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

Sparsity-based One-dimensional Phase Retrieval of Continuous Non-negative Pulse Trains

Not Accessible

Your library or personal account may give you access

Abstract

Represented by discrete-time signals, continuous non-negative pulse trains have been successfully reconstructed from under-sampled measurements of their Fourier amplitude based on assumptions of sparsity and finite support.

© 2018 The Author(s)

PDF Article
More Like This
Non-Iterative Holographic Image Reconstruction and Phase Retrieval Using a Deep Convolutional Neural Network

Yair Rivenson, Yibo Zhang, Harun Günaydın, Da Teng, and Aydogan Ozcan
STh1J.3 CLEO: Science and Innovations (CLEO:S&I) 2018

Phase Retrieval Based on Wave Modulation

Xingchen Pan, Cheng Liu, and Jianqiang Zhu
CTh2D.4 Computational Optical Sensing and Imaging (COSI) 2018

Robust Holographic Autofocusing Based on Edge Sparsity

Yibo Zhang, Hongda Wang, Yichen Wu, Miu Tamamitsu, and Aydogan Ozcan
AM1J.7 CLEO: Applications and Technology (CLEO:A&T) 2018

Poster Presentation

Media 1: PDF (198 KB)     
Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved