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A Wavelet Based Method for ToF Camera Depth Images Denoising

Abstract Details

2022, Master of Science (M.S.), University of Dayton, Electrical Engineering.
This work addresses the problem of shot noise in Time-of-Flight (ToF) camera depth sensors, which is caused by the random nature of photon emission and detection. In this paper, we derive a Bayesian denoising technique based on Maximum A Posteriori (MAP) probability estimation, implemented in the wavelet domain, which denoises (2D) depth images acquired by ToF cameras. We also propose a new noise model describing the photon noise present in the raw ToF data. We demonstrate that the raw data captured by ToF camera depth sensors follows a Skellam distribution. We test the resulting denoising technique, in the millimeter level, with real sensor data and verify that it performs better than other denoising methods described in the literature.
Keigo Hirakawa (Advisor)
58 p.

Recommended Citations

Citations

  • Idoughi, A. (2022). A Wavelet Based Method for ToF Camera Depth Images Denoising [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1657779847494683

    APA Style (7th edition)

  • Idoughi, Achour. A Wavelet Based Method for ToF Camera Depth Images Denoising. 2022. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1657779847494683.

    MLA Style (8th edition)

  • Idoughi, Achour. "A Wavelet Based Method for ToF Camera Depth Images Denoising." Master's thesis, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1657779847494683

    Chicago Manual of Style (17th edition)