We introduce and evaluate a post-processing technique for fast denoising of diffusion-
weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal …

The search for efficient image denoising methods is still a valid challenge at the crossing of
functional analysis and statistics. In spite of the sophistication of the recently proposed …

We present an in-depth analysis of a variation of the nonlocal means (NLM) image
denoising algorithm that uses principal component analysis (PCA) to achieve a higher …

Digital images are matrices of equally spaced pixels, each containing a photon count. This
photon count is a stochastic process due to the quantum nature of light. It follows that all …

Non-local means (NLM) provides a powerful framework for denoising. However, there are a
few parameters of the algorithm-most notably, the width of the smoothing kernel-that are …

A novel image sequence denoising algorithm is presented. The proposed approach takes
advantage of the self-similarity and redundancy of adjacent frames. The algorithm is inspired …

DRAM systems achieve high performance when all DRAM banks are busy servicing useful
memory requests. The degree to which DRAM banks are busy is called DRAM Bank-Level …

Aiming at the difficulty of single-phase grounding fault line selection in a small current
grounding system, a distribution network fault line selection method based on modified …

In remote sensing change detection, Markov random field (MRF) has been used
successfully to model the prior probability using class-labels dependencies. MRF has …

We propose a randomized version of the nonlocal means (NLM) algorithm for large-scale
image filtering. The new algorithm, called Monte Carlo nonlocal means (MCNLM), speeds …