A high resolution spectrum reconstruction algorithm using compressive sensing theory

Zhaoyu Zheng; Dakai Liang; Shulin Liu; Shuqing Feng

doi:10.1117/12.2189451

17 July 2015 A high resolution spectrum reconstruction algorithm using compressive sensing theory

Zhaoyu Zheng, Dakai Liang, Shulin Liu, Shuqing Feng

Proceedings Volume 9524, International Conference on Optical and Photonic Engineering (icOPEN 2015); 95241D (2015) https://doi.org/10.1117/12.2189451
Event: International Conference on Optical and Photonic Engineering (icOPEN2015), 2015, Singapore, Singapore

Abstract

This paper proposes a quick spectrum scanning and reconstruction method using compressive sensing in composite structure. The strain field of corrugated structure is simulated by finite element analysis. Then the reflect spectrum is calculated using an improved transfer matrix algorithm. The K-means singular value decomposition sparse dictionary is trained . In the test the spectrum with limited sample points can be obtained and the high resolution spectrum is reconstructed by solving sparse representation equation. Compared with the other conventional basis, the effect of this method is better. The match rate of the recovered spectrum and the original spectrum is over 95%.

Citation Download Citation

Zhaoyu Zheng, Dakai Liang, Shulin Liu, and Shuqing Feng "A high resolution spectrum reconstruction algorithm using compressive sensing theory", Proc. SPIE 9524, International Conference on Optical and Photonic Engineering (icOPEN 2015), 95241D (17 July 2015); https://doi.org/10.1117/12.2189451

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