  Medical image acquisition is often intervented by unwanted noise that
corrupts the information content. This paper introduces an unsupervised medical
image denoising technique that learns noise characteristics from the available
images and constructs denoised images. It comprises of two blocks of data
processing, viz., patch-based dictionaries that indirectly learn the noise and
residual learning (RL) that directly learns the noise. The model is generalized
to account for both 2D and 3D images considering different medical imaging
instruments. The images are considered one-by-one from the stack of MRI/CT
images as well as the entire stack is considered, and decomposed into
overlapping image/volume patches. These patches are given to the patch-based
dictionary learning to learn noise characteristics via sparse representation
while given to the RL part to directly learn the noise properties. K-singular
value decomposition (K-SVD) algorithm for sparse representation is used for
training patch-based dictionaries. On the other hand, residue in the patches is
trained using the proposed deep residue network. Iterating on these two parts,
an optimum noise characterization for each image/volume patch is captured and
in turn it is subtracted from the available respective image/volume patch. The
obtained denoised image/volume patches are finally assembled to a denoised
image or 3D stack. We provide an analysis of the proposed approach with other
approaches. Experiments on MRI/CT datasets are run on a GPU-based supercomputer
and the comparative results show that the proposed algorithm preserves the
critical information in the images as well as improves the visual quality of
the images.
