Open Access

ARTICLE

Algorithm Development of Cloud Removal from Solar Images Based on Pix2Pix Network

Xian Wu¹, Wei Song^1,2,3,*, Xukun Zhang¹, Ganghua Lin^2,4, Haimin Wang^5,6,7, Yuanyong Deng^2,4

1 School of Information Engineering, Minzu University of China, Beijing, 100081, China
2 Key Laboratory of Solar Activity, Chinese Academy of Sciences (KLSA, CAS), Beijing, 100101, China
3 National Language Resource Monitoring and Research Center of Minority Languages, Minzu University of China, Beijing, 100081, China
4 National Astronomical Observatories, Chinese Academy of Sciences (NAOC, CAS), Beijing, 100101, China
5 Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights, Newark, 07102-1982, USA
6 Institute for Space Weather Sciences, New Jersey Institute of Technology, University Heights, Newark, 07102-1982, USA
7 Big Bear Solar Observatory, New Jersey Institute of Technology, 40386, North Shore Lane, Big Bear City, 92314-9672, USA

* Corresponding Author: Wei Song. Email:

Computers, Materials & Continua 2022, 71(2), 3497-3512. https://doi.org/10.32604/cmc.2022.022325

Received 04 August 2021; Accepted 11 October 2021; Issue published 07 December 2021

Abstract

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 on Huairou 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.

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Keywords

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