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About 10,000 papers for “Point spread function”
Pieter Sijtsma, Conventional Beamforming · 2014
2Acoustic array measurements in closed wind tunnel test section Airbus A340 model in DNW-LLF 8x6 m2 closed test section (AWIATOR) wall mounted array 3CLEAN-CLASSIC (1) microphone array (93 mics) source plane 6 m2 m 2 m monopole source Array simulations
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2005
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SpringerReference · 2011
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National Cancer Institute · Definitions · 2020
A spatial plot showing values in an image that result from an ideal point activity concentration source.T he abscissa of this plot shows relative distance from the point's location.
Cited by 0Open access
Gigi Galiana (511748), R. Todd Constable (135961) · Figshare · 2015
<p>Using the proposed 8(a–c) as well as a map of (signal at target voxel)/(mean signal in all other voxels), shown in (d). As can be seen from the individual single point images, the proposed encoding results in noise-like artifacts distributed throughout the image, leading to a relatively uniform ability to localize signal across the field of view.</p>
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Michael Hirsch, Christian J. Schuler, Stefan Harmeling et al. · 2015
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2014
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Loku Gunawardhana (21539734), Wilna Moree (2995290), Jiaming Guo (264640) et al. · Figshare · 2025
Table containing raw data used to generate the point spread function cross-sections in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0325155#pone.0325155.g004" target="_blank">Fig 4</a>. (XLSX)
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Loku Gunawardhana (21539734), Wilna Moree (2995290), Jiaming Guo (264640) et al. · Figshare · 2025
Raw cross-sections of the point spread function used to produce <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0325155#pone.0325155.g004" target="_blank">Fig 4</a>. (ZIP)
Cited by 0Open access
Gentile, M., Courbin, F., Meylan, G. · arXiv (Cornell University) · 2012
Planned wide-field weak lensing surveys are expected to reduce the statistical errors on the shear field to unprecedented levels. In contrast, systematic errors like those induced by the convolution with the point spread function (PSF) will not benefit from that scaling effect and will require very accurate modeling and correction. While numerous methods have been devised to carry out the PSF correction itself, modeling of the PSF shape and its spatial variations across the instrument field of view has, so far, attracted much less attention. This step is nevertheless crucial because the PSF is only known at star positions while the correction has to be performed at any position on the sky. A reliable interpolation scheme is therefore mandatory and a popular approach has been to use low-order bivariate polynomials. In the present paper, we evaluate four other classical spatial interpolation methods based on splines (B-splines), inverse distance weighting (IDW), radial basis functions (RBF) and ordinary Kriging (OK). These methods are tested on the Star-challenge part of the GRavitational lEnsing Accuracy Testing 2010 (GREAT10) simulated data and are compared with the classical polynomial fitting (Polyfit). We also test all our interpolation methods independently of the way the PSF is modeled, by interpolating the GREAT10 star fields themselves (i.e., the PSF parameters are known exactly at star positions). We find in that case RBF to be the clear winner, closely followed by the other local methods, IDW and OK. The global methods, Polyfit and B-splines, are largely behind, especially in fields with (ground-based) turbulent PSFs. In fields with non-turbulent PSFs, all interpolators reach a variance on PSF systematics $σ_{sys}^2$ better than the $1\times10^{-7}$ upper bound expected by future space-based surveys, with the local interpolators performing better than the global ones.
Cited by 0Open access
Marc Gentile, F. Courbin, G. Meylan · Springer Link (Chiba Institute of Technology) · 2012
Planned wide-field weak lensing surveys are expected to reduce the statistical errors on the shear field to unprecedented levels. In contrast, systematic errors like those induced by the convolution with the point spread function (PSF) will not benefit from that scaling effect and will require very accurate modeling and correction. While numerous methods have been devised to carry out the PSF correction itself, modeling of the PSF shape and its spatial variations across the instrument field of view has, so far, attracted much less attention. This step is nevertheless crucial because the PSF is only known at star positions while the correction has to be performed at any position on the sky. A reliable interpolation scheme is therefore mandatory and a popular approach has been to use low-order bivariate polynomials. In the present paper, we evaluate four other classical spatial interpolation methods based on splines (B-splines), inverse distance weighting (IDW), radial basis functions (RBF) and ordinary Kriging (OK). These methods are tested on the Star-challenge part of the GRavitational lEnsing Accuracy Testing 2010 (GREAT10) simulated data and are compared with the classical polynomial fitting (Polyfit). In all our methods we model the PSF using a single Moffat profile and we interpolate the fitted parameters at a set of required positions. This allowed us to win the Star-challenge of GREAT10, with the B-splines method. However, we also test all our interpolation methods independently of the way the PSF is modeled, by interpolating the GREAT10 star fields themselves (i.e., the PSF parameters are known exactly at star positions). We find in that case RBF to be the clear winner, closely followed by the other local methods, IDW and OK. The global methods, Polyfit and B-splines, are largely behind, especially in fields with (ground-based) turbulent PSFs. In fields with non-turbulent PSFs, all interpolators reach a variance on PSF systematics σ2sys better than the 1 × 10-7 upper bound expected by future space-based surveys, with the local interpolators performing better than the global ones.
Cited by 39Open access
Farnoud Kazemzadeh (710575), Alexander Wong (283533), Bradford B. Behr (710576) et al. · Figshare · 2015
<p>a) The interferogram observed and recorded by the detector with a specific region of interest bordered by the red rectangle. b) A zoom-in of the region of interest. c) The PSF of MOHI produced by performing a Fourier transform on the recorded interferogram.</p>
Cited by 0Open access
Wei Wang, Guohai Situ · Scientific Reports · 2017
Rotating point spread functions (PSF), such as the double helix (DH) PSF, are widely used in localization-based super-resolution imaging because of their large working depth range. In this article, we propose an interferometric DH PSF (iDH PSF) using two opposed objective lenses as in the 4Pi microscope. In the proposed iDH PSF, the super-resolution in the axial PSF is transferred to the azimuthal rotation. Moreover, we design an iDH PSF whose imaging range reaches 3 μm, which is roughly 3 times as much as that which can be obtained by using other interferometric localization-based super-resolution imaging methods.
Cited by 7Open access
M. C. Britton · 2006
The effects of anisoplanatism on the adaptive optics point spread function are investigated. A model is derived that combines observations of the guide star with an analytic formulation of anisoplanatism to generate predictions for the adaptive optics point spread function at arbitrary locations within the field of view. The analytic formulation captures the dependencies of anisoplanatism on aperture diameter, observing wavelength, angular offset, zenith angle and turbulence profile. The predictions of this model are compared to narrowband 2.12 µm and 1.65 µm images of a 21 arcsec binary (mv=7.3, 7.6) acquired with the Palomar Adaptive Optics System on the Hale 5 meter telescope. Contemporaneous measurements of the turbulence profile made with a DIMM/MASS unit are used together with images of the primary to predict the point spread function of the binary companion. Predicted companion Strehl ratios are shown to match measurements to within a few percent, whereas predictions based on the isoplanatic angle approximation are highly discrepant. The predicted companion point spread functions are shown to agree with observations to 10%. These predictions are used to measure the differential photometry between binary members to an accuracy of 1 part in 10 3, and the differential astrometry to an accuracy of 1 mas. Errors in the differential astrometry are shown to be dominated by differential atmospheric tilt jitter. These results are compared to other techniques that have been employed for photometry, astrometry, and high contrast imaging. Subject headings: instrumentation:adaptive optics, techniques: high angular resolution, techniques: photometric 1.
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Mustafa Mir (318053), S. Derin Babacan (318054), Michael Bednarz (318056) et al. · Figshare · 2015
<p>A) Comparison of raw and deconvolved. PSF in the x-y plane; the deconvolution process reduces the FWHM from 397 nm to 153 nm. B) Comparison of raw and deconvolved PSF in the x–z plane; the deconvolution process reduces the FWHM from 1218 nm to 357 nm. The dashed lines show the data and the circular markers indicate the Gaussian fit used to determine the FWHM.</p>
Cited by 0Open access
P. Magain, F. Courbin, M. Gillon et al. · 2006
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M. Gentile, F. Courbin, G. Meylan · Astronomy and Astrophysics · 2012
Planned wide-field weak lensing surveys are expected to reduce the statistical errors on the shear field to unprecedented levels. In contrast, systematic errors like those induced by the convolution with the point spread function (PSF) will not benefit from that scaling effect and will require very accurate modeling and correction. While numerous methods have been devised to carry out the PSF correction itself, modeling of the PSF shape and its spatial variations across the instrument field of view has, so far, attracted much less attention. This step is nevertheless crucial because the PSF is only known at star positions while the correction has to be performed at any position on the sky. A reliable interpolation scheme is therefore mandatory and a popular approach has been to use low-order bivariate polynomials. In the present paper, we evaluate four other classical spatial interpolation methods based on splines (B-splines), inverse distance weighting (IDW), radial basis functions (RBF) and ordinary Kriging (OK). These methods are tested on the Star-challenge part of the GRavitational lEnsing Accuracy Testing 2010 (GREAT10) simulated data and are compared with the classical polynomial fitting (Polyfit). In all our methods we model the PSF using a single Moffat profile and we interpolate the fitted parameters at a set of required positions. This allowed us to win the Star-challenge of GREAT10, with the B-splines method. However, we also test all our interpolation methods independently of the way the PSF is modeled, by interpolating the GREAT10 star fields themselves (i.e., the PSF parameters are known exactly at star positions). We find in that case RBF to be the clear winner, closely followed by the other local methods, IDW and OK. The global methods, Polyfit and B-splines, are largely behind, especially in fields with (ground-based) turbulent PSFs. In fields with non-turbulent PSFs, all interpolators reach a variance on PSF systematics σ2sys better than the 1 × 10-7 upper bound expected by future space-based surveys, with the local interpolators performing better than the global ones.
Cited by 75Open access
Birrer, Simon, Bhamre, Vikram, Nierenberg, Anna et al. · Open Repository and Bibliography (University of Liège) · 2022
PSFr empirically reconstructs an oversampled version of the point spread function (PSF) from astronomical imaging observations. The code provides a light-weighted API of a refined version of an algorithm originally implemented in lenstronomy (ascl:1804.012). It provides user support with different artifacts in the data and supports the masking of pixels, or the treatment of saturation levels. PSFr has been used to reconstruct the PSF from multiply imaged lensed quasar images observed by the Hubble Space Telescope in a crowded lensing environment and more recently with James Webb Space Telescope (JWST) imaging data for a wide dynamical flux range.
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Larry C. Andrews · SPIE eBooks · 2009
This excerpt gives a succinct explanation of the Point Spread Function and Modulation Transfer Function.
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Larry C. Andrews · SPIE eBooks · 2019
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