Open Access

ARTICLE

Solar Image Cloud Removal based on Improved Pix2Pix Network

by Xukun Zhang¹, Wei Song^1,2,3,*, Ganghua Lin^2,4, Yuxi Shi⁵

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 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, 07102, USA

* Corresponding Author: Wei Song. Email:

Computers, Materials & Continua 2022, 73(3), 6181-6193. https://doi.org/10.32604/cmc.2022.027215

Received 12 January 2022; Accepted 15 March 2022; Issue published 28 July 2022

Abstract

In ground-based observations of the Sun, solar images are often affected by appearance of thin clouds, which contaminate the images and affect the scientific results from data analysis. In this paper, the improved Pixel to Pixel Network (Pix2Pix) network is used to convert polluted images to clear images to remove the cloud shadow in the solar images. By adding attention module to the model, the hidden layer of Pix2Pix model can infer the attention map of the input feature vector according to the input feature vector. And then, the attention map is multiplied by the input feature map to give different weights to the hidden features in the feature map, adaptively refine the input feature map to make the model pay attention to important feature information and achieve better recovery effect. In order to further enhance the model’s ability to recover detailed features, perceptual loss is added to the loss function. The model was tested on the full disk H-alpha images datasets provided by Huairou Solar Observing Station, National Astronomical Observatories. The experimental results show that the model can effectively remove the influence of thin clouds on the picture and restore the details of solar activity. The peak signal-to-noise ratio (PSNR) reaches 27.3012 and the learned perceptual image patch similarity (LPIPS) reaches 0.330, which is superior to the existed dehaze algorithms.

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Keywords

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