Deconvolution of Images

Idier, Jérôme; Blanc‐Féraud, Laure

doi:10.1002/9780470611197.ch6

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…(18) corresponds to the Wiener filtering [Ref. 1, chap. 4] with a non-null prior mean bold-italicmbold-italico o^(f)=h˜*(f)i˜(f)+SnSo(f)m˜o(f)|h˜(f)|2+SnSo(f).…”

Section: Results On Simulated Astronomical Datamentioning

confidence: 99%

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Marginalized myopic deconvolution of adaptive optics corrected images using Markov chain Monte Carlo methods

Yan,

Mugnier,

Giovannelli

et al. 2023

J. Astron. Telesc. Instrum. Syst.

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.Adaptive optics (AO) corrected image restoration is particularly difficult, as it suffers from the lack of knowledge on the point spread function (PSF) in addition to usual difficulties. An efficient approach is to marginalize the object out of the problem and to estimate the PSF and (object and noise) hyperparameters only, before deconvolving the image using these estimates. Recent works have applied this marginal myopic deconvolution method, based on the maximum a posteriori estimator, combined with a parametric model of the PSF, to a series of AO-corrected astronomical and satellite images. However, this method does not enable one to infer global uncertainties on the parameters. We propose a PSF estimation method, which consists in choosing the minimum mean square error estimator and computing the latter as well as the associated uncertainties thanks to a Markov chain Monte Carlo algorithm. We validate our method by means of realistic simulations, in both astronomical and satellite observation contexts. Finally, we present results on experimental images for both applications: an astronomical observation on Very Large Telescope/spectro-polarimetric high-contrast exoplanet research with the Zimpol instrument and a ground-based LEO satellite observation at Côte d’Azur Observatory’s 1.52 m telescope with Office National d'Etudes et de Recherches Aérospatiales’s ODISSEE AO bench.

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“…(18) corresponds to the Wiener filtering [Ref. 1, chap. 4] with a non-null prior mean bold-italicmbold-italico o^(f)=h˜*(f)i˜(f)+SnSo(f)m˜o(f)|h˜(f)|2+SnSo(f).…”

Section: Results On Simulated Astronomical Datamentioning

confidence: 99%

“…However, the correction is partial and residual blurring remains, impacting high spatial frequencies of the observed object. Therefore, the observation system includes post-processing to restore the high frequencies 1 …”

Section: Introductionmentioning

confidence: 99%

Marginalized myopic deconvolution of adaptive optics corrected images using Markov chain Monte Carlo methods

Yan,

Mugnier,

Giovannelli

et al. 2023

J. Astron. Telesc. Instrum. Syst.

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A Study on Convolution using Half-Precision Floating-Point Numbers on GPU for Radio Astronomy Deconvolution

Seznec

Gac

Ferrari

et al. 2018

2018 IEEE International Workshop on Signal Processing Systems (SiPS)

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The use of IEEE 754-2008 half-precision floatingpoint numbers is an emerging trend in Graphical Processing Units' architecture. Being such a compact way of representing data, its use may speed up programs by reducing the memory bandwidth usage and allowing hardware designers to fit more computing units within the same die space. In this paper, we highlight the acceleration offered by the use of half floatingpoint numbers over different implementations of the same operation, a 2D convolution. We show that even though it may lead up to a significant speed-up, the degradation brought by this new format is not always negligible. Then, we choose a deconvolution problem inspired by the SKA radio-telescope processing pipeline to show how half floats behave in a more complex application.

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Deconvolution of Images

Cited by 2 publications

References 35 publications

Marginalized myopic deconvolution of adaptive optics corrected images using Markov chain Monte Carlo methods

Marginalized myopic deconvolution of adaptive optics corrected images using Markov chain Monte Carlo methods

A Study on Convolution using Half-Precision Floating-Point Numbers on GPU for Radio Astronomy Deconvolution

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