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.
Highlights
The key to the research of the field of solar physics is to study the solar image to understand the structure and evolution of solar activity
Four methods: Dark channel prior network (DCP), CGAN (Pix2Pix model without LossL1), L1 model (Pix2Pix model without LossGAN), CycleGAN are compared with the proposed method
The data set includes two parts: the full-disk Halpha solar images obscured by cloud and the cloudless full-disk Halpha solar images.82 typical pairs of Halpha images are selected as original training set, and 15 pairs of them are chosen to test the performance of the proposed method
Summary
The key to the research of the field of solar physics is to study the solar image to understand the structure and evolution of solar activity. At other Observatories in the world, the percentage may be higher In these days, instruments work normally; if cloud cover the Sun, their shadows will degrade the observed images. The research object is combined with the deep learning method, and use Pixel to Pixel network to get more cloudless full-disk Halpha images from the cloud-covered full-disk Halpha images. HoanN [8] propose a cloud removal of remote sensing algorithm image based on multi-output support vector regression Most of these approaches depend on the physical scattering model [9], which is formulated as Eqs. Connected Pyramid Dehazing Network (DCPDN) [12] implements GAN on image cloud removal which learns transmission map and atmospheric light simultaneously in the generators by optimizing the final image cloud removal performance for cloudless images. As discussed in Introduction, it is meaningful to investigate a model-free cloud removal method via GAN
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