Abstract

Static feature-specific imaging (SFSI), where the measurement basis remains fixed/static during the data measurement process, has been shown to be superior to conventional imaging for reconstruction tasks. Here, we describe an adaptive approach that utilizes past measurements to inform the choice of measurement basis for future measurements in an FSI system, with the goal of maximizing the reconstruction fidelity while employing the fewest measurements. An algorithm to implement this adaptive approach is developed for FSI systems, and the resulting systems are referred to as adaptive FSI (AFSI) systems. A simulation study is used to analyze the performance of the AFSI system for two choices of measurement basis: principal component (PC) and Hadamard. Here, the root mean squared error (RMSE) metric is employed to quantify the reconstruction fidelity. We observe that an AFSI system achieves as much as 30% lower RMSE compared to an SFSI system. The performance improvement of the AFSI systems is verified using an experimental setup employed using a digital micromirror device (DMD) array.

© 2010 Optical Society of America

Adaptive feature-specific imaging: a face recognition example

Pawan K. Baheti and Mark A. Neifeld
Appl. Opt. 47(10) B21-B31 (2008)

Adaptive feature-specific imaging for recognition of non-Gaussian classes

Pawan K. Baheti, Jun Ke, and Mark A. Neifeld
Appl. Opt. 48(28) 5225-5239 (2009)

Recognition using information-optimal adaptive feature-specific imaging

Pawan K. Baheti and Mark A. Neifeld
J. Opt. Soc. Am. A 26(4) 1055-1070 (2009)

Optimizing measurements for feature-specific compressive sensing

Abhijit Mahalanobis and Mark Neifeld
Appl. Opt. 53(26) 6108-6118 (2014)

Feature-specific structured imaging

Pawan K. Baheti and Mark A. Neifeld
Appl. Opt. 45(28) 7382-7391 (2006)