In-flight characterization of spatial quality using point spread functions

2004 ◽  
pp. 151-170
Author(s):  
T Choi ◽  
M Rangaswamy ◽  
D Helder
Keyword(s):  
Point Spread Functions ◽  
Point Spread ◽  
Spatial Quality

Paleokarst reservoirs: Efficient and flexible characterization using point-spread function-based convolution modeling

2021 ◽  
pp. 1-65
Author(s):  
Kristian Jensen ◽  
Martin Kyrkjebø Johansen ◽  
Isabelle Lecomte ◽  
Xavier Janson ◽  
Jan Tveranger ◽  
...  
Keyword(s):  
Seismic Data ◽  
Convolution Operators ◽  
Point Spread Functions ◽  
Point Spread ◽  
Spread Function ◽  
Open Cavities ◽  
Modeling Experiment ◽  
Seismic Characterization ◽  
Seismic Images

Paleokarst originate from collapse, degradation and infill of karstified rock, and typically feature spatially heterogeneous elements such as breakdown products, sediment infills and preserved open cavities on all scales. Paleokarst may further contain aquifer or hydrocarbon reservoirs as well as pose a drilling hazard during exploration. Seismic characterization of paleokarst reservoirs therefore remains both a challenging and important task. We illustrate how the application of 2(3)D spatial convolution operators, referred to as point-spread functions (PSFs), allows for seismic modeling of complex and heterogeneous paleokarst geology at a cost equivalent to conventional repeated 1D convolution. Unlike the latter, which only considers vertical resolution effects, PSF-based convolution modeling yields simulated prestack depth migrated images accounting for 3D resolution effects both vertically and laterally caused by acquisition geometries, frequency-band limitations, and propagation effects in the overburden. We confirm the validity of the approach by a comparison of modeled results to results obtained from a published physical modeling experiment. Finally, we present four additional separate case studies to highlight the usability and flexibility of the approach by assessing different issues and challenges pertaining to characterizing and interpreting seismic features of paleokarst. Through PSF-based convolution modeling, geoscientists working with paleokarst seismic data may be better able to understand how various acquisition and modeling parameters affect seismic images of paleokarst geology.

Depth profiling of gold nanoparticles and characterization of point spread functions in reconstructed and human skin using multiphoton microscopy

2011 ◽  
Vol 5 (1) ◽  
pp. 85-96 ◽  
Author(s):  
Hagar I. Labouta ◽  
Martina Hampel ◽  
Sibylle Thude ◽  
Katharina Reutlinger ◽  
Karl-Heinz Kostka ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Human Skin ◽  
Multiphoton Microscopy ◽  
Depth Profiling ◽  
Point Spread Functions ◽  
Point Spread

SU-GG-I-127: Characterization of Point Spread Functions of Indirect Digital Radiography Detectors

2010 ◽  
Vol 37 (6Part5) ◽  
pp. 3130-3131
Author(s):  
I Sechopoulos ◽  
A Schmitz ◽  
JW Eberhard
Keyword(s):  
Digital Radiography ◽  
Point Spread Functions ◽  
Point Spread

scholarly journals The Sloan Digital Sky Survey extended point spread functions

2019 ◽  
Vol 491 (4) ◽  
pp. 5317-5329 ◽  
Author(s):  
Raúl Infante-Sainz ◽  
Ignacio Trujillo ◽  
Javier Román
Keyword(s):  
Scattered Light ◽  
Sloan Digital Sky Survey ◽  
Coma Cluster ◽  
Point Spread Functions ◽  
Astronomical Surveys ◽  
Point Spread ◽  
Deep Imaging ◽  
Sky Survey ◽  
Science Philosophy

ABSTRACT A robust and extended characterization of the point spread function (PSF) is crucial to extract the photometric information produced by deep imaging surveys. Here, we present the extended PSFs of the Sloan Digital Sky Survey (SDSS), one of the most productive astronomical surveys of all time. By stacking ∼1000 images of individual stars with different brightness, we obtain the bidimensional SDSS PSFs extending over 8 arcmin in radius for all the SDSS filters (u, g, r, i, z). This new characterization of the SDSS PSFs is near a factor of 10 larger in extension than previous PSFs characterizations of the same survey. We found asymmetries in the shape of the PSFs caused by the drift scanning observing mode. The flux of the PSFs is larger along the drift scanning direction. Finally, we illustrate with an example how the PSF models can be used to remove the scattered light field produced by the brightest stars in the central region of the Coma cluster field. This particular example shows the huge importance of PSFs in the study of the low-surface brightness Universe, especially with the upcoming of ultradeep surveys, such as the Large Synoptic Survey Telescope (LSST). Following a reproducible science philosophy, we make all the PSF models and the scripts used to do the analysis of this paper publicly available (snapshot v0.4-0-gd966ad0).

scholarly journals Axial localization and tracking of self-interference nanoparticles by lateral point spread functions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yongtao Liu ◽  
Zhiguang Zhou ◽  
Fan Wang ◽  
Günter Kewes ◽  
Shihui Wen ◽  
...  
Keyword(s):  
Mirror Image ◽  
Excitation Wavelength ◽  
Far Field ◽  
Point Spread Functions ◽  
Microscopy Imaging ◽  
Localization Accuracy ◽  
Field Patterns ◽  
Atomic Force ◽  
Point Spread ◽  
Sensing Technology

AbstractSub-diffraction limited localization of fluorescent emitters is a key goal of microscopy imaging. Here, we report that single upconversion nanoparticles, containing multiple emission centres with random orientations, can generate a series of unique, bright and position-sensitive patterns in the spatial domain when placed on top of a mirror. Supported by our numerical simulation, we attribute this effect to the sum of each single emitter’s interference with its own mirror image. As a result, this configuration generates a series of sophisticated far-field point spread functions (PSFs), e.g. in Gaussian, doughnut and archery target shapes, strongly dependent on the phase difference between the emitter and its image. In this way, the axial locations of nanoparticles are transferred into far-field patterns. We demonstrate a real-time distance sensing technology with a localization accuracy of 2.8 nm, according to the atomic force microscope (AFM) characterization values, smaller than 1/350 of the excitation wavelength.

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