Optimal denoising for photon-limited imaging
Date of Award
Ph.D. in Electrical Engineering
Department of Electrical and Computer Engineering
Advisor: Keigo Hirakawa
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.
Poisson distribution, Digital images Deconvolution, Imaging systems Image quality, Electrical Engineering
Copyright 2015, author
Cheng, Wu, "Optimal denoising for photon-limited imaging" (2015). Graduate Theses and Dissertations. 1099.