@inproceedings{7fc96b2dcc9d4b3f99fe3ad0f255250b,
title = "Adaptive compressive imaging for object reconstruction",
abstract = "Static Feature-specific imaging (SFSI) employing a fixed/static measurement basis has been shown to achieve superior reconstruction performance to conventional imaging under certain conditions.1-5 In this paper, we describe an adaptive FSI system in which past measurements inform the choice of measurement basis for future measurements so as to maximize the reconstruction fidelity while employing the fewest measurements. An algorithm to implement an adaptive FSI system for principle component (PC) measurement basis is described. The resulting system is referred to as a PC-based adaptive FSI (AFSI) system. A simulation study employing the root mean squared error (RMSE) metric to quantify the reconstruction fidelity is used to analyze the performance of the PC-based AFSI system. We observe that the AFSI system achieves as much as 30% lower RMSE compared to a SFSI system.",
keywords = "Adaptive, Compressive sensing, Feature-specific imaging",
author = "Jun Ke and Amit Ashok and Neifeld, {Mark A.}",
year = "2010",
doi = "10.1117/12.861738",
language = "English",
isbn = "9780819483140",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Adaptive Coded Aperture Imaging, Non-Imaging, and Unconventional Imaging Sensor Systems II",
note = "Adaptive Coded Aperture Imaging, Non-Imaging, and Unconventional Imaging Sensor Systems II ; Conference date: 01-08-2010 Through 02-08-2010",
}