Share Email Print
cover

Proceedings Paper

Optical imaging based on compressive sensing
Author(s): Shen Li; Cai-wen Ma; Ai-li Xia
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Compressive Sensing (CS) is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. The basic idea of CS theory is that a signal or image, unknown but supposed to be sparse or compressible in some basis, can be subjected to fewer measurements than the nominal number of pixels, and yet be accurately reconstructed. By designing optical sensors to measure inner products between the scene and a set of test functions according to CS theory, we can use sophisticated computational methods to infer critical scene structure and content for significantly economizing the resources in data acquisition store and transmit. In this paper, we investigate how CS can provide new insights into optical imaging including optical devices. We first give a brief overview of the CS theory and reviews associated fast numerical reconstruction algorithms. Next, this paper explores the potential of several different physically realizable optical systems based on CS principles. In the end, we briefly discuss possible implication in the areas of data compression and optical imaging.

Paper Details

Date Published: 18 August 2011
PDF: 9 pages
Proc. SPIE 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 81942H (18 August 2011); doi: 10.1117/12.900691
Show Author Affiliations
Shen Li, Xi'an Institute of Optics and Precision Mechanics (China)
Cai-wen Ma, Xi'an Institute of Optics and Precision Mechanics (China)
Ai-li Xia, Xi'an Institute of Optics and Precision Mechanics (China)


Published in SPIE Proceedings Vol. 8194:
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications
Makoto Ikeda; Nanjian Wu; Guangjun Zhang; Kecong Ai, Editor(s)

© SPIE. Terms of Use
Back to Top