BlindNet: an untrained learning approach toward computational imaging with model uncertainty

Xiangyu Zhang; Fei Wang; Guohai Situ

doi:10.1117/12.2601966

9 October 2021 BlindNet: an untrained learning approach toward computational imaging with model uncertainty

Xiangyu Zhang, Fei Wang, Guohai Situ

Proceedings Volume 11898, Holography, Diffractive Optics, and Applications XI; 1189810 (2021) https://doi.org/10.1117/12.2601966
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Abstract

Deep neural networks have been successfully applied to constrained object priors and parameter optimization. Here, we propose a novel learning-free deep neural network architecture to tackle uncertain system optimization. This blind system constraint deep neural network(BlindNet) need not to know all the parameters of the system and can simultaneously acquire the desired image and system parameters. In order to do so, we showed that the BlindNet can perform phase retrieval on the diffraction pattern with unknown diffraction distance.

Conference Presentation

Citation Download Citation

Xiangyu Zhang, Fei Wang, and Guohai Situ "BlindNet: an untrained learning approach toward computational imaging with model uncertainty", Proc. SPIE 11898, Holography, Diffractive Optics, and Applications XI, 1189810 (9 October 2021); https://doi.org/10.1117/12.2601966

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