Lossless Astronomical Image Compression and the Effects of Noise

Pence, W. D.; Seaman, R.; White, R. L.

doi:10.1086/599023

Cited by 45 publications

(43 citation statements)

References 12 publications

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“…techniques (such as gzip) than do the floating-point originals (Gaztañaga et al 2001;Watson 2002;White & Greenfield 1999;Pence et al 2009;Bernstein et al 2009). In the Δ ¼ 0:5σ representation, after lossless compression, storage and transmission of the image "costs" only a few bits per noisedominated pixel.…”

Section: Discussionmentioning

confidence: 99%

What Bandwidth Do I Need for My Image?

Price-Whelan¹

2010

PUBL ASTRON SOC PAC

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ABSTRACT. Computer representations of real numbers are necessarily discrete, with some finite resolution, discreteness, quantization, or minimum representable difference. We perform astrometric and photometric measurements on stars and co-add multiple observations of faint sources to demonstrate that essentially all of the scientific information in an optical astronomical image can be preserved or transmitted when the minimum representable difference is a factor of 2 finer than the root variance of the per-pixel noise. Adopting a representation this coarse reduces bandwidth for data acquisition, transmission, or storage, or permits better use of the system dynamic range, without sacrificing any information for downstream data analysis, including information on sources fainter than the minimum representable difference itself.

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Section: Discussionmentioning

confidence: 99%

What Bandwidth Do I Need for My Image?

Price-Whelan¹

2010

PUBL ASTRON SOC PAC

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“…The second data set corresponds to the observations of the Moon transit on February 25th, 2007 by the Extreme Ultraviolet Imager (EUVI; Howard et al 2008), a part of the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) instrument suite on board the STEREO-B spacecraft. These images are compressed using the RICE algorithm (Nightingale 2011), which is lossless (Pence et al 2009) and hence does not introduce additional errors in the PSF estimation.…”

Section: Experimental Datamentioning

confidence: 99%

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Gonzalez

Delouille

Jacques

2016

J. Space Weather Space Clim.

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Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.

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“…In the morning, the data are compressed and copied to two external 200 GB USB hard drives connected to the control computer. Using rice compression software (see Pence et al 2009Pence et al , 2010, and references therein), the raw data are compressed by a factor of ∼40%.…”

Section: Data Handlingmentioning

confidence: 99%

The KELT-South Telescope1

Pepper¹,

Kuhn²,

Siverd³

et al. 2012

Publications of the Astronomical Society of the Pacific

181

135

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ABSTRACT. The Kilodegree Extremely Little Telescope (KELT) project is a survey for new transiting planets around bright stars. KELT-South is a small-aperture, wide-field automated telescope located at Sutherland, South Africa. The telescope surveys a set of 26°× 26°fields around the southern sky and targets stars in the range of 8 < V < 10 mag, searching for transits by hot Jupiters. This article describes the KELT-South system hardware and software and discusses the quality of the observations. We show that KELT-South is able to achieve the necessary photometric precision to detect transits of hot Jupiters around solar-type main-sequence stars.

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Lossless Astronomical Image Compression and the Effects of Noise

Cited by 45 publications

References 12 publications

What Bandwidth Do I Need for My Image?

What Bandwidth Do I Need for My Image?

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

The KELT-South Telescope1

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