• Stochastic bilateral filter an...

Treffer: Stochastic bilateral filter and stochastic non-local means for high-dimensional images

Title:
Stochastic bilateral filter and stochastic non-local means for high-dimensional images
Authors:
Karam, Christina M.
Source:
Graduate Theses and Dissertations
Publisher Information:
eCommons
Publication Year:
2015
Collection:
University of Dayton: eCommons
Subject Terms:
Image analysis Data processing Mathematical models, Multispectral imaging Data processing, Remote-sensing images, Electrical Engineering, Bilateral Filter, Non-Local Means, Monte Carlo, hyperspectral images
Document Type:
Fachzeitschrift text
Language:
unknown
ISBN:
978-1-4297-2889-8
1-4297-2889-2
Relation:
https://ecommons.udayton.edu/graduate_theses/1109; http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429728892
Availability:
https://ecommons.udayton.edu/graduate_theses/1109
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429728892
Rights:
Copyright © 2015, author
Accession Number:
edsbas.11B35E93
Database:
BASE

Weitere Informationen

We propose stochastic bilateral filter (SBF) and stochastic non-local means (SNLM)--fast image filtering aimed at processing high dimensional images (such as color and hyperspectral images). SBF and SNLM are comprised of an efficient randomized process, where it agrees with conventional bilateral filter (BF) or non-local means (NLM) on average. By Monte-Carlo, we repeat this process a few times with different random instantiations so that they can be averaged to attain the correct BF/NLM output. The computational bottleneck of the SBF is constant with respect to the color dimension, meaning the execution time for hyperspectral images is nearly the same as the grayscale images. For SNLM, it is constant with respect to the window and block sizes but is still dependent on the color dimension. They are considerably faster than the conventional and existing fast'' bilateral filter and "fast'' non-local means implementations."