RestoreNet: A deep learning framework for image restoration in optical multi-Aperture imaging system

Ju Tang, Kaiqiang Wang, Xiaoyan Wu, Jianglei Di, Guodong Liu, Jianlin Zhao

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Imaging blur is an inevitable problem because of the low response to medium frequency in optical multi-Aperture imaging system, in which Wiener filtering is usually used in implementing image restoration to obtain clear highresolution images. Recent notable developments in the field of deep learning have opened up exciting avenues inspiring us to use data-driven approach for image deblurring in optical multi-Aperture imaging system. In this paper, a deep learning framework named RestoreNet, which is based on a U-shaped convolution neural network, is proposed to replace the general Wiener filtering for image restoration. Numerical simulation and experiment results show that RestoreNet could recover the imaging map from system successfully, just like Wiener filtering does. However, RestoreNet only needs one dataset containing a few images for training, and shows strong image restoration ability without the point spread function or optical transfer function of system in testing, as well as the priori information of object and noise. As a result, RestoreNet is an effective alternative in image restoration of the optical multi-Aperture imaging system.

源语言英语
主期刊名Optics Frontier Online 2020
主期刊副标题Optics Imaging and Display
编辑Hannan Wang
出版商SPIE
ISBN(电子版)9781510639638
DOI
出版状态已出版 - 2020
活动Optics Frontier Online 2020: Optics Imaging and Display - Virtual, Online
期限: 19 6月 202020 6月 2020

出版系列

姓名Proceedings of SPIE - The International Society for Optical Engineering
11571
ISSN(印刷版)0277-786X
ISSN(电子版)1996-756X

会议

会议Optics Frontier Online 2020: Optics Imaging and Display
Virtual, Online
时期19/06/2020/06/20

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