On the use of deep learning for three-dimensional computational imaging

George Barbastathis; Subeen Pang; Iksung Kang; Ziling Wu; Zhiguang Liu; Zhen Guo; Fucai Zhang

doi:10.1117/12.2655261

8 March 2023 On the use of deep learning for three-dimensional computational imaging

George Barbastathis, Subeen Pang, Iksung Kang, Ziling Wu, Zhiguang Liu, Zhen Guo, Fucai Zhang

Proceedings Volume 12445, Practical Holography XXXVII: Displays, Materials, and Applications; 124450J (2023) https://doi.org/10.1117/12.2655261
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Deep learning has proven to be an efficient and robust method for many computational imaging systems. The advantages of machine learning, as a rule, are that it is fast—at least in its supervised form after training is complete—and seems exceedingly effective in capturing regularizing priors. Here, we focus the discussion on non-invasive three-dimensional (3D) object reconstruction. One then faces the additional dilemma of choosing the appropriate model of light-matter interaction inside the specimen, i.e. the forward operator. We describe the three stages of approximation that are applicable: weak scattering with weak diffraction (also known as the Radon transform), weak scattering with strong diffraction, and strong scattering. We then overview machine learning approaches for the various models, and glance at the consequences of oversimplifying the forward operator choice.

Conference Presentation

Citation Download Citation

George Barbastathis, Subeen Pang, Iksung Kang, Ziling Wu, Zhiguang Liu, Zhen Guo, and Fucai Zhang "On the use of deep learning for three-dimensional computational imaging", Proc. SPIE 12445, Practical Holography XXXVII: Displays, Materials, and Applications, 124450J (8 March 2023); https://doi.org/10.1117/12.2655261

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