A Wavelet Based Method for ToF Camera Depth Images Denoising

Date of Award

2022

Degree Name

M.S. in Electrical and Computer Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Keigo Hirakawa

Abstract

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.

Keywords

Electrical Engineering, Time of flight, denoising, Bayesian, image denoising, depth images

Rights Statement

Copyright © 2022, author.

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