Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection
Computer Vision and Pattern Recognition
2010-02-18 v1 Multimedia
Abstract
The SSIM-optimized exact global histogram specification (EGHS) is shown to converge in the sense that the first order approximation of the result's quality (i.e., its structural similarity with input) does not decrease in an iteration, when the step size is small. Each iteration is composed of SSIM gradient ascent and basic EGHS with the specified target histogram. Selection of step size and other parameters is also discussed.
Keywords
Cite
@article{arxiv.1002.3344,
title = {Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection},
author = {Alireza Avanaki},
journal= {arXiv preprint arXiv:1002.3344},
year = {2010}
}
Comments
Supplement to published work, on SSIM-optimized exact global histogram specification; please see arXiv:0901.0065