Recognition of wavefront aberrations corresponding to individual Zernike functions from the pattern of the point scattering function in the focal plane using neural networks

2021 ◽  
Author(s):  
Ilya Rodin ◽  
Pavel Serafimovich ◽  
Sergey Popov
Keyword(s):  
Neural Networks ◽  
Focal Plane ◽  
Scattering Function ◽  
Wavefront Aberrations

Adaptive nonuniformity correction for IR focal-plane arrays using neural networks

1991 ◽  
Author(s):  
Dean A. Scribner ◽  
Kenneth A. Sarkady ◽  
Melvin R. Kruer ◽  
John T. Caulfield ◽  
J. D. Hunt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
Nonuniformity Correction

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.

Intelligent sensors research using pulse-coupled neural networks for focal plane image processing

1996 ◽  
Author(s):  
Gregory L. Tarr ◽  
Richard A. Carreras ◽  
Christopher R. DeHainaut ◽  
Xavier Clastres ◽  
Laurent Freyss ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Focal Plane ◽  
Coupled Neural Networks ◽  
Intelligent Sensors ◽  
Plane Image ◽  
Pulse Coupled Neural Networks

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.

scholarly journals Performance Comparison of Wavefront-Sensorless Adaptive Optics Systems by Using of the Focal Plane

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Huizhen Yang ◽  
Zhen Zhang ◽  
Jian Wu
Keyword(s):  
Adaptive Optics ◽  
Focal Plane ◽  
Control Algorithm ◽  
Performance Comparison ◽  
Convergence Speed ◽  
Control Algorithms ◽  
System Control ◽  
Wavefront Aberrations ◽  
Intensity Measurements ◽  
Better Than

The correction capability and the convergence speed of the wavefront-sensorless adaptive optics (AO) system are compared based on two different system control algorithms, which both use the information of focal plane. The first algorithm is designed through the linear relationship between the second moment of the aberration gradients and the masked far-field intensity distribution and the second is stochastic parallel gradient descent (SPGD), which is the most commonly used algorithm in wavefront-sensorless AO systems. A wavefront-sensorless AO model is established with a 61-element deformable mirror (DM) and a CCD. Performance of the two control algorithms is investigated and compared through correcting different wavefront aberrations. Results show that the correction ability of AO system based on the proposed control algorithm is obviously better than that of AO system based on SPGD algorithm when the wavefront aberrations increase. The time needed by the proposed control algorithm is much less than that of SPGD when the AO system achieves similar correction results. Additionally, the convergence speed of the proposed control algorithm is independent of the turbulence strength while the number of intensity measurements needed by SPGD increases as the turbulence strength magnifies.

Analog neural networks for focal-plane image processing

1990 ◽  
Author(s):  
Bimal P. Mathur ◽  
Shih-Chi Liu ◽  
H. Taichi Wang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Focal Plane ◽  
Plane Image ◽  
Analog Neural Networks

Tandem scanning reflected light microscopy: How it works and applications

1986 ◽  
Vol 44 ◽  
pp. 84-87
Author(s):  
Alan Boyde ◽  
Milan Hadravský ◽  
Mojmír Petran ◽  
Timothy F. Watson ◽  
Sheila J. Jones ◽  
...  
Keyword(s):  
Light Microscopy ◽  
Focal Plane ◽  
Central Symmetry ◽  
Single Line ◽  
Scanning Microscope ◽  
Reflected Light ◽  
Illuminating Light ◽  
Illuminating Beam

The principles of tandem scanning reflected light microscopy and the design of recent instruments are fully described elsewhere and here only briefly. The illuminating light is intercepted by a rotating aperture disc which lies in the intermediate focal plane of a standard LM objective. This device provides an array of separate scanning beams which light up corresponding patches in the plane of focus more intensely than out of focus layers. Reflected light from these patches is imaged on to a matching array of apertures on the opposite side of the same aperture disc and which are scanning in the focal plane of the eyepiece. An arrangement of mirrors converts the central symmetry of the disc into congruency, so that the array of apertures which chop the illuminating beam is identical with the array on the observation side. Thus both illumination and “detection” are scanned in tandem, giving rise to the name Tandem Scanning Microscope (TSM). The apertures are arranged on Archimedean spirals: each opposed pair scans a single line in the image.

Imaging sub-micron objects with light microscopy: Visualization of the T-4 bacteriophage

1989 ◽  
Vol 47 ◽  
pp. 834-835
Author(s):  
Malcolm Brown ◽  
Reynolds M. Delgado ◽  
Michael J. Fink
Keyword(s):  
Electron Microscopy ◽  
Light Microscopy ◽  
Focal Plane ◽  
Phase Contrast ◽  
Dark Field ◽  
E Coli ◽  
Polystyrene Beads ◽  
Television Camera ◽  
Transmission Electron ◽  
Universal Microscope

While light microscopy has been used to image sub-micron objects, numerous problems with diffraction-limitations often preclude extraction of useful information. Using conventional dark-field and phase contrast light microscopy coupled with image processing, we have studied the following objects: (a) polystyrene beads (88nm, 264nm, and 557mn); (b) frustules of the diatom, Pleurosigma angulatum, and the T-4 bacteriophage attached to its host, E. coli or free in the medium. Equivalent images of the same areas of polystyrene beads and T-4 bacteriophages were produced using transmission electron microscopy.For light microscopy, we used a Zeiss universal microscope. For phase contrast observations a 100X Neofluar objective (N.A.=1.3) was applied. With dark-field, a 100X planachromat objective (N.A.=1.25) in combination with an ultra-condenser (N.A.=1.25) was employed. An intermediate magnifier (Optivar) was available to conveniently give magnification settings of 1.25, 1.6, and 2.0. The image was projected onto the back focal plane of a film or television camera with a Carl Zeiss Jena 18X Compens ocular.

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