  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. Local methods suggested in
recent years, have obtained better results than global methods. However by more
intelligent training in such a way that first, important data is more effective
for training, second, clustering in such way that training blocks lie in
low-rank subspaces, we can design a dictionary applicable for image de-noising
and obtain results near the state of the art local methods. 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? and 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. In addition to our framework, 7 popular
dictionary learning methods are simulated and compared. The results are
compared based on two major factors: (1) de-noising performance and (2)
execution time. Experimental results show that our dictionary learning
framework outperforms its competitors in terms of both factors.
