Dror Baron -
Compressed Sensing Hardware
Compressed sensing (CS) is a new framework for integrated sensing and
compression. The fundamental revelation is that, if an N-sample signal
x is sparse and has a good K-term approximation in some basis, then
it can be reconstructed using M =O(K log(N/K)) << N linear projections of x
onto another basis.
Furthermore, x can be reconstructed using linear
programming, which has polynomial complexity.
Some of the CS projects I have worked on are described here, and
links to numerous other papers appear on the
Nuit Blanche
blog and the
compressed sensing resource page.
This webpage describes hardware-related CS projects
that I have been involved in.
Compressed sensing camera:
We developed a single-pixel CS camera that takes a random projection
of an image with a pattern of zeros and ones by bouncing light off
an array of micro-mirrors and collecting photons at a single
photodiode.
-
M. B. Wakin,
J. N. Laska,
M. F. Duarte,
D. Baron,
S. Sarvotham,
D. Takhar,
K. F. Kelly,
and R. G. Baraniuk,
"An Architecture for Compressive Imaging",
Proceedings of International Conference on Image Processing
(ICIP), Atlanta, GA, October 2006 (pdf).
-
M. B. Wakin,
J. N. Laska,
M. F. Duarte,
D. Baron,
S. Sarvotham,
D. Takhar,
K. F. Kelly,
and R. G. Baraniuk,
"Compressive Imaging for Video Representation and Coding",
Proceedings of Picture Coding Symposium
(PCS), Beijing, China, May 2006
(pdf).
-
D. Takhar,
J. N. Laska,
M. B. Wakin,
M. F. Duarte,
D. Baron,
S. Sarvotham,
K. F. Kelly,
and R. G. Baraniuk,
"A New Compressive Imaging Camera Architecture using Optical-Domain Compression,"
SPIE Electronic Imaging,
San Jose, CA, pp. 43-52, January 2006
(pdf).
New analog-to-digital converters:
Current analog to digital converters are too slow to sample
wideband signals at their Nyquist frequency.
However, for sparse signals the Nyquist frequency is a
worst-case bound on the requisite measurement rate.
Our techniques project the signal directly in analog
hardware by modulating and then filtering an analog signal,
and then digitize at a reduced sampling rate.
-
S. Kirolos,
J. N. Laska,
M. B. Wakin,
M. F. Duarte,
D. Baron,
T. Ragheb, Y. Massoud,
and R. G. Baraniuk,
"Analog-to-Information Conversion via Random Demodulation,"
Proceedings of the IEEE Dallas Circuits and Systems Workshop
(DCAS), Dallas, TX, October 2006 (pdf).
-
J. A. Tropp,
M. B. Wakin,
M. F. Duarte,
D. Baron,
and R. G. Baraniuk,
"Random Filters for Compressive Sampling and Reconstruction,"
Proceedings of the International Conference on Acoustics, Speech, and Signal Processing
(ICASSP2006), Tolouse, France, 2006
(pdf).
Back to my homepage.
Last updated June 2014.