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Image denoising for real image sensors

Zhang, Jiachao
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286

Abstract Details

2015, Master of Science (M.S.), University of Dayton, Electrical Engineering.
This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed in this work to calibrate the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we offer a new Poisson mixture image denoising scheme to overcome the problem. Experiments with real sensor data verify that the undersmooth is effectively improved.
Keigo Hirakawa (Advisor)
46 p.
Electrical Engineering
image denoising; image sensor; Poisson

Recommended Citations

Citations

  • Zhang, J. (2015). Image denoising for real image sensors [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286

    APA Style (7th edition)

  • Zhang, Jiachao. Image denoising for real image sensors. 2015. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286.

    MLA Style (8th edition)

  • Zhang, Jiachao. "Image denoising for real image sensors." Master's thesis, University of Dayton, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286

    Chicago Manual of Style (17th edition)

dayton1437954286
470
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This open access ETD is published by University of Dayton and OhioLINK.
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