Algorithm development of cloud removal from solar images based on pix2pix network

Xian Wu, Wei Song, Xukun Zhang, Ganghua Lin, Haimin Wang, Yuanyong Deng

Research output: Contribution to journalArticlepeer-review

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 onHuairou 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.

Original languageEnglish (US)
Pages (from-to)3497-3512
Number of pages16
JournalComputers, Materials and Continua
Volume71
Issue number2
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Modeling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Cloud removal
  • Pix2Pix
  • Solar image

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