TY - GEN
T1 - Information-optimal adaptive compressive imaging
AU - Ashok, Amit
AU - Huang, James L.
AU - Neifeld, Mark A.
PY - 2011
Y1 - 2011
N2 - We adopt a sequential Bayesian experiment design framework for compressive imaging wherein the measurement basis is data dependent and therefore adaptive. The criteria for measurement basis design employs the task-specific information (TSI), an information theoretic metric, that is conditioned on the past measurements. A Gaussian scale mixture prior model is used to represent compressible natural scenes in theWavelet basis. The resulting adaptive compressive imager design yields significant performance improvements compared to a static compressive imager using random projections.
AB - We adopt a sequential Bayesian experiment design framework for compressive imaging wherein the measurement basis is data dependent and therefore adaptive. The criteria for measurement basis design employs the task-specific information (TSI), an information theoretic metric, that is conditioned on the past measurements. A Gaussian scale mixture prior model is used to represent compressible natural scenes in theWavelet basis. The resulting adaptive compressive imager design yields significant performance improvements compared to a static compressive imager using random projections.
UR - http://www.scopus.com/inward/record.url?scp=84861302993&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861302993&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2011.6190217
DO - 10.1109/ACSSC.2011.6190217
M3 - Conference contribution
AN - SCOPUS:84861302993
SN - 9781467303231
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1255
EP - 1259
BT - Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
T2 - 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Y2 - 6 November 2011 through 9 November 2011
ER -