Signal Processing - September 2016 - 34

systems to niche applications like X-ray and gamma-ray imaging. The resurgence of lensless imaging can be attributed to the
convergence of four factors: the development of digital CMOS
and CCD sensor arrays, efficient and realistic image models
and recovery algorithms, powerful computing, and new mask
designs (such as the separable mask in the FlatCam).
The further development of lensless imaging, however, will
face challenges. As the mask is moved closer to the sensor in
any pinhole or coded aperture camera, the angular resolution
decreases, resulting in a trade-off between minimal thickness and spatial resolution [29]. Additionally, computationally
recovering a scene from less-than-perfectly conditioned sensor
measurements results in noise amplification. Although noise
amplification cannot be eliminated, careful design of mask
patterns and regularization models can minimize this effect.
The necessity for a computational algorithm also results in a
time-lag between image acquisition and reconstruction (~100 ms
for FlatCam). Such a delay may be acceptable in certain applications but unacceptable in others such as augmented or virtual
reality. There are a number of avenues for continued research
and development that could lead to significantly improved lensless imaging performance, including new architectures for
improving spatial resolution, new image models to reduce the
demultiplexing noise, and new computational algorithms to support high-speed sensing. Sometimes, size matters. The lensless
imaging approach promises to challenge the traditional barriers
of size, weight, cost, and performance in a broad range of applications spanning consumer, medical, scientific imaging, machine
vision, and remote sensing. Indeed, the future of lensless imaging
research and development looks very bright.

Acknowledgments
This work was supported in part by NSF grants CCF-1527501
and CCF-1502875, DARPA REVEAL grant HR0011-16-C-0028,
ONR DURIP grant N00014-15-1-2878, and ONR grant N0001415-1-2735.

Authors
Vivek Boominathan (vivekb@rice.edu) is a Ph.D. candidate in
the Electrical and Computer Engineering Department at Rice
University, Houston, Texas. His research interests are in the areas
of computer vision, signal processing, and computational imaging. He received his B.Tech. degree in electrical engineering from
the Indian Institute of Technology, Hyderabad, in 2012. He is a
three-time recipient of Todai (Japan)-IIT Undergraduate Student
Scholarships for academic excellence in 2009, 2010, and 2011.
Jesse K. Adams (jka4@rice.edu) is a Ph.D. candidate in the
Applied Physics/Electrical and Computer Engineering Department
at Rice University, Houston, Texas. His research interests lie in
the areas of nanofabrication, nano-optics, neuroengineering, and
computational imaging. He received his B.Sc. degree in physics
from the University of North Florida in 2014.
M. Salman Asif (sasif@ece.ucr.edu) is an assistant professor
in the Department of Electrical and Computer Engineering at
the University of California, Riverside. He received his B.Sc.
degree in 2004 from the University of Engineering and
34

Technology, Lahore, Pakistan, and his M.S.E.E. and Ph.D.
degrees from the Georgia Institute of Technology, Atlanta, in
2008 and 2013, respectively. He worked as a research intern at
Mitsubishi Electric Research Laboratories in Cambridge,
Massachusetts, in the summer of 2009 and at Samsung
Standards Research Laboratory in Richardson, Texas, in the
summer of 2010. He worked as a senior research engineer at
Samsung Research America, Dallas, Texas, from August 2012
to January 2014 and as a postdoctoral researcher at Rice
University from February 2014 to June 2016. His research
interests include compressive sensing, computational and medical imaging, and machine learning.
Benjamin W. Avants (bwa1@rice.edu) is currently a research
engineer in the Robinson Lab at Rice University, Texas, who
specializes in the design, fabrication, development, programming, and automation of experimental tools and apparatus,
including micro- and nano-fabrication. His role also includes
system modeling and the development of data processing tools
and techniques. He received his bachelor's degree in electrical
engineering and computer engineering from the University of
Memphis in 2012 with second majors in physics and math. He
began working with the Robinson Lab that same year. His personal interests also include alternative power generation and
storage methods as well as robotics.
Jacob T. Robinson (jtrobinson@rice.edu) is an assistant
professor in the Department of Electrical and Computer
Engineering with joint appointments in the Bioengineering
Department at Rice University and the Neuroscience
Department at Baylor College of Medicine. He earned his B.S.
degree in physics from the University of California, Los Angeles,
in 2003, followed by his M.S. and Ph.D. degrees in applied
physics from Cornell University in 2008. His research is focused
on developing scalable nanotechnologies to manipulate and
measure brain activity. From 2008 to 2012, he was a postdoctoral researcher in the Department of Chemistry and Chemical
Biology at Harvard University, after which he joined Rice
University as an assistant professor. He is a recipient of the John
S. Dunn Foundation Collaborative Research Award, the Hamill
Innovations Award, and the DARPA Young Faculty Award. His
areas of interest include nanowire electrodes, nanophotonic
probes, and nanoscale magnetic materials.
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
(openstax.org). His research interests lie in new theory, algorithms, and hardware for sensing, signal processing, and
machine learning. He is a Fellow of the IEEE and the
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 ECE 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.

IEEE SIgnal ProcESSIng MagazInE

September 2016

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