A Study on Clustering for Clustering Based Image De-noising
Journal Title: Journal of Information Systems and Telecommunication - Year 2014, Vol 2, Issue 4
Abstract
In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? The second, what data clustering method is suitable for de-noising? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. Experimental results show that our dictionary learning framework outperforms its competitors in terms of de-noising performance and execution time.
Authors and Affiliations
Hossein Bakhshi Golestani, Mohsen Joneidi, Mostafa Sadeghii
Keywords
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- EP ID EP185869
- DOI 10.7508/jist.2014.04.001
- Views 194
- Downloads 0
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