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

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6181-6193, 2022, DOI:10.32604/cmc.2022.027215

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… More >

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

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3497-3512, 2022, DOI:10.32604/cmc.2022.022325

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… More >