Multiresolution Non-local Means Filter for 3D Image Denoising
In this work, we present a new adaptive multiresolution Non-Local Means
filter for 3D MR Image Denoising. Our work is based on the recently
proposed
Non-local (NL-) means filter (Buades2005). As for all the denoising
filters,
the choice of the filtering parameters is crucial in NL-means-based
restoration. The balance between structure preservation and noise
removal is a
difficult task and different sets of parameters can be optimal for
different
space-frequency resolutions of the image. In this paper, we propose to
implicitly adapt the among of denoising according to the underlying
structures
thanks to a new adaptive soft wavelet coefficient mixing. Moreover, a
Rician
formulation of the NL-means filter has been incorporated within our
multiresolution framework in order to deal with the specific nature of
noise in
MR images. Quantitative validation was carried out on Brainweb datasets
with
Gaussian and Rician noise. The results show that the proposed
multiresolution
filter outperforms optimized versions of the NL-means filter. Finally,
qualitative experiments on anatomical and Diffusion-Weighted MR images
show
that the proposed filter efficiently removes the noise while preserving
fine
structures.
Details can be found in:
Coupé P., Manjón J.V., Robles M., Collins D.L. Adaptive Multiresolution Non-Local Means Filter for 3D MR Image Denoising. IET Image processing 6(5): 558-568, 2012.
Demo data and the source code of the proposed method can be found here
Version 1.0 (September 2008)