期刊
OPTICS AND LASERS IN ENGINEERING
卷 146, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.optlaseng.2021.106707
关键词
Optical transfer functions; Image reconstruction techniques; Neural networks
类别
资金
- National Natural Science Foundation of China (NSFC) [62075183, 61927810, 61905197]
- Fundamental Research Funds for the Central Universities [310201911qd002]
- Open Research Fund of State Key Laboratory of Transient Optics and Photonics [SKLST202008]
The proposed improved network, RestoreNet-Plus, based on deep learning for image restoration in optical synthetic aperture imaging system shows more stability and satisfactory results compared to other methods.
The synthetic aperture technology can improve the resolution effectively in the optical imaging system. In fact, the imaging blur, turbulence aberration and noise can affect the imaging quality of optical synthetic aperture imaging system seriously. Several non-blind methods are applied generally to recover the degraded maps with the prior information. However, the restoration effect is not stable enough and satisfactory. As a data-driven approach, the deep learning framework possesses advantages in solving this problem. In this paper we propose an improved network, RestoreNet-Plus, for the image restoration of optical synthetic aperture imaging system. After the proofs of numerical simulation and experiment results, RestoreNet-Plus is a better alternative compared with other methods, owing to its better restoration ability, strong denoising ability and capacity for turbulence correction error.
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