Optimal Denoising of Image Contrast and IMU-Based Image Deblurring
|Event Date:||November 1, 2018|
|Speaker Affiliation:||Electrical &
University of Dayton
|School or Program:||Electrical and Computer Engineering
Electrical and Computer Engineering
University of Dayton
In the first part of the talk, we address the denoising problem of Poisson imaging. 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 interscale relation is represented as random partitioning (binomial distribution) or local image contrast. In this work, 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.
Keigo Hirakawa is an Associate Professor and the Program Director for the Computer Engineering Program at the University of Dayton. Prior to UD, he was with Harvard University as a Research Associate of Department of Statistics. He simultaneously earned Ph.D in Electrical and Computer Engineering from Cornell University and M.M. in Jazz Performance from New England Conservatory of Music after receiving M.S. in Electrical and Computer Engineering from Cornell University and B.S. in Electrical Engineering from Princeton University. He is an associate editor for IEEE Transactions on Image Processing and SPIE/IS&T Journal of Electronic Imaging, and served on the technical committee of IEEE SPS IVMSP as well as the organization committees of IEEE ICIP 2012 and IEEE ICASSP 2017. He has received a number of recognitions, including a paper award at IEEE ICIP 2007 and a number of keynote speeches.