Parallel blind deconvolution of astronomical images based on the fractal energy ratio of the image and regularization of the point spread function

Jia, Peng; Cai, Dongmei; Wang, Dong

doi:10.1007/s10686-014-9405-2

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…Several analytical PSF reconstruction algorithms are proposed for different telescopes including on-axis PSF reconstruction (Jolissaint et al 2012), off-axis PSF reconstruction (Witzel et al 2014) for ordinary AO systems, PSF reconstruction for ground layer adaptive optic system (Peter 2010;Villecroze et al 2012), multi-object adaptive optic system (Martin et al 2016) and multi-conjugate adaptive optic system (Gilles et al 2018). These analytical PSF reconstruction algorithms can provide PSFs for image deconvolution (Fusco et al 2000;Jia et al 2014). However, reconstructed PSFs provided by these algorithms need additional wavefront measurements and are effective only within the field of view of AO systems.…”

Section: Classic Psf Modeling Methodsmentioning

confidence: 99%

PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

Jia

Huang

et al. 2020

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Ground based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instruments design and image restoration methods development. Because the point spread function can reflect performance of the whole optic system, it is appropriate to use the point spread function to describe atmospheric turbulence induced aberrations. Assuming point spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a non-parametric point spread function -PSF-NET. The PSF-NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF-NET with simulated and real observation data, we find that a well trained PSF-NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF-NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF-NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF-NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would be benefit to develop new observation methods for ground based optical telescopes.

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Section: Classic Psf Modeling Methodsmentioning

confidence: 99%

PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

Jia

Huang

et al. 2020

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“…These properties indicate us that although texture features are not organized in a regular way for solar images, the relative weights of different texture features are stable, which means if we measure the relative weights of texture features in a statistical way, the probability distribution in the same wavelength should be the same. We can use multifractal properties to describe texture features in solar images (Jia et al 2014;Peng et al 2017). The multi-fractal property of texture features means the spatial distribution of textures in solar images (coded by texture features) satisfies the same continuous power spectrum and for different scales, the exponents of the spectrum are different.…”

Section: The Multi-fractal Property Of Texture Features In Solar Imagesmentioning

confidence: 99%

Solar Image Restoration with the CycleGAN Based on Multi-fractal Properties of Texture Features

Jia

Huang

Cai

et al. 2019

ApJL

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Texture is one of the most obvious characteristics in solar images and it is normally described by texture features. Because textures from solar images of the same wavelength are similar, we assume texture features of solar images are multi-fractals. Based on this assumption, we propose a pure databased image restoration method: with several high resolution solar images as references, we use the Cycle-Consistent Adversarial Network to restore burred images of the same steady physical process, in the same wavelength obtained by the same telescope. We test our method with simulated and real observation data and find that our method can improve the spatial resolution of solar images, without loss of any frames. Because our method does not need paired training set or additional instruments, it can be used as a post-processing method for solar images obtained by either seeing limited telescopes or telescopes with ground layer adaptive optic system.

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“…In different wavelengths, solar images are different and these images are composed of different texture features as shown in Figure 1. Texture features in solar images may be self-similar and these images are usually called fractal (Jia, Cai, and Wang, 2014), such as the granulation. In other wavelengths, they are not self-similar in the whole spatial scale, which means they can not be described by a spectrum with the same exponent.…”

Section: Principle Of the Perception Evaluationmentioning

confidence: 99%

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

et al. 2019

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Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques. Based on the assumption that texture features in solar images are multi-fractal which can be extracted by a trained deep neural network as feature maps, a new reduced-reference objective image quality metric, the perception evaluation is proposed. The perception evaluation is defined as cosine distance of Gram matrix between feature maps extracted from high resolution reference images and that from blurred images. We evaluate performance of the perception evaluation with simulated blurred images and real observation images. The results show that with a high resolution image as reference, the perception evaluation can give robust estimate of image quality for solar images of different scenes.

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Parallel blind deconvolution of astronomical images based on the fractal energy ratio of the image and regularization of the point spread function

Cited by 8 publications

References 33 publications

PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

Solar Image Restoration with the CycleGAN Based on Multi-fractal Properties of Texture Features

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

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