个多尺度的学习表示，通过使用一个有效的四叉树分解的学习字典和重叠的图像补丁。所提出的框架提供了一种替代预定义的字典，如小波，并示出导致国家的最先进的结果，在一些图像和视频增强和恢复应用。本文介绍了拟议的框架，并伴随着它的许多例子证明其实力。
This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD algorithm was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein, IEEE Trans. Signal Process., 54 (2006), pp. 4311-4322], formulating sparse dictionary learning for grayscale image representation as an optimization problem, efficiently solved via orthogonal matching pursuit (OMP) and singular value decomposition (SVD). Following this work, we propose a multiscale learned representation, obtained by using an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to predefined dictionaries such as wavelets and is shown to lead to state-of-the-art results in a number of image and video enhancement and restoration applications. This paper describes the proposed framework and accompanies it by numerous examples demonstrating its strength.