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

Huang, Yi; Jia, Peng; Cai, Dongmei; Cai, Bin

doi:10.1007/s11207-019-1524-5

Cited by 6 publications

(6 citation statements)

References 40 publications

(36 reference statements)

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“…A preliminary IF-based image restoration method was proposed by Jia et al (2019) for solar image restoration. Because solar images of the same wavelength are representations of the same physical process, we find that texture features in solar images of the same wavelength satisfies the same probability distribution (Huang et al 2019). Then with several high resolution solar images as references, the CycleGAN (Zhu et al 2017) can restore any frames of solar images of the same wavelength.…”

Section: Introductionmentioning

confidence: 94%

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Jia

Ning

Sun

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data–driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data–driven image restoration method based on generative adversarial networks with option–driven learning. Our method uses several high resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.

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Section: Introductionmentioning

confidence: 94%

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Jia

Ning

Sun

et al. 2020

Monthly Notices of the Royal Astronomical Society

Self Cite

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“…Solar features show strong similarity, which allows for the use of reduced-reference IQMs. In Huang et al (2019), such a metric has been proposed, and is based on the assumption of the multi-fractal property of solar images.…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of deep-learning methods, two important components can be taken into account, (1) the structural appearance of solar features and (2) the deviations from the true image distribution. While recent methods try to compare structural similarity over pixel-based estimations (Huang et al 2019;Deng et al 2015), to date there exists no IQM for solar observations that directly estimates deviations from the true image distribution. The stability of deep-learning methods relies on the variety and the quantity of available training samples.…”

Section: Introductionmentioning

confidence: 99%

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

Jarolim

Veronig

Pötzi

et al. 2020

A&A

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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.

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“…Solar features show a strong similarity, which allows for the use of reduced-reference image quality metrics. In Huang et al (2019) such a metric has been proposed, based on the assumption of the multi-fractal property of solar images.…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of deep learning methods, two important components can be taken into account, (1) the structural appearance of solar features and (2) the deviations from the true image distribution. While recent methods try to compare structural similarity over pixel-based estimations (Huang et al 2019;Deng et al 2015), to this point there exists no image quality metric for solar observations which directly estimates deviations from the true image distribution. The stability of deep learning methods relies on the variety and the quantity of training samples used.…”

Section: Introductionmentioning

confidence: 99%

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

Jarolim,

Veronig,

Pötzi

et al. 2020

Preprint

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Context. Within the last 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 a critical task, in order to maximize 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 highquality 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. In addition, we present an extension of this architecture by using also low-quality samples in the training step, which takes the 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 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 little effort of manual labeling.

show abstract

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

Cited by 6 publications

References 40 publications

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

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

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

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