Signal Processing - September 2016 - 45

SIGNAL PROCESSING FOR
COMPUTATIONAL PHOTOGRAPHY AND DISPLAYS

Ayush Bhandari and Ramesh Raskar

Signal Processing for Time-of-Flight Imaging Sensors
An introduction to inverse problems in computational 3-D imaging

T

ime-of-flight (ToF) sensors offer a cost-effective and realtime solution to the problem of three-dimensional (3-D)
imaging-a theme that has revolutionized our sceneunderstanding capabilities and is a topic of contemporary
interest across many areas of science and engineering. The goal
of this tutorial-style article is to provide a thorough understanding of ToF imaging systems from a signal processing perspective that is useful to all application areas. Starting with a
brief history of the ToF principle, we describe the mathematical basics of the ToF image-formation process, for
both time- and frequency-domain, present an overview of important results within the topic, and discuss contemporary challenges where this emerging
area can benefit from the signal processing community. In particular, we examine case studies
where inverse problems in ToF imaging are coupled with signal processing theory and methods,
such as sampling theory, system identification,
and spectral estimation, among others. Through
this exposition, we hope to establish that ToF sensors are more than just depth sensors; depth information may be used to encode other forms of
physical parameters, such as, the fluorescence lifetime
of a biosample or the diffusion coefficient of turbid/scattering medium. The MATLAB scripts and ToF sensor
data used for reproducing figures in this article are available
via the aauthor's webpage: http://www.mit.edu/~ayush/Code.

©ISTOCKPHOTO.COM/YAKOBCHUK

Introduction
A brief history of computational imaging

Digital Object Identifier 10.1109/MSP.2016.2582218
Date of publication: 2 September 2016

1053-5888/16©2016IEEE

"A picture is worth a thousand words." Throughout the past several centuries, this immemorial phrase has pushed the development of photography from analog to digital, accelerated by
breakthrough advancements in sensor technology. The digital
revolution created a necessity for sophisticated signal processing
algorithms tailored for image enhancement, storage, and compression. Shannon's sampling theory was the pathway for
IEEE SIgnal ProcESSIng MagazInE

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September 2016

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http://www.mit.edu/~ayush/Code http://www.ISTOCKPHOTO.COM/YAKOBCHUK

Table of Contents for the Digital Edition of Signal Processing - September 2016

Signal Processing - September 2016 - Cover1
Signal Processing - September 2016 - Cover2
Signal Processing - September 2016 - 1
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Signal Processing - September 2016 - Cover3
Signal Processing - September 2016 - Cover4
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