Compressive Imaging: A New Framework for Computational Image Processing

Monday, November 7, 2005 - 11:00am - 12:00pm
EE/CS 3-180
Richard Baraniuk (Rice University)
Imaging sensors, hardware, and algorithms are under increasing
pressure to accommodate ever larger and higher-dimensional data sets;
ever faster capture, sampling, and processing rates; ever lower power
consumption; communication over ever more difficult channels; and
radically new sensing modalities. Fortunately, over the past few
decades, there has been an enormous increase in computational power
and data storage capacity, which provides a new angle to tackle these
challenges. We could be on the verge of moving from a digital signal
processing (DSP) paradigm, where analog signals (including light
fields) are sampled periodically to create their digital counterparts
for processing, to a computational signal processing (CSP) paradigm,
where analog signals are converted directly to any of a number of
intermediate, condensed representations for processing using
optimization techniques. At the foundation of CSP lie new uncertainty
principles that generalize Heisenberg's between the time and frequency
domains and the concept of compressibility. As an example of CSP, I
will overview Compressive Imaging, an emerging field based on the
revelation that a small number of linear projections of a compressible
image contain enough information for image reconstruction and
processing. The implications of compressive imaging are promising for
many applications and enable the design of new kinds of imaging
systems and cameras. For more information, a compressive imaging
resource page is available on the web at
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