A point-spread-function-aware filtered backprojection algorithm for focal-plane-scanning optical projection tomography

2016 ◽  
Author(s):  
Kevin G. Chan ◽  
Michael Liebling
Keyword(s):  
Point Spread Function ◽  
Focal Plane ◽  
Filtered Backprojection ◽  
Optical Projection Tomography ◽  
Optical Projection ◽  
Point Spread ◽  
Spread Function ◽  
Plane Scanning

scholarly journals Point spread function based image reconstruction in optical projection tomography

2017 ◽  
Vol 62 (19) ◽  
pp. 7784-7797 ◽  
Author(s):  
Anna K Trull ◽  
Jelle van der Horst ◽  
Willem Jan Palenstijn ◽  
Lucas J van Vliet ◽  
Tristan van Leeuwen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Point Spread Function ◽  
Optical Projection Tomography ◽  
Optical Projection ◽  
Point Spread ◽  
Spread Function

scholarly journals Recognition of wavefront aberrations types corresponding to single Zernike functions from the pattern of the point spread function in the focal plane using neural networks

2020 ◽  
Vol 44 (6) ◽  
pp. 923-930
Author(s):  
I.A. Rodin ◽  
S.N. Khonina ◽  
P.G. Serafimovich ◽  
S.B. Popov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Point Spread Function ◽  
Focal Plane ◽  
Prediction Errors ◽  
Intensity Pattern ◽  
Data Set ◽  
Point Spread ◽  
The Neural Network ◽  
Spread Function

In this work, we carried out training and recognition of the types of aberrations corresponding to single Zernike functions, based on the intensity pattern of the point spread function (PSF) using convolutional neural networks. PSF intensity patterns in the focal plane were modeled using a fast Fourier transform algorithm. When training a neural network, the learning coefficient and the number of epochs for a dataset of a given size were selected empirically. The average prediction errors of the neural network for each type of aberration were obtained for a set of 15 Zernike functions from a data set of 15 thousand PSF pictures. As a result of training, for most types of aberrations, averaged absolute errors were obtained in the range of 0.012 – 0.015. However, determining the aberration coefficient (magnitude) requires additional research and data, for example, calculating the PSF in the extrafocal plane.

scholarly journals Neural networks application to determine the types and magnitude of aberrations from the pattern of the point spread function out of the focal plane

2021 ◽  
Vol 2086 (1) ◽  
pp. 012148
Author(s):  
P A Khorin ◽  
A P Dzyuba ◽  
P G Serafimovich ◽  
S N Khonina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Spread Function ◽  
Focal Plane ◽  
Prediction Errors ◽  
Point Spread ◽  
The Neural Network ◽  
Spread Function ◽  
The Mean

Abstract Recognition of the types of aberrations corresponding to individual Zernike functions were carried out from the pattern of the intensity of the point spread function (PSF) outside the focal plane using convolutional neural networks. The PSF intensity patterns outside the focal plane are more informative in comparison with the focal plane even for small values/magnitudes of aberrations. The mean prediction errors of the neural network for each type of aberration were obtained for a set of 8 Zernike functions from a dataset of 2 thousand pictures of out-of-focal PSFs. As a result of training, for the considered types of aberrations, the obtained averaged absolute errors do not exceed 0.0053, which corresponds to an almost threefold decrease in the error in comparison with the same result for focal PSFs.

Point spread function reconstruction coupling AO telemetry and focal plane images

2018 ◽  
Author(s):  
Olivier A. Martin ◽  
Carlos M. Correia ◽  
Benoit Neichel ◽  
Thierry Fusco ◽  
Peter L. Wizinowich ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Focal Plane ◽  
Point Spread ◽  
Spread Function ◽  
Function Reconstruction

CdZnTe Focal Plane Polarimetric Performances for a Laue Lens Point Spread Function

2012 ◽  
Vol 59 (4) ◽  
pp. 1582-1590
Author(s):  
J. B. de Campos ◽  
R. M. Curado da Silva ◽  
C. P. Gloster ◽  
Alessandro Pisa ◽  
E. Caroli ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Focal Plane ◽  
Laue Lens ◽  
Point Spread ◽  
Spread Function

scholarly journals A Space-Variant Deblur Method for Focal-Plane Microwave Imaging

2018 ◽  
Vol 8 (11) ◽  
pp. 2166
Author(s):  
Shenshen Luan ◽  
Shuguo Xie ◽  
Tianheng Wang ◽  
Xuchun Hao ◽  
Meiling Yang ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Focal Plane ◽  
Imaging System ◽  
Millimetre Wave ◽  
Point Spread ◽  
Processing Step ◽  
Wide Range ◽  
Spread Function ◽  
Polar Coordinate ◽  
Space Variant

In the research of passive millimetre wave (PMMW) imaging, the focal plane array (FPA) can realize fast, wide-range imaging and detection. However, it has suffered from a limited aperture and off-axis aberration. Thus, the result of FPA is usually blurred by space-variant point spread function (SVPSF) and is hard to restore. In this paper, a polar-coordinate point spread function (PCPSF) model is presented to describe the circle symmetric characteristic of space-variant blur, and a log-polar-coordinate transformation (LPCT) method is propagated as the pre-processing step before the Lucy–Richardson algorithm to eliminate the space variance of blur. Compared with the traditional image deblur method, LPCT solves the problem by analyzing the physical model instead of the approximating it, which has proved to be a feasible way to deblur the FPA imaging system.

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