Stochastic bilateral filter and stochastic non-local means for high-dimensional images
Date of Award
2015
Degree Name
M.S. in Electrical Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Advisor: Keigo Hirakawa
Abstract
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."
Keywords
Image analysis Data processing Mathematical models, Multispectral imaging Data processing, Remote-sensing images, Electrical Engineering, Bilateral Filter, Non-Local Means, Monte Carlo, hyperspectral images
Rights Statement
Copyright © 2015, author
Recommended Citation
Karam, Christina M., "Stochastic bilateral filter and stochastic non-local means for high-dimensional images" (2015). Graduate Theses and Dissertations. 1109.
https://ecommons.udayton.edu/graduate_theses/1109
