Time Stretch Inspired Computational Imaging

Autor:	Jalali, Bahram, Suthar, Madhuri, Asghari, Mohamad, Mahjoubfar, Ata
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition Physics - Data Analysis Statistics and Probability Physics - Optics Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1706.07841 Zobrazit plný text záznamu View this record from Arxiv