No Cover Image

Journal article 22 views 1 download

Removing cloud shadows from ground-based solar imagery

Amal Chaoui, Jay Morgan Orcid Logo, Adeline Paiement, Jean Aboudarham

Machine Vision and Applications, Volume: 35, Issue: 6

Swansea University Author: Jay Morgan Orcid Logo

  • 67663.VOR.pdf

    PDF | Version of Record

    This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

    Download (3.56MB)

Check full text

DOI (Published version): 10.1007/s00138-024-01607-2

Abstract

The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows...

Full description

Published in: Machine Vision and Applications
ISSN: 0932-8092 1432-1769
Published: Springer Science and Business Media LLC 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa67663
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds. Quantitative assessments are obtained through image quality indices (RMSE, PSNR, SSIM, and FID). We demonstrate improved results with regards to the traditional cloud removal technique and a sparse coding baseline, on different cloud types and textures.
College: College of Science
Funders: Agence National de la Recherche
Issue: 6

Similar Items