Image reconstruction by volume holograms with frequency conversion

1994 ◽  
Author(s):  
S. M. Karpuck ◽  
Alexander S. Rubanov ◽  
Alexei L. Tolstik ◽  
A. V. Chaley
Keyword(s):  
Image Reconstruction ◽  
Frequency Conversion ◽  
Volume Holograms

Criteria and Methods for Reliable Three-Dimensional Image Reconstruction

1974 ◽  
Vol 32 ◽  
pp. 330-331
Author(s):  
R. A. Crowther
Keyword(s):  
Image Reconstruction ◽  
Finite Number ◽  
Density Distribution ◽  
Electron Micrographs ◽  
Mathematical Problem ◽  
Three Dimensional ◽  
Ideal Case ◽  
Dimensional Image ◽  
General Object ◽  
The Ideal

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow

Image reconstruction from noisy digital holograms

1990 ◽  
Vol 137 (5) ◽  
pp. 351 ◽  
Author(s):  
C.P. Mariadassou ◽  
B. Yegnanarayana
Keyword(s):  
Image Reconstruction ◽  
Digital Holograms

Research on expanding the range of reproduction of magnetic flux density of the DC field of the state primary standard GET12-2011

2020 ◽  
pp. 3-7
Author(s):  
Vladlen Ya. Shifrin ◽  
Denis I. Belyakov ◽  
Alexander E. Shilov ◽  
Denis D. Kosenko
Keyword(s):  
Magnetic Flux ◽  
Magnetic Induction ◽  
Frequency Conversion ◽  
Magnetic Measurements ◽  
Primary Standard ◽  
Uncertainty Budget ◽  
The State ◽  
State Primary Standard ◽  
Magnetic Flux Density ◽  
Constant Field

The results of works aimed at increasing the level of uniformity of measurements of the magnetic induction of a constant field – the basic value in the field of magnetic measurements. A set of equipment for reproducing a unit of magnetic induction of a constant field in the range of 1–25 mT was created and described. The inclusion of this complex in the State primary standard of units of magnetic induction, magnetic flux, magnetic moment and magnetic induction gradient GET 12-2011 will ensure the reproduction and direct transmission of the unit of permanent magnetic induction in the ranges of not only weak (10–3–1 mT), but medium (1–25 mT) and strong (0.025–1 T) magnetic fields. A quantum cesium magnetometer based on the resolved structure of cesium atoms was created to transmit the unit of magnetic induction to the region of medium fields. The procedure for calculating the frequency conversion coefficients to magnetic induction of the created quantum cesium magnetometer is described. The uncertainty budget for reproducing a unit of magnetic induction of a constant field using the created complex is estimated.

PEMBELAJARAN SIMULASI PENCITRAAN CT DENGAN MENGGUNAKAN PRINSIP REKONSTRUKSI CITRA DALAM SOFTWARE MATLAB

2015 ◽  
Vol 8 (3) ◽  
pp. 161
Author(s):  
Samuel Gideon
Keyword(s):  
Image Reconstruction ◽  
Ct Imaging ◽  
The Body ◽  
Reconstruction Technique ◽  
Imaging Features ◽  
Back Projection ◽  
Cross Sectional ◽  
Low Contrast ◽  
Image Reconstruction Technique ◽  
Imaging Algorithms

This research was conducted as a learning alternatives for study of CT (computed tomograpghy) imaging using image reconstruction technique which are inversion matrix, back projection and filtered back projection. CT imaging can produce images of objects that do not overlap. Objects more easily distinguishable although given the relatively low contrast. The image is generated on CT imaging is the result of reconstruction of the original object. Matlab allows us to create and write imaging algorithms easily, easy to undersand and gives applied and exciting other imaging features. In this study, an example cross-sectional image recon-struction performed on the body of prostate tumors using. With these methods, medical prac-titioner (such as oncology clinician, radiographer and medical physicist) allows to simulate the reconstruction of CT images which almost resembles the actual CT visualization techniques.Keywords : computed tomography (CT), image reconstruction, Matlab

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