Optimal denoising for photon-limited imaging

Date of Award

2015

Degree Name

Ph.D. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Keigo Hirakawa

Abstract

Most conventional imaging modalities detect light indirectly by observing high energy photons. The random nature of photon emission and detection are often the dominant source of noise in imaging. Such case is referred to as photon-limited imaging, and the noise distribution is well modeled as Poisson. Multiplicative multiscale innovation (MMI) presents a natural model for Poisson count measurement, where the inter-scale relation is represented as random partitioning (binomial distribution) or local image contrast. In this paper, we propose a nonparametric empirical Bayes estimator that minimizes the mean square error of MMI coefficients. The proposed method achieves better performance compared with state-of-art methods in both synthetic and real sensor image experiments under low illumination.

Keywords

Poisson distribution, Digital images Deconvolution, Imaging systems Image quality, Electrical Engineering

Rights Statement

Copyright © 2015, author

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