Object-Independent Image Restoration Based on Deep Learning

Hongwei Qi, Bing Dong*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The optical imaging system is affected by both internal factors (such as manufacturing and alignment errors) and external factors (such as atmospheric turbulence), preventing it from reaching diffraction-limited imaging. The blind image restoration algorithms typically require lots of iterative computations, leading to poor real-time performance. Image restoration methods based on neural network have limited application scenario. To solve the above mentioned issues, this paper introduces an object-independent image restoration method based on deep convolutional neural network (DCNN). The DCNN utilizes object-independent three-channel image feature as its input. Without estimating the wavefront aberration modes, the blurred point spread function (PSF) is predicted by the DCNN. Subsequently, a deconvolution operation can be applied to restore the image. Not only the blurred PSF, the restored image is also incorporated in the loss function to enable effective image restoration under complex aberrations. It is proved that our method can achieve good image restoration results even for complex aberrations involving 35 Zernike aberration modes. Compared to traditional blind restoration algorithms, it exhibits advantages in restoration time and better SSIM and PSNR.

源语言英语
主期刊名Proceedings of the 8th International Symposium of Space Optical Instruments and Applications - ISSOIA 2023
编辑H. Paul Urbach, Deren Li, Dengyun Yu
出版商Springer Science and Business Media Deutschland GmbH
86-100
页数15
ISBN(印刷版)9789819767175
DOI
出版状态已出版 - 2024
活动8th International Symposium of Space Optical Instruments and Applications, ISSOIA 2023 - Beijing, 中国
期限: 15 11月 202317 11月 2023

出版系列

姓名Springer Proceedings in Physics
191 SPP
ISSN(印刷版)0930-8989
ISSN(电子版)1867-4941

会议

会议8th International Symposium of Space Optical Instruments and Applications, ISSOIA 2023
国家/地区中国
Beijing
时期15/11/2317/11/23

指纹

探究 'Object-Independent Image Restoration Based on Deep Learning' 的科研主题。它们共同构成独一无二的指纹。

引用此