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point spread functionblurred imagedeconvolutionThe objective of present research is to find the correct dimensions of point spread function (PSF), which is a crucial step in blind deconvolution of linearly blurred images. Usually size of PSF is estimated by trial and error, based on user ...
In the three-dimensional biological microscopic image restoration processing, the selection of three dimensional point spread function (3D-PSF) space will determine the restoration effects and restoration time. Based on the theoretical analysis about the effect of 3D-PSF sampling interval and layers on...
PSF = otf2psf(OTF) computes the inverse Fast Fourier Transform of the optical transfer function (OTF) and creates a point-spread function (PSF), centered at the origin. example PSF = otf2psf(OTF,sz) specifies the size, sz, of the output point-spread function.Examples...
When successful, the reconstruction produces an image of the medium, but for the Delay-and-Sum (DAS) approach, the point spread function (PSF) is a diffraction-limited result with sidelobes. Distortions may occur due to using a reconstruction sound speed that is different from the true sound...
W. Isotropic three-dimensional super-resolution imaging with a self-bending point spread function. Nat. Photonics 8, 302–306 (2014). 61. Pavani, S. R., DeLuca, J. G. & Piestun, R. Polarization sensitive, three-dimensional, single-molecule imaging of cells with a double- helix system....
We present HiFi-SIM, a high-fidelity SIM reconstruction algorithm, by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background...
The changing point-spread function: single-molecule-based super-resolution imaging Histochem. Cell Biol., 141 (2014), pp. 577-585 CrossrefView in ScopusGoogle Scholar 15 S.R.P. Pavani, M.A. Thompson, et al., W.E. Moerner Three-dimensional, single-molecule fluorescence imaging beyond the...
MATLAB: za=norminv(ea1+ea) Finally, the estimated home team expected points is: Est.AwayTeamPoints=za·σa+a¯ where a¯ is the average away team winning points σa is the standard deviation of away team points Estimating Home Team Winning Spread The estimated home team victory margin ...