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2008

Sparse Representation for Color Image Restoration

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Sparse Representation for Color Image Restoration
Abstract--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 [1] 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 [2]. 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.
Julien Mairal, Michael Elad, Guillermo Sapiro
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TIP
Authors Julien Mairal, Michael Elad, Guillermo Sapiro
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