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  • Open Access

    ARTICLE

    Solar Image Cloud Removal based on Improved Pix2Pix Network

    Xukun Zhang1, Wei Song1,2,3,*, Ganghua Lin2,4, 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 >

  • Open Access

    ARTICLE

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

    Xian Wu1, Wei Song1,2,3,*, Xukun Zhang1, Ganghua Lin2,4, Haimin Wang5,6,7, Yuanyong Deng2,4

    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 >

  • Open Access

    ARTICLE

    Data Matching of Solar Images Super-Resolution Based on Deep Learning

    Liu Xiangchun1, Chen Zhan1, Song Wei1,2,3,*, Li Fenglei1, Yang Yanxing4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4017-4029, 2021, DOI:10.32604/cmc.2021.017086

    Abstract The images captured by different observation station have different resolutions. The Helioseismic and Magnetic Imager (HMI: a part of the NASA Solar Dynamics Observatory (SDO) has low-precision but wide coverage. And the Goode Solar Telescope (GST, formerly known as the New Solar Telescope) at Big Bear Solar Observatory (BBSO) solar images has high precision but small coverage. The super-resolution can make the captured images become clearer, so it is wildly used in solar image processing. The traditional super-resolution methods, such as interpolation, often use single image’s feature to improve the image’s quality. The methods based on deep learning-based super-resolution image… More >

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