Mendeley
TY  _  JOUR
ID  -  13930804151442806
TI  -  A Study on Clustering for Clustering Based Image De-noising
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Bakhshi Golestani Hossein
AU  -  Joneidi Mohsen
AU  -  Sadeghi Mostafa
AD  -  Sharif
AD  -  Sharif
AD  -  Sharif
Y1  -  2014
PY  -  2014
VL  -  8
IS  -  
SP  -  1
EP  -  10
KW  -  Image de-noising
KW  -  data clustering
KW  -  dictionary learning
KW  -  histogram equalization and sparse representation
DO  -  
N2  -  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.
UR  - rimag.ir/en/Article/14856
L1  - rimag.ir/en/Article/Download/14856
ER  -  
