Image Quality Assessment for Full-Disk Solar Observations with Generative Adversarial Networks

Abstract

In order to assure a stable series of recorded images of sufficient quality for further scientific analysis, an objective image quality measure is required. Especially when dealing with ground-based observations, which are subject to varying seeing conditions and clouds, the quality assessment has to take multiple effects into account and provide information about the affected regions. In this study, we develop a deep learning method that is suited to identify anomalies and provide an image quality assessment of solar full-disk H$\alpha$ filtergrams. The approach is based on the structural appearance and the true image distribution of high-quality observations. We employ a neural network with an encoder-decoder architecture to perform an identity transformation of selected high-quality observations. The encoder network is used to achieve a compressed representation of the input data, which is reconstructed to the original by the decoder. We use adversarial training to recover truncated information based on the high-quality image distribution. When images with reduced quality are transformed, the reconstruction of unknown features (e.g., clouds, contrails, partial occultation) shows deviations from the original. This difference is used to quantify the quality of the observations and to identify the affected regions. We apply our method to full-disk H$\alpha$ filtergrams from Kanzelhöhe Observatory recorded during 2012-2019 and demonstrate its capability to perform a reliable image quality assessment for various atmospheric conditions and instrumental effects, without the requirement of reference observations. Our quality metric achieves an accuracy of 98.5% in distinguishing observations with quality-degrading effects from clear observations and provides a continuous quality measure which is in good agreement with the human perception.

References in corpus (5)

• A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
• A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
• Real-time flare detection in ground-based H$\alpha$ imaging at Kanzelhöhe Observatory
• Review of image quality measures for solar imaging
• Perception Evaluation -- A new solar image quality metric based on the multi-fractal property of texture features

Cited by in corpus (5)

• Full-disc Ca II K observations -- a window to past solar magnetism
• Multi-channel coronal hole detection with convolutional neural networks
• A Universal Method for Solar Filament Detection from H-alpha Observations using Semi-supervised Deep Learning
• Kanzelhöhe Observatory: instruments, data processing and data products
• Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning