On the use of deep learning for single-pixel imaging

Saad Rizvi; Jie Cao; Qun Hao

doi:10.1117/12.2581027

10 October 2020 On the use of deep learning for single-pixel imaging

Saad Rizvi, Jie Cao, Qun Hao

Proceedings Volume 11551, Holography, Diffractive Optics, and Applications X; 1155106 (2020) https://doi.org/10.1117/12.2581027
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

We apply deep learning (DL) to counter three key problems which may occur in single-pixel imaging (SPI) namely noise, appearance of ringing or pixelated artifacts due to undersampling, and effects of projector lens aberration or defocusing. We employ a multi-scale mapping based deep convolutional neural network (DCNN) architecture to rectify undesirable effects in a 96×96 target reconstruction produced by environmental or system conditions, and optical anomalies. We train the proposed DCNN on augmented experimental data as well as simulation data to achieve robust experimental performance. Experimental results on real targets (2D and 3D) demonstrate the superior performance of the proposed method compared to conventional SPI.

Conference Presentation

Citation Download Citation

Saad Rizvi, Jie Cao, and Qun Hao "On the use of deep learning for single-pixel imaging", Proc. SPIE 11551, Holography, Diffractive Optics, and Applications X, 1155106 (10 October 2020); https://doi.org/10.1117/12.2581027

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