Geometric kernel smoothing of tensor fields
Methodology
2010-11-30 v1
Abstract
In this paper, we study a kernel smoothing approach for denoising a tensor field. Particularly, both simulation studies and theoretical analysis are conducted to understand the effects of the noise structure and the structure of the tensor field on the performance of different smoothers arising from using different metrics, viz., Euclidean, log-Euclidean and affine invariant metrics. We also study the Rician noise model and compare two regression estimators of diffusion tensors based on raw diffusion weighted imaging data at each voxel.
Cite
@article{arxiv.1011.6298,
title = {Geometric kernel smoothing of tensor fields},
author = {Owen Carmichael and Jun Chen and Debashis Paul and Jie Peng},
journal= {arXiv preprint arXiv:1011.6298},
year = {2010}
}
Comments
14 figures