RestoreNet-Plus: Image restoration via deep learning in optical synthetic aperture imaging system

Ju Tang, Ji Wu, Kaiqiang Wang, Zhenbo Ren, Xiaoyan Wu, Liusen Hu, Jianglei Di, Guodong Liu, Jianlin Zhao

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

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.

Original languageEnglish
Article number106707
JournalOptics and Lasers in Engineering
Volume146
DOIs
StatePublished - Nov 2021

Keywords

  • (110.3010) Image reconstruction techniques
  • (110.4850) Optical transfer functions
  • (200.4260) Neural networks

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