Title

Mulit-Resolution Aitchison Geometry Image Denoising for Low-Light Photography

Date of Award

1-1-2020

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

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