Mairal Julien, Elad Michael, Sapiro Guillermo
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
IEEE Trans Image Process. 2008 Jan;17(1):53-69. doi: 10.1109/tip.2007.911828.
Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.
近年来，信号的稀疏表示引起了广泛关注。诸如图像之类的自然信号在冗余字典上允许稀疏分解这一假设，催生了用于处理此类数据源的高效算法。特别是，设计适用于图像的字典一直是一项重大挑战。K-SVD算法最近被提出用于此任务，并已证明在各种灰度图像处理任务中表现出色。在本文中，我们解决了为彩色图像学习字典的问题，并扩展了已有的基于K-SVD的灰度图像去噪算法。这项工作提出了处理非均匀噪声和缺失信息的方法，为彩色图像去噪、去马赛克和图像修复等应用中的最新成果铺平了道路，本文对此进行了论证。
