Noise-driven anisotropic diffusion filtering of MRI
IEEE Transactions on Image Processing
An Object-Based Method for Rician Noise Estimation in MR Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Evaluation of a validation method for MR imaging-based motion tracking using image simulation
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
Segmentation based noise variance estimation from background MRI data
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Thermal noise estimation and removal in MRI: a noise cancellation approach
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A new similarity measure for non-local means filtering of MRI images
Journal of Visual Communication and Image Representation
Rician noise attenuation in the wavelet packet transformed domain for brain MRI
Integrated Computer-Aided Engineering
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A new method for noise filtering in images that follow a Rician model-with particular attention to magnetic resonance imaging-is proposed. To that end, we have derived a (novel) closed-form solution of the linear minimum mean square error (LMMSE) estimator for this distribution. Additionally, a set of methods that automatically estimate the noise power are developed. These methods use information of the sample distribution of local statistics of the image, such as the local variance, the local mean, and the local mean square value. Accordingly, the dynamic estimation of noise leads to a recursive version of the LMMSE, which shows a good performance in both noise cleaning and feature preservation. This paper also includes the derivation of the probability density function of several local sample statistics for the Rayleigh and Rician model, upon which the estimators are built.