Image-quality assessment for full-disk solar observations with generative adversarial networks

@article{Jarolim2020ImagequalityAF,
  title={Image-quality assessment for full-disk solar observations with generative adversarial networks},
  author={Robert Jarolim and Astrid M. Veronig and Werner Potzi and Tatiana Podladchikova},
  journal={Astronomy \& Astrophysics},
  year={2020}
}
Context. In recent decades, solar physics has entered the era of big data and the amount of data being constantly produced from ground- and space-based observatories can no longer be purely analyzed by human observers. Aims. 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… 

Figures and Tables from this paper

Kanzelhöhe Observatory: Instruments, Data Processing and Data Products

This article describes the separate processing steps from data acquisition to high level products for different observing wavelengths, and presents in detail the quality classification, which is important for further processing of the raw images.

Full-disc Ca ii K observations—A window to past solar magnetism

Full-disc observations of the Sun in the Ca ii K  line provide one of the longest collections of solar data. First such observations were made in 1892 and since then various sites around the world

Multi-Channel Coronal Hole Detection with Convolutional Neural Networks

<p>Being the source region of fast solar wind streams, coronal holes are one of the key components which impact space weather. The precise detection of the coronal hole boundary is an important

References

SHOWING 1-10 OF 22 REFERENCES

Deep Learning

Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

SONG: Stellar Oscillations Network Group . A global network of small telescopes for asteroseismology and planet searches.

One of the limiting factors in current asteroseismic investigations of solar type stars is the limited time coverage of single-site observations. To remedy this problem we are studying the design of

Image Sharpening by Flows Based on Triple Well Potentials

Image sharpening in the presence of noise is formulated as a non-convex variational problem. The energy functional incorporates a gradient-dependent potential, a convex fidelity criterion and a high

The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python

Perception Evaluation: A New Solar Image Quality Metric Based on the Multi-fractal Property of Texture Features

The results show that with a high-resolution image as reference, the perception evaluation can give a robust estimate of the image quality for solar images in different scenarios.

An Event-Based Verification Scheme for the Real-Time Flare Detection System at Kanzelhöhe Observatory

In the framework of the Space Situational Awareness program of the European Space Agency (ESA/SSA), an automatic flare detection system was developed at Kanzelhöhe Observatory (KSO), a new event-based method is introduced to overcome the problem of rare events.

The Solar Dynamics Observatory (SDO)

The Solar Dynamics Observatory (SDO) was launched on 11 February 2010 at 15:23 UT from Kennedy Space Center aboard an Atlas V 401 (AV-021) launch vehicle. A series of apogee-motor firings lifted SDO

A Machine-learning Data Set Prepared from the NASA Solar Dynamics Observatory Mission

This curated data set from the NASA Solar Dynamics Observatory (SDO) mission will facilitate machine-learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission.