Image restoration for synthetic aperture systems with a non-blind deconvolution algorithm via a deep convolutional neural network-Reference-Cited by-同舟云学术

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Image restoration for synthetic aperture systems with a non-blind deconvolution algorithm via a deep convolutional neural network

Published:2020-03-23 Issue:7 Volume:28 Page:9929
ISSN:1094-4087
Container-title:Optics Express
language:en
Short-container-title:Opt. Express

Author:

Hui Mei,Wu Yong,Li Weiqian,Liu Ming,Dong Liquan,Kong Lingqin,Zhao Yuejin

Funder

National Natural Science Foundation of China

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics

Reference12 articles.

1. Aperture Synthesis Using Independent Telescopes

2. Synthetic apertures: An Overview

3. 3D shape measurement in the presence of strong interreflections by epipolar imaging and regional fringe projection

4. Active sparse aperture imaging using independent transmitter modulation with improved incoherent Fourier ptychographic algorithm

5. Improving mid-frequency contrast in sparse aperture optical imaging systems based upon the Golay-9 array

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2. Discover Image Restoration: Analysis and Novel Architecture Proposal;2024 International Conference on Inventive Computation Technologies (ICICT);2024-04-24

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4. RPIR: A Semiblind Unsupervised Learning Image Restoration Method for Optical Synthetic Aperture Imaging Systems With Co-Phase Errors;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. Image denoising via multi-scale spatial frequency domain convolutional neural network Noise-Net;Optoelectronic Imaging and Multimedia Technology X;2023-11-27

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