Deconvolution in Astronomy: A Review

By Jean-Luc Starck, E. Pantin, Fionn Murtagh. 2002

440 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fd9c5e7f76aa0802aee9e59d99665fcbd8f3916f

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In Applied Mathematics

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Multiscale CLEAN Deconvolution of Radio Synthesis Images (2008, background): “Thus the number of degrees of freedom in the reconstruction can be signiﬁcantly reduced with a concomittant increase in various measures of performance, such as robustness, stability, and signal to noise.”
Blind deconvolution in scientific imaging & computational photography (2011, methodology): “Next to it, a Knox-Thompson reconstruction is shown, which was obtained using Speckle1, a reconstruction software by Stelzer (2009). For the reconstruction, 300 Knox-Thompson and 100 triple correlation phase pairs were used. Finally, the rightmost image shows the result of our basic algorithm withou”
The Grid Method for In‐plane Displacement and Strain Measurement: A Review and Analysis (2016, background): “[175] for instance in the representative field of astronomy).”
Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data (2008, positive): “Our most recent development, constrained spherical deconvolution (CSD), makes use of a non-negativity constraint on the fibre orientation distribution, which provides sufficient additional information to alsopermit the use of superresolution, whereby the number of estimated parameters exceeds the nu”
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning (2016, neutral): “The Shannon-Nyquist sampling theorem [35, 41] limits the ability of deconvolution techniques in removing the eﬀect of the PSF, particularly in the presence of noise [32, 10, 43].”
DeconvolutionLab2: An open-source software for deconvolution microscopy. (2017, methodology): “For an in-depth understanding and a more complete overview of the deconvolution ﬁeld, we refer to the reviews [3,5,6,23] that cover most of the methods described here.”
Multi-Wiener SURE-LET Deconvolution (2013, neutral): “MAGE DECONVOLUTION is a standard linear inverse problem with applications in medical imaging [2], seismology [3], astronomy [4], remote sensing [5], optical imaging [6], etc.”
Directly Observing the Galaxies Likely Responsible for Reionization (2016, methodology): “This tool has been widely used in astronomy for purposes such as de-noising images (Starck et al. 2002), analyzing the clustering of galaxies (Slezak et al. 1993; Escalera & MacGillivray 1995; Martínez et al. 2005), and detecting non-Gaussianity in the cosmic microwave background (Starck et al.…”
Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution (2007, background): “Using prior information allows the possibility of estimating more parameters than actual measurements (a concept known as super-resolution; Starck et al., 2002), provided that the amount of prior information is sufficient to constrain the additional degrees of freedom.”
Regularizing Flows for Constrained Matrix-Valued Images (2004, neutral): “in computed tomography and in astronomy [8, 27].”
Adaptive Optics for Astronomy (2012, methodology): “Starck et al. (2002) describe a number of methods to perform deconvolution based on different mathematical approaches, each of which is suited to specific circumstances, for example whether the astronomical object has well defined edges (e.g. planets, asteroids) or not (e.g. galaxies).”
Image deblurring with Poisson data: from cells to galaxies (2009, methodology): “More recent reviews, mainly focused also on astronomical applications, are [106, 125].”
High Dynamic Range Imaging (2015, methodology): “Such scattering can be modeled with point spread functions (PSF) and removed using deconvolution [146].”