Information optimal compressive imaging: Design and implementation

Amit Ashok, James Huang, Yuzhang Lin, Ronan Kerviche

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9186
ISBN (Print)9781628412130
DOIs
StatePublished - 2014
Event50 Years of Optical Sciences at the University of Arizona - San Diego, United States
Duration: Aug 19 2014Aug 20 2014

Other

Other50 Years of Optical Sciences at the University of Arizona
CountryUnited States
CitySan Diego
Period8/19/148/20/14

Fingerprint

Imaging
Imaging techniques
Calibration
Hardware Implementation
Quantization
Image processing
projection
Projection
Random Projection
Hardware
Focal plane arrays
hardware
Compressibility
Focal Plane
Imager
System Architecture
Sparsity
Prior Knowledge
Image sensors
Demonstrate

Keywords

  • Compressive sensing
  • image formation
  • image priors
  • imaging
  • information theory
  • sparsity

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Ashok, A., Huang, J., Lin, Y., & Kerviche, R. (2014). Information optimal compressive imaging: Design and implementation. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9186). [91860K] SPIE. https://doi.org/10.1117/12.2063947

Information optimal compressive imaging : Design and implementation. / Ashok, Amit; Huang, James; Lin, Yuzhang; Kerviche, Ronan.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 9186 SPIE, 2014. 91860K.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ashok, A, Huang, J, Lin, Y & Kerviche, R 2014, Information optimal compressive imaging: Design and implementation. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 9186, 91860K, SPIE, 50 Years of Optical Sciences at the University of Arizona, San Diego, United States, 8/19/14. https://doi.org/10.1117/12.2063947
Ashok A, Huang J, Lin Y, Kerviche R. Information optimal compressive imaging: Design and implementation. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 9186. SPIE. 2014. 91860K https://doi.org/10.1117/12.2063947
Ashok, Amit ; Huang, James ; Lin, Yuzhang ; Kerviche, Ronan. / Information optimal compressive imaging : Design and implementation. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 9186 SPIE, 2014.
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