Coded-aperture x- or γ-ray telescope with least-squares image reconstruction. III. Data acquisition and analysis enhancements

1997 ◽  
Vol 68 (6) ◽  
pp. 2404-2411 ◽  
Author(s):  
Truman P. Kohman
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Least Squares ◽  
Coded Aperture ◽  
Γ Ray

scholarly journals Gamma-Ray Imaging of the Galactic Center Region

1989 ◽  
Vol 136 ◽  
pp. 581-585
Author(s):  
W. R. Cook ◽  
D. M. Palmer ◽  
T. A. Prince ◽  
S. M. Schindler ◽  
C. H. Starr ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Galactic Center ◽  
Coded Aperture ◽  
Center Region ◽  
Gamma Ray Imaging ◽  
The Galaxy ◽  
Γ Ray ◽  
Luminous Objects

The Caltech imaging γ-ray telescope was launched by balloon from Alice Springs, NT, Australia and performed observations of the galactic center during the period 12.62 to 13.00 April 1988 UT. The first coded-aperture images of the galactic center region at energies above 30 keV show a single strong γ-ray source which is located 0.7±0.1° from the galactic nucleus and is tentatively identified as 1E1740.7-2942. If the source is at the distance of the galactic center, it is one of the most luminous objects in the galaxy at energies from 35 to 200 keV.

Image Reconstruction on Quincunx Patterned Green Channel

2013 ◽  
Vol 717 ◽  
pp. 493-496
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Image Reconstruction ◽  
Least Squares ◽  
Least Squares Method ◽  
Interpolation Method ◽  
Experimental Results ◽  
Color Filter ◽  
Image Artifacts ◽  
Green Channel ◽  
Filter Array ◽  
Single Sensor

This paper addresses the issue of the quincunx patterned green channel interpolation method that is obtained by single sensor cameras. Our goal is to reconstruct the green channel in Bayer color filter array (CFA) data. We present a new filter-based method for the reduction of image artifacts in green channel. To reconstruct green channel, we trained a filter using least squares method. Experimental results confirm the effectiveness of the proposed method. Compared to other bilinear and bicubic filters, the improvement in quality has been achieved.

Fundamental Sensor Development in Electrical Resistance Tomography

2015 ◽  
Vol 73 (6) ◽  
Author(s):  
Ling En Hong ◽  
Ruzairi Hj. Abdul Rahim ◽  
Anita Ahmad ◽  
Mohd Amri Md. Yunus ◽  
Khairul Hamimah Aba ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Electrical Resistance ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Region Of Interest ◽  
Display System ◽  
Electrical Resistance Tomography ◽  
Fundamental Understanding ◽  
Collection Strategies

This paper will provide a fundamental understanding of one of the most commonly used tomography, Electrical Resistance Tomography (ERT). Unlike the other tomography systems, ERT displayed conductivity distribution in the Region of Interest (ROI) and commonly associated to Sensitivity Theorem in their image reconstruction. The fundamental construction of ERT includes a sensor array spaced equally around the imaged object periphery, a Data Acquisition (DAQ), image reconstruction and display system. Four ERT data collection strategies that will be discussed are Adjacent Strategy, Opposite Strategy, Diagonal Strategy and Conducting Boundary Strategy. We will also explain briefly on some of the possible Data Acquisition System (DAQ), forward and inverse problems, different arrangements for conducting and non-conducting pipes and factors that influence sensor arrays selections. 

The use of an active coded aperture for improved directional measurements in high energy γ-ray astronomy

1980 ◽  
Vol 27 (1) ◽  
pp. 375-380 ◽  
Author(s):  
A. Johansson ◽  
B. L. Beron ◽  
L. Campbell ◽  
R. Eichler ◽  
P. Gorodetsky ◽  
...  
Keyword(s):  
High Energy ◽  
Coded Aperture ◽  
Γ Ray

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics

2020 ◽  
Vol 26 (3) ◽  
pp. 403-412 ◽  
Author(s):  
Pavel Potocek ◽  
Patrick Trampert ◽  
Maurice Peemen ◽  
Remco Schoenmakers ◽  
Tim Dahmen
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Scanning Electron Microscopy ◽  
Image Reconstruction ◽  
Data Acquisition ◽  
Real Life ◽  
Large Field ◽  
Acquisition Time ◽  
Scanning Electron Microscopy Data ◽  
Scanning Electron

AbstractWith the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2–3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

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