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Optimal Denoising for Photon-limited Imaging

Abstract Details

2015, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical Engineering.
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.
Keigo Hirakawa (Committee Chair)

Recommended Citations

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)