Noise estimation and denoising using Non-Local PCA (NLPCA)
In this page we present a novel method for
MRI denoising that exploits both the sparseness and self-similarity
properties of the MR images. The proposed method is a two-stage
approach that first filters the noisy image using a non local PCA
thresholding strategy by automatically estimating the local noise level
present in the image and second uses this filtered image as a guide
image within a rotationally invariant non-local means filter. The
proposed method internally estimates the amount of local noise presents
in the images that enables applying it automatically to images with
spatially varying noise levels and also corrects the Rician noise
induced bias locally. The proposed approach has been compared with
related state-of-the-art methods showing competitive results in all the
studied cases..
Details can be found in:
Manjón J.V., Coupe P., Buades A. MRI Noise Estimation and Denoising Using Non-Local PCA. Medical Image Analysis, 22:35-47. 2015.
A Demo of the proposed method can be found here:
Version 1.0 (February 2015)