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cheng_dissertation revised2 final format approved LW 10-27-15.pdf (11.23 MB)
ETD Abstract Container
Abstract Header
Optimal Denoising for Photon-limited Imaging
Author Info
Cheng, Wu
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446401290
Abstract Details
Year and Degree
2015, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical Engineering.
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.
Committee
Keigo Hirakawa (Committee Chair)
Subject Headings
Electrical Engineering
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Citations
Cheng, W. (2015).
Optimal Denoising for Photon-limited Imaging
[Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446401290
APA Style (7th edition)
Cheng, Wu.
Optimal Denoising for Photon-limited Imaging.
2015. University of Dayton, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446401290.
MLA Style (8th edition)
Cheng, Wu. "Optimal Denoising for Photon-limited Imaging." Doctoral dissertation, University of Dayton, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446401290
Chicago Manual of Style (17th edition)
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Document number:
dayton1446401290
Download Count:
472
Copyright Info
© 2015, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.