The Durham Adaptive Optics Simulation Platform (DASP): Current status

Basden, Alastair; Bharmal, Nazim Ali; Jenkins, David R.; Morris, Tim; Osborn, James; Jia, Peng; Staykov, L.

doi:10.1016/j.softx.2018.02.005

Cited by 24 publications

(14 citation statements)

References 22 publications

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“…The atmospheric turbulence is continuous medium which is hard to model in an analytical way. Right now, the Monte Carlo model is widely used for performance evaluation of adaptive optics systems (Carbillet et al 2005;Wang et al 2010;Rigaut & Van Dam 2013;Basden et al 2018). In a Monte Carlo model, we divide the extended atmospheric turbulence into several different layers.…”

Section: The Concept Of Psf-net Modelling 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: The Concept Of Psf-net Modelling 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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“…We generated PSF templates with size of 24 × 24 pixels in 400 locations equally distributed in a field of view of 14 arcmin, as shown in the figure 7. The atmospheric turbulence phase screen is generated by the method proposed in Jia et al (2015a,b) and we use the Durham Adaptive Optics Simulation Platform to generate PSFs (Basden et al 2018). We add different levels of noise to PSFs to make them as simulated star images in dataset7 and dataset8.…”

Section: Test the Dae Based Psf Model With A Simulated Telescope Affementioning

confidence: 99%

Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

Jia

et al. 2020

Monthly Notices of the Royal Astronomical Society

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The point spread function reflects the state of an optical telescope and it is important for data post-processing methods design. For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose to use the denoising autoencoder, a type of deep neural network, to model the point spread function of wide field small aperture telescopes. The denoising autoencoder is a pure data based point spread function modelling method, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them as realizations of the point spread function, i.e., simulated star images. Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and can map any star images obtained by wide field small aperture telescopes directly to its point spread function, which could be used to design data post-processing or optical system alignment methods.

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“…According to our experience, we select feature maps: Feature 1, Feature 2, Feature 3 and Feature 4 from the trained VGG16 as candidate feature maps in this paper, as shown in Figure 2. To evaluate the representative ability of these feature maps, we generate several short exposure point spread functions (PSF) with different D/r0 through Monte-Carlo simulation (Jia et al, 2015b;Basden et al, 2018). Then we convolve high resolution solar images with these PSFs to generate blurred images as shown in Figure 3.…”

Section: Algorithm 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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The Durham Adaptive Optics Simulation Platform (DASP): Current status

Cited by 24 publications

References 22 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

Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

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

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