  In this paper, the problem of de-noising of an image contaminated with
additive white Gaussian noise (AWGN) is studied. This subject has been
continued to be 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. Noting that the type of clustering plays an important role
in clustering-based de-noising methods, we address two questions about the
clustering. First, which parts of data should be considered for clustering?
Second, what data clustering method is suitable for de-noising? 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 traditional dictionary learning methods such as
K-SVD.
