摘要：A critical issue in image restoration is the problem of noise removal
while keeping the integrity of relevant image information. The method
proposed in this paper is a fully automatic 3D blockwise version of the
nonlocal (NL) means filter with wavelet subbands mixing. The proposed
wavelet subbands mixing is based on a multiresolution approach
for improving the quality of image denoising filter. Quantitative validation
was carried out on synthetic datasets generated with the BrainWeb simulator.
The results show that our NL-means filter with wavelet subbands
mixing outperforms the classical implementation of the NL-means filter in
terms of denoising quality and computation time. Comparison with wellestablished
methods, such as nonlinear diffusion filter and total variation
minimization, shows that the proposed NL-means filter produces better
denoising results. Finally, qualitative results on real data are presented.
