Sparse Representation for Color Image Restoration (PREPRINT)
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, and shown to perform very well for various gray-scale image processing tasks. In this paper we address the problem of learning dictionaries for color images and extend the K-SVD-based gray-scale 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2006
- Accession Number
- ADA478604
Entities
People
- Guillermo Sapiro
- Julien Mairal
- Michael Elad
Organizations
- University of Minnesota