  Recently, the problem of blind image separation has been widely investigated,
especially the medical image denoise which is the main step in medical
diag-nosis. Removing the noise without affecting relevant features of the image
is the main goal. Sparse decomposition over redundant dictionaries become of
the most used approaches to solve this problem. NMF codes naturally favor
sparse, parts-based representations. In sparse representation, signals
represented as a linear combination of a redundant dictionary atoms. In this
paper, we propose an algorithm based on sparse representation over the
redundant dictionary and Non-Negative Matrix Factorization (N-NMF). The
algorithm initializes a dic-tionary based on training samples constructed from
noised image, then it searches for the best representation for the source by
using the approximate matching pursuit (AMP). The proposed N-NMF gives a better
reconstruction of an image from denoised one. We have compared our numerical
results with different image denoising techniques and we have found the
performance of the proposed technique is promising. Keywords: Image denoising,
sparse representation, dictionary learning, matching pursuit, non-negative
matrix factorization.
