TY - JOUR
T1 - Algorithm development of cloud removal from solar images based on pix2pix network
AU - Wu, Xian
AU - Song, Wei
AU - Zhang, Xukun
AU - Lin, Ganghua
AU - Wang, Haimin
AU - Deng, Yuanyong
N1 - Funding Information:
Funding Statement: Funding for this study was received from the open project of CAS Key Laboratory of Solar Activity (Grant No: KLSA202114) and the crossdiscipline research project of Minzu University of China (2020MDJC08).
Funding Information:
Acknowledgement: Authors are thankful to the Huairou Solar Observing Station (HSOS) which provided the data. Authors gratefully acknowledge technical and financial support from CAS Key Laboratory of Solar Activity, Media Computing Lab of Minzu University of China and New Jersey Institute of Technology. We acknowledge for the data resources from “National Space Science Data Center, National Science Technology Infrastructure of China. (https://www.nssdc.ac.cn).
Publisher Copyright:
© 2022 Tech Science Press. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Sky clouds affect solar observations significantly. Their shadows obscure the details of solar features in observed images. Cloud-covered solar images are difficult to be used for further research without pre-processing. In this paper, the solar image cloud removing problem is converted to an image-to-image translation problem, with a used algorithm of the Pixel to Pixel Network (Pix2Pix), which generates a cloudless solar image without relying on the physical scattering model. Pix2Pix is consists of a generator and a discriminator. The generator is a well-designed U-Net. The discriminator uses PatchGAN structure to improve the details of the generated solar image, which guides the generator to create a pseudo realistic solar image. The image generation model and the training process are optimized, and the generator is jointly trained with the discriminator. So the generation model which can stably generate cloudless solar image is obtained. Extensive experiment results onHuairou Solar Observing Station, National Astronomical Observatories, and Chinese Academy of Sciences (HSOS, NAOC and CAS) datasets show that Pix2Pix is superior to the traditional methods based on physical prior knowledge in peak signal-to-noise ratio, structural similarity, perceptual index, and subjective visual effect. The result of the PSNR, SSIM and PI are 27.2121 dB, 0.8601 and 3.3341.
AB - Sky clouds affect solar observations significantly. Their shadows obscure the details of solar features in observed images. Cloud-covered solar images are difficult to be used for further research without pre-processing. In this paper, the solar image cloud removing problem is converted to an image-to-image translation problem, with a used algorithm of the Pixel to Pixel Network (Pix2Pix), which generates a cloudless solar image without relying on the physical scattering model. Pix2Pix is consists of a generator and a discriminator. The generator is a well-designed U-Net. The discriminator uses PatchGAN structure to improve the details of the generated solar image, which guides the generator to create a pseudo realistic solar image. The image generation model and the training process are optimized, and the generator is jointly trained with the discriminator. So the generation model which can stably generate cloudless solar image is obtained. Extensive experiment results onHuairou Solar Observing Station, National Astronomical Observatories, and Chinese Academy of Sciences (HSOS, NAOC and CAS) datasets show that Pix2Pix is superior to the traditional methods based on physical prior knowledge in peak signal-to-noise ratio, structural similarity, perceptual index, and subjective visual effect. The result of the PSNR, SSIM and PI are 27.2121 dB, 0.8601 and 3.3341.
KW - Cloud removal
KW - Pix2Pix
KW - Solar image
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U2 - 10.32604/cmc.2022.022325
DO - 10.32604/cmc.2022.022325
M3 - Article
AN - SCOPUS:85120801245
SN - 1546-2218
VL - 71
SP - 3497
EP - 3512
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 2
ER -