Signal Processing - September 2016 - 15

thus providing spectral information at a
higher level of detail. The massive scale
of multispectral data-at high resolutions
in the spectral, spatial, and temporal
dimensions-has long presented a major
challenge. This article presents an overview of computational multispectral
imaging, from a signal processing perspective, covering a breadth of topics
from system design, optimization to resolution, and noise analysis. While most
of the focus in the special issue has been
on imaging within the visible spectrum,
Patel et al. expand upon how the recent
developments in computational imaging have the potential to significantly
improve capabilities of mm-wave imaging systems. They discuss the merits
and drawbacks of available computational mm-wave imaging approaches
and identify avenues of research in
this rapidly evolving field. Finally,
Wetzstein and Lanman show us the
recent progress in compressive displays
capable of producing a realistic 3-D
visual experience of the world. At the
core of these devices is the use of
factorization techniques that provide
an alternative to Lippmann's integral
imaging and its inherent resolution/
sampling limitations.

Novel applications
The special issue also has two articles
that explore two novel application areas: digital preservation of cultural
heritage and gaze-contingent computational displays. Cultural heritage
provides an important opportunity for
computational imaging, as it enables
the noninvasive analysis of art through
different imaging techniques and new
ways to interact with art. Huang et al.
present techniques to preserve art digitally, study and document it, and
present it on modern media devices
to provide new experiences. The final article in the special issue, by
Stengel and Magnor, provides an
overview of recent developments in
computational display algorithms that
exploit ga ze-estimation to enhance
perceived vi sual quality of conventional video footage when viewed on
commodity monitors, projectors, or
head-mounted displays.

Computational imaging and displays
is a rapidly evolving field, and we hope
that the breadth and depth of coverage
of this special issue will not only serve
as a reminder about the huge strides
this community has made but also be a
catalyst for identifying and tackling the
major challenges facing the community.

Acknowledgments
We would like to take this opportunity
to thank all of the people involved in
making this special issue possible,
including the authors who submitted
white papers, the authors who contributed to the special issue, the IEEE staff
(especially IEEE Signal Processing
Society Publications Administrator
Rebecca Wollman, Managing Editor
Jessica Barragué, and the production
staff ), and, of course, the editorial board
of IEEE Signal Processing Magazine.
We thoroughly enjoyed putting together
this special issue and hope that you will
enjoy your journey through the recent
advances in computational photography and displays.

Meet the guest editors
Amit Agrawal (amitkagrawal@gmail.
com) is a senior research scientist at Amazon Lab126. Before
joining Amazon, he
was a principal member of research
staff at Mitsubishi Electric Research
Labs, Cambridge, Massachusetts. He
received his B.tech. degree in electrical
engineering from the Indian Institute of
Technology, Kanpur, in 2000 and his
M.S. and Ph.D. degrees from the University of Maryland, College Park, in
2003 and 2006, respectively. His work
has appeared in several technology publications including American Scientist,
Wired, New Scientist, Computer Graphics World Japan, and Engadget.
Richard G. Baraniuk
(richb@rice.edu) is the
Victor E. Cameron Professor of Electrical and
Computer Engineering
at Rice University and
the founder and director of OpenStax
IEEE SIgnal ProcESSIng MagazInE

September 2016

(openstax.org). He is a Fellow of the
IEEE and American Association for the
Advancement of Science and has
received national young investigator
awards from the National Science Foundation and Office of Naval Research; the
Rosenbaum Fellowship from the Isaac
Newton Institute of Cambridge University; the Electrical and Computer Engineering Young Alumni Achievement Award
from the University of Illinois; the IEEE
Signal Processing Society Best Paper,
Best Column, Education, and Technical
Achievement Awards; and the IEEE
James H. Mulligan, Jr. Medal.
Paolo Favaro (paolo.
favaro@inf.unibe.ch)
is a full professor at
the University of Bern,
Switzerland, where he
heads the Computer
Vision Group. He received the laurea
degree from Università di Padova,
Italy, in 1999 and the M.Sc. and Ph.D.
degrees in electrical engineering from
Washington University in St. Louis,
Missouri, in 2002 and 2003, respectively. From 2006 to 2011, he was a lecturer
and then reader at Heriot-Watt University and honorary fellow at the University of Edinburgh, United Kingdom.
His research fields include computer
vision, computational photography,
machine learning, and signal and image processing.
Ashok Veeraraghavan
(vashok@rice.edu) is
currently an assistant
professor of electrical
and computer engineering at Rice University, Texas. Before joining Rice
University, he was a research scientist
for three years at Mitsubishi Electric
Research Labs in Cambridge, Massachusetts. He received his bachelor's
degree in electrical engineering from
the Indian Institute of Technology,
Madras, in 2002 and his M.S. and Ph.D.
degrees from the Department of Electrical and Computer Engineering at the
University of Maryland, College Park,
in 2004 and 2008, respectively.
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http://www.openstax.org http://www.M.Sc

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