Optical imaging based on compressive sensing

Shen Li; Cai-wen Ma; Ai-li Xia

doi:10.1117/12.900691

18 August 2011 Optical imaging based on compressive sensing

Shen Li, Cai-wen Ma, Ai-li Xia

Proceedings Volume 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications; 81942H (2011) https://doi.org/10.1117/12.900691
Event: International Symposium on Photoelectronic Detection and Imaging 2011, 2011, Beijing, China

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.

Citation Download Citation

Shen Li, Cai-wen Ma, and Ai-li Xia "Optical imaging based on compressive sensing", Proc. SPIE 8194, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, 81942H (18 August 2011); https://doi.org/10.1117/12.900691

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available