New Methods for MRI Denoising based on Sparseness and Self-Similarity
This paper proposes two new methods for the three-dimensional denoising of
magnetic
resonance images that exploit the sparseness and self-similarity
properties of
the images. The proposed methods are based on a three-dimensional
moving-window
discrete cosine transform hard thresholding and a three-dimensional
rotationally invariant version of the well-known nonlocal means filter.
The
proposed approaches were compared with related state-of-the-art methods
and produced very competitive
results. Both methods run in less than a minute, making them usable in
most
clinical and research settings.
Example results for 15% of Rician noise.
Details can be found in:
Manjón J.V., Coupé P., Buades A., Collins D.L., Robles M. New Methods for MRI Denoising based on Sparseness and Self-Similarity. Medical Image Analysis, 16(1):18-27, 2012.
Demo data and the source code of the proposed method can be found here.