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

2020 ◽  
Vol 643 ◽  
pp. A72
Author(s):  
R. Jarolim ◽  
A. M. Veronig ◽  
W. Pötzi ◽  
T. Podladchikova

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, the quality assessment has to take multiple effects into account and provide information about the affected regions. The automatic and robust identification of quality-degrading effects is critical for maximizing the scientific return from the observations and to allow for event detections in real time. 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 Hα filtergrams. The approach is based on the structural appearance and the true image distribution of high-quality observations. Methods. 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 of 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. In addition, we present an extension of this architecture that also uses low-quality samples in the training step. This approach takes characteristics of both quality domains into account, and improves the sensitivity for minor image-quality degradation. Results. We apply our method to full-disk Hα filtergrams from the Kanzelhöhe Observatory recorded during 2012−2019 and demonstrate its capability to perform a reliable image-quality assessment for various atmospheric conditions and instrumental effects. 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. Conclusions. The developed method is capable of providing a reliable image-quality assessment in real time, without the requirement of reference observations. Our approach has the potential for further application to similar astrophysical observations and requires only coarse manual labeling of a small data set.

Biometrics ◽  
2017 ◽  
pp. 1241-1257
Author(s):  
Ehsan Khoramshahi ◽  
Juha Hietaoja ◽  
Anna Valros ◽  
Jinhyeon Yun ◽  
Matti Pastell

This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%.


2018 ◽  
pp. 34-50
Author(s):  
Ehsan Khoramshahi ◽  
Juha Hietaoja ◽  
Anna Valros ◽  
Jinhyeon Yun ◽  
Matti Pastell

This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%.


Author(s):  
Chuang Zhang ◽  
Xianzhao Yang ◽  
Xiaoyu Huang ◽  
Guiyue Yu ◽  
Suting Chen

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