Transcript Compressed Sensing and GPUs: Two Complimentary Revolutions ? Igor Carron http://nuit-blanche.blogspot.com GPUCamp Dec. 6th, 2008 Intro • • • • Compressed Sensing: What is it ? Why is it revolutionary ? Why use.

Compressed Sensing
and GPUs: Two
Complimentary Revolutions ?
Igor Carron
http://nuit-blanche.blogspot.com
GPUCamp
Dec. 6th, 2008
Intro
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Compressed Sensing: What is it ?
Why is it revolutionary ?
Why use GPUs ?
The Future
• Appendices
• References
Compressed Sensing:
What is it ?
• The George Costanza “do the opposite”
sampling scheme !
• First Publications: 2004, David Donoho,
Emmanuel Candes, Justin Romberg,
Terence Tao (Fields Medal 2006)
Compressed Sensing (CS):
What is it ?
• Un signal can be:
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–
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A voice recording (1D)
An image (2-D)
An image with 100,000 colors / spectral bands (3D)
A movie with 100,000 colors (5D)…..
• CS = A New Sampling method where signal
acquisition and compression are performed at
the same time.
An example
Other examples [2]
An example:
How does it work ?
Superposition of several views on one Focal Plane Array [9]
An example:
How does it work ?
Disambiguation is the result of a technique based on CS [9]
An example of CS measurement
[8]
Usual Photography
Approach: pinhole
Camera, Lens…
CS
An example of CS measurement
(cont.)
• One notices three elements:
– CS, as implemented here, is an indirect image
acquisition technique.
– One needs a reconstruction step in order to
transform CS data into understandable
images.
– The size of the CS data (before reconstruction
is smaller than the initial size of the image.
A Second Example
CS Camera at Rice University [1]
A Second Example (cont.)
• http://www.youtube.com/watch?v=xvIHHK
_X3B8
• Check an acquisition and reconstruction at
3 min and 30 seconds into the video.
Why is it Revolutionary ?
• Today: One takes 5 MP images. Power is
expanded on acquiring this image and
then is further expanded on compressing it
into a 300 KB JPEG image. Most
information is thrown out.
• CS: 1.5MB of information are acquired.
That’s it!
Why is it Revolutionary ?
• Compression and Acquisition are performed
simultaneously.
– Power required is reduced. Less need for memory.
– Useful for data of large dimension where no
compressionm scheme is know to exist.
– Sub-sampling compared to traditional sampling
methods.
• Data are naturally encrypted.
• Allow for new hardware development (with new
trade-offs).
• Data are “eternal”.
The Big Picture
Why use GPUs ?
CS Acquisition
• Acquisition is generally performed in some
analog fashion.
– Low cost in power and
– near optimal compression
– See examples of hardware being developed
in the appendix.
• There may be case where GPUs can be
indispensable: Adaptive CS.
CS Reconstruction
• Principal bottleneck!
• Different types of reconstruction
algorithms are being developed. The
methods improve every months.
• Reconstruction of large images is still too
slow.
• CS reconstruction of signals larger than
dimension 2 will probably required specific
processor in the near term.
CS Reconstruction
• UCLA GPU/Multicore [5]
• “…This algorithm has been especially designed to take benefit of
current parallel many-core architectures and achieves noticeable
speedups. Besides, it is easy to implement on these architecture. To
validate our approach, we proposed implementations on various
current high-end platforms, such as vectorized multicore CPU, GPU
and Cell. Pros and cons of both platforms and implementations have
been discussed…”
CS Reconstruction
• University of Calgary GPU solver [6]
“….fast GPU implementation of the MP algorithm,
based on the recently released NVIDIA CUDA
API and CUBLAS library. The results show that
the GPU version is substantially faster (up to 31
times) than the highly optimized CPU version
based on CBLAS (GNU Scientific Library)…”
CS Reconstruction
• Graz University of Technology GPU solver
[7]
“…The drawback of these algorithms is their long
reconstruction time which makes it impossible to
use them in clinical practice. This study
demonstrates that these optimization problems
can be solved on modern graphic processing
units (GPUs), with computation times that allow
real time imaging…”
The Future
• Utilization of GPUs in CS acquisition maybe
of interest in specific domains ? Adaptive
Acquisition ?
• GPUs should be used for CS reconstruction
problems in a variety of domains.
• Possibility of performing object recognition
directly from CS measurements (no need for
CS reconstructions). GPU will need to
perform comparison between sets of CS
measurements and libraries and
dictionaries...
The Future
• It unravels under our own eyes!
• For more information:
– Rice University Repository
http://www.dsp.ece.rice.edu/cs/cscamera/
– CS: The Big Picture
http://igorcarron.googlepages.com/cs
– Nuit Blanche Blog
http://nuit-blanche.blogspot.com
Appendices
CS Acquisition: Examples
• CS acquisition can be embodied in
different technologies
• Several examples are featured at:
http://igorcarron.googlepages.com/compress
edsensinghardware
Different Technology Readiness
Levels (TRL)
1D
A2I converters (Rice/Caltech/U
Michigan) [22]
2D
CS Camera at Rice University [1]
Single Pixel Illumination camera
(Rice, U of Arizona) [3]
Georgia Tech Transform Imager
[10]
Georgia Tech Random Convolution
Imager [11]
EPFL CMOS Imager [12]
3D
CS Hyperspectral Imager at Duke
University [14]
Columbia CS light for 3D
participating media [15]
MIT Random Lens Imager [16]
HyperGeoCam – Texas A&M [17]
• Some times, the measurement hardware
are the same but their operations is more
exotic.
IRM (Stanford) [18]
Seismic (University of British
Columbia, CA) [19]
Traditional way
Compressive Sensing Way
Ground Penetrating Radar (ITB,
Indonesia) [20]
• There are also many existing systems that
are implementing CS without knowing it.
– Coded Aperture
– Computational Photography
– ….
References
• [1] Compressive Imaging: A New Single Pixel Camera
• [2] Camera Culture MIT.
• [3] Pawan K. Baheti and Mark A. Neifeld, Feature-specific
structured imaging
• [5] Alexandre Borghi, Jerome Darbon, Sylvain Peyronnet, Tony F.
Chan and Stanley Osher , A Simple Compressive Sensing Algorithm
for Parallel Many-Core Architectures
• [6] Fast GPU Implementation of Sparse Signal Recovery from
Random Projections, M. Andrecut
• [7] Real Time Elimination of Undersampling Artifacts in CE MRA
using Variational Denoising on Graphics Hardware, Florian
Knoll, Markus Unger, Franz Ebner, Rudolf Stollberger
• [8] Compressive coded aperture superresolution image
reconstruction,
Roummel F. Marcia and Rebecca M. Willett
• [9] Fast disambiguation of superimposed images for increased
field of view, Roummel F. Marcia, Changsoon Kim, Jungsang Kim,
David Brady, and Rebecca M. Willett
References
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[10] Transform Imager
[11] Justin Romberg, Architectures for Compressive Sampling
[12] CMOS Compressed Imaging by Random Convolution, Laurent
Jacques, Pierre Vandergheynst, Alexandre Bibet, Vahid
Majidzadeh, Alexandre Schmid, Yusuf Leblebici.
[14] Coded Aperture Snapshot Spectral Imaging (CASSI)
[15] Compressive Structured Light for Recovering Inhomogeneous
Participating Media
[16] Random Lens Imaging Rob Fergus, Antonio Torralba, and William T.
Freeman
[17] HyperGeocam
[18] Michael Lustig
[19] SLIM/UBC, Felix Herrmann, Yogi Erlangga and Tim Lin : Compressive
sampling meets seismic imaging
[20] : A Compressive SFCW-GPR System, Andriyan Suksmono, Endon
Bharata, A. Andaya Lestari, A. Yarovoy, and L.P. Ligthart.
[22] A2I website