Time Stretch Inspired Computational Imaging
Computer Vision and Pattern Recognition
2017-06-27 v1 Data Analysis, Statistics and Probability
Optics
Machine Learning
Abstract
We show that dispersive propagation of light followed by phase detection has properties that can be exploited for extracting features from the waveforms. This discovery is spearheading development of a new class of physics-inspired algorithms for feature extraction from digital images with unique properties and superior dynamic range compared to conventional algorithms. In certain cases, these algorithms have the potential to be an energy efficient and scalable substitute to synthetically fashioned computational techniques in practice today.
Keywords
Cite
@article{arxiv.1706.07841,
title = {Time Stretch Inspired Computational Imaging},
author = {Bahram Jalali and Madhuri Suthar and Mohamad Asghari and Ata Mahjoubfar},
journal= {arXiv preprint arXiv:1706.07841},
year = {2017}
}
Comments
This work has been published in the PHOTOPTICS 2017 - 5th International Conference on Photonics, Optics and Laser Technology, Volume 1, ISBN 978-989-758-223-3, pages 340-345