An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images

摘要

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3D optimized blockwise version of the Non Local (NL) means filter []. The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2D images, but reducing the computational burden is a critical aspect to extend the method to 3D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully-automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: (a) an automatic tuning of the smoothing parameter, (b) a selection of the most relevant voxels, (c) a blockwise implementation and (d) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb []. The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods (Anisotropic Diffusion [] and Total Variation minimization process []) in terms of accuracy (measured by the Peak Signal to Noise Ratio) with low computation time. Finally, qualitative results on real data are presented.