Name
Abstract | ||
|---|---|---|
During their acquisition, magnetic resonance (MR) images are affected by random noise, causing the observed magnitude image samples to be Rician distributed. In order to reduce the noise level while preserving the relevant image features, we develop an optimized Non-Local Means (NLM) denoising algorithm. The most sensitive parameters of the proposed NLM estimator are optimized on the squared-magnitude image, which follows a non-central chi-square distribution on two degrees of freedom. This minimum MSE optimization is performed via the minimization of the so-called chi-square unbiased risk estimate (CURE). Taking advantage of some acceleration techniques involving convolutions and parallel computation, we show that the proposed CURE-optimized NLM outperforms some state-of-the-art NLM algorithms with no increase in computation time. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISBI.2012.6235622 | ISBI |
Keywords | Field | DocType |
non-local means,optimisation,optimized nonlocal means denoising algorithm,mr image denoising,parallel computation,chi-square distribution,noncentral chi-square distribution,nlm estimator,image denoising,noise level,magnetic resonance imaging,random noise,biomedical mri,magnitude image samples,cure-optimized nlm outperforms,rician distribution,squared-magnitude imaging,cure-optimized nonlocal means,medical image processing,chi-square unbiased risk estimation,mean square error methods,state-of-the-art nlm algorithms,psnr,image features,noise reduction,non local means,parallel computer,noncentral chi square distribution,kernel,chi square distribution,magnetic resonance | Kernel (linear algebra),Noise reduction,Pattern recognition,Feature (computer vision),Computer science,Convolution,Non-local means,Artificial intelligence,Rician fading,Computation,Estimator | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4577-1857-1 | 1 |
PageRank | References | Authors |
0.37 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung Ook Hong | 1 | 2 | 0.84 |
| F. Luisier | 2 | 447 | 22.09 |
| Patrick J. Wolfe | 3 | 355 | 40.15 |