Chinese Space Science and Technology ›› 2021, Vol. 41 ›› Issue (3): 16-23.doi: 10.16708/j.cnki.1000.758X.2021.0033

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Low-light image enhancement of space satellites based on GAN

CHEN Yulang1,GAO Jingmin1,*,ZHANG Kebei2,ZHANG Yang1   

  1. 1 School of Automation, Beijing Information Science& Technology University, Beijing 100192, China
    2 Beijing Institute of Control Engineering, Beijing 100190, China
  • Received:2020-07-28 Revised:2020-09-08 Accepted:2020-09-15 Online:2021-06-25 Published:2021-06-25
  • Contact: Email: gaojm_biti@163.com E-mail:gaojm_biti@163.com

Abstract: Aiming at the problem of serious information damage of satellite optical images under the lowlight imaging condition, we proposed a satellite lowlight image enhancement method based on GAN. The method can improve the average brightness and contrast of images, restore image details, and provide higherquality information for image processing techniques such as image recognition. Firstly, we designed a densely connected generator to strengthen the information propagation and fusion between each feature extraction phase, reduce the loss of feature, and better extract similar semantic information in normallight and lowlight images. Combining the idea of EnlightenGAN, the globallocal discriminator structure was introduced to enhance images more naturally. Under the condition of a small number of samples, unpaired training was used to the proposed method, and data enhancement methods such as random scaling and flipping of the input images were applied to improve the training effect and model performance. Finally, the proposed method was validated by simulation. The experimental results show that, under the condition of low illumination, the proposed method reduced NIQE by 1.034 and 0.699 compared with the LIME and EnlightenGAN. The proposed method can preserve more image details, realize higher overall and local brightness, higher contrast, and more natural effects of enhancement.

Key words: low-light image enhancement, GAN, unpaired training, dense connection, relativistic discriminator

CLC Number: