20 October 2014 Information optimal compressive imaging: design and implementation
Author Affiliations +
Abstract
Compressive imaging exploits sparsity/compressibility of natural scenes to reduce the detector count/read-out bandwidth in a focal plane array by effectively implementing compression during the acquisition process. How-ever, realizing the full potential of compressive imaging entails several practical challenges, such as measurement design, measurement quantization, rate allocation, non-idealities inherent in hardware implementation, scalable imager architecture, system calibration and tractable image formation algorithms. We describe an information-theoretic approach for compressive measurement design that incorporates available prior knowledge about natural scenes for more efficient projection design relative to random projections. Compressive measurement quantization and rate-allocation problem are also considered and simulation studies demonstrate the performance of random and information-optimal projection designs for quantized compressive measurements. Finally we demonstrate the feasibility of optical compressive imaging with a scalable compressive imaging hardware implementation that addresses system calibration and real-time image formation challenges. The experimental results highlight the practical effectiveness of compressive imaging with system design constraints, non-ideal system components and realistic system calibration.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amit Ashok, Amit Ashok, James Huang, James Huang, Yuzhang Lin, Yuzhang Lin, Ronan Kerviche, Ronan Kerviche, "Information optimal compressive imaging: design and implementation", Proc. SPIE 9186, Fifty Years of Optical Sciences at The University of Arizona, 91860K (20 October 2014); doi: 10.1117/12.2063947; https://doi.org/10.1117/12.2063947
PROCEEDINGS
11 PAGES


SHARE
Back to Top