TY - JOUR
T1 - RestoreNet-Plus
T2 - Image restoration via deep learning in optical synthetic aperture imaging system
AU - Tang, Ju
AU - Wu, Ji
AU - Wang, Kaiqiang
AU - Ren, Zhenbo
AU - Wu, Xiaoyan
AU - Hu, Liusen
AU - Di, Jianglei
AU - Liu, Guodong
AU - Zhao, Jianlin
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - (110.3010) Image reconstruction techniques
KW - (110.4850) Optical transfer functions
KW - (200.4260) Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85107731369&partnerID=8YFLogxK
U2 - 10.1016/j.optlaseng.2021.106707
DO - 10.1016/j.optlaseng.2021.106707
M3 - 文章
AN - SCOPUS:85107731369
SN - 0143-8166
VL - 146
JO - Optics and Lasers in Engineering
JF - Optics and Lasers in Engineering
M1 - 106707
ER -