Article


Article Code : 13930804151442806(DOI : 10.7508/jist.2014.04.001)

Article Title : A Study on Clustering for Clustering Based Image De-noising

Journal Number : 8 Autumn 2014

Visited : 813

Files : 628 KB


List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Hossein Bakhshi Golestani h.b.golestani@gmail.com - M.Sc
2 Mohsen Joneidi joneidi@knights.ucf.edu - M.Sc
3 Mostafa Sadeghi m.saadeghii@gmail.com - M.Sc

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.