On the use of artificial intelligence for solving inverse problems (Conference Presentation)

George Barbastathis; Alexandre Goy; Shuai Li; Mo Deng; Kwabena Arthur; Zhengyun Zhang

doi:10.1117/12.2513283

13 March 2019 On the use of artificial intelligence for solving inverse problems (Conference Presentation)

George Barbastathis, Alexandre Goy, Shuai Li, Mo Deng, Kwabena Arthur, Zhengyun Zhang

Proceedings Volume 10887, Quantitative Phase Imaging V; 108870W (2019) https://doi.org/10.1117/12.2513283
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

I will discuss the emerging trend in computational imaging to train deep neural networks (DNNs) for image formation. The DNNs are trained from examples consisting of pairs of known objects and their corresponding raw images drawn from databases such as ImageNet, Faces-LFW and MNIST. The raw images are converted to complex amplitude maps and displayed on a Spatial Light Modulator (SLM.) After training, the DNNs are capable of recovering unknown objects, i.e. objects not previously included in the training sets, from the raw images in several scenarios: (1) phase objects retrieved from intensity after lensless propagation; (2) phase objects retrieved from intensity after lensless propagation at extremely low photon counts; and (3) amplitude objects retrieved from intensity in-focus after propagation through a strong scatterer. Recovery is robust to disturbances in the optical system, such as additional defocus or various misalignments. This suggests that DNNs may form robust internal models of the physics of light propagation and detection and generalize priors from the training set. In the talk I will discuss in more detail various methods to incorporate the physics into DNN training, and how DNN architecture and “hyper-parameters” (i.e., depth, number of units in each depth, presence or absence of skip connections, etc.) influence the quality of image recovery.

Conference Presentation

Citation Download Citation

George Barbastathis, Alexandre Goy, Shuai Li, Mo Deng, Kwabena Arthur, and Zhengyun Zhang "On the use of artificial intelligence for solving inverse problems (Conference Presentation)", Proc. SPIE 10887, Quantitative Phase Imaging V, 108870W (13 March 2019); https://doi.org/10.1117/12.2513283

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Artificial intelligence

Inverse problems

Image restoration

Physics

Spatial light modulators

Computational imaging

Databases

Show All Keywords

Keywords/Phrases

Search In:

Publication Years