Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Self-Supervised Approach to Joint Imaging Inverse Problems

Not Accessible

Your library or personal account may give you access

Abstract

In many computational imaging applications, only sparse measurements on a source are possible, hindering reconstruction. We present a probabilistic, self-supervised method, the physics-informed variational autoencoder (P-VAE), that jointly reconstructs many sources, each with sparse measurements.

© 2023 The Author(s)

PDF Article  |   Presentation Video
More Like This
Solving Inverse Problems using Self-Supervised Deep Neural Nets

Jiapeng Liu, Muralidhar M. Balaji, Christopher A. Metzler, M. Salman Asif, and Prasanna Rangarajan
CTh5A.2 Computational Optical Sensing and Imaging (COSI) 2021

Physics-Informed Variational Autoencoder for Undersampled Fourier Ptychography

Yolanda Hu, Andrew Olsen, Jan Funke, Srinivas Turaga, and Vidya Ganapati
CF1D.8 Computational Optical Sensing and Imaging (COSI) 2022

Deep learning in photoacoustic tomography utilizing variational autoencoders

Teemu Sahlström and Tanja Tarvainen
1263108 European Conference on Biomedical Optics (ECBO) 2023

Presentation Video

Presentation video access is available to:

  1. Optica Publishing Group subscribers
  2. Technical meeting attendees
  3. Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads


More Like This
Solving Inverse Problems using Self-Supervised Deep Neural Nets

Jiapeng Liu, Muralidhar M. Balaji, Christopher A. Metzler, M. Salman Asif, and Prasanna Rangarajan
CTh5A.2 Computational Optical Sensing and Imaging (COSI) 2021

Physics-Informed Variational Autoencoder for Undersampled Fourier Ptychography

Yolanda Hu, Andrew Olsen, Jan Funke, Srinivas Turaga, and Vidya Ganapati
CF1D.8 Computational Optical Sensing and Imaging (COSI) 2022

Deep learning in photoacoustic tomography utilizing variational autoencoders

Teemu Sahlström and Tanja Tarvainen
1263108 European Conference on Biomedical Optics (ECBO) 2023

Self-supervised neural network for holographic microscopy

Luzhe Huang, Hanlong Chen, Tairan Liu, and Aydogan Ozcan
ATu3Q.4 CLEO: Applications and Technology (CLEO:A&T) 2023

Optics-Free Imaging Using A Self-Consistent Supervised Deep Neural Network

Soren Nelson and Rajesh Menon
JTu5A.3 Applied Industrial Spectroscopy (AIS) 2021

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.