Mulit-Resolution Aitchison Geometry Image Denoising for Low-Light Photography
Date of Award
2020
Degree Name
M.S. in Electrical Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Advisor: Keigo Hirakawa
Abstract
In low-photon imaging regime, noise in image sensors are dominated by shot noise, bestmodeled statistically as Poisson. In this work, we show that the Poisson likelihood functionis very well matched with the Bayesian estimation of the "difference of log of contrast of pixelintensities". More specifically, our work takes root in statistical compositional data analysis,whereby we reinterpret the Aitchison geometry as a multiresolution analysis in log-pixeldomain. We demonstrate that the difference-log-contrast has wavelet-like properties thatcorrespond well with human visual system, while being robust to illumination variations.We derive a denoising technique based on an approximate conjugate prior for the latentAitchison variable that gives rise to an explicit minimum mean squared error estimation.The resulting denoising techniques preserves image contrast details that are arguably moremeaningful to human vision than the pixel intensity values themselves.
Keywords
Electrical Engineering, Image denoising, Poisson, low light imaging, Aitchison geometry
Rights Statement
Copyright © 2020, author
Recommended Citation
Miller, Sarah Victoria, "Mulit-Resolution Aitchison Geometry Image Denoising for Low-Light Photography" (2020). Graduate Theses and Dissertations. 6820.
https://ecommons.udayton.edu/graduate_theses/6820
