Reconstruction algorithm an object image based on a series of its images distorted in a random environment

2021 ◽  
Author(s):  
Aleksey Borisenkov ◽  
Oleg Goryachkina ◽  
Nikolay Gusev ◽  
Dmitry Sukhanov
Keyword(s):  
Random Environment ◽  
Reconstruction Algorithm ◽  
Object Image

scholarly journals On the Danger of Applying Statistical Reconstruction Methods in the Case of Missing Phase Information

1979 ◽  
Vol 49 ◽  
pp. 95-101
Author(s):  
J.C. Dainty ◽  
M.A. Fiddy ◽  
A.H. Greenaway
Keyword(s):  
Fourier Transform ◽  
Real Data ◽  
Reconstruction Algorithm ◽  
Object Image ◽  
Reconstruction Algorithms ◽  
Phase Information ◽  
Object Reconstruction ◽  
Statistical Reconstruction ◽  
Reconstruction Methods ◽  
Actual Use

The use of entropy as a basis for object/image reconstruction procedures is not new, but with the appearance of new, faster algorithms the actual use of these algorithms for the reconstruction of objects from ‘real’ data is likely to increase.The purpose of this contribution is not to discourage such applications, but to illustrate that, under certain circumstances, there is a need for caution in interpreting the results obtained from such algorithms. Specifically, we shall show that the application of statistical methods to problems of object reconstruction, in situations where only the modulus of the object Fourier transform is known, could lead to wholly false conclusions. Indeed, we shall primarily be concerned here with situations for which there is no ‘correct’ solution. In such situations it is pointless to speak of ‘safe’ object reconstruction algorithms. The important point here is that the user of a statistically based ‘object reconstruction algorithm’ may be totally ignorant of whether or not he is working in this régime.

Driven periodic structures in random environment

1999 ◽  
Vol 09 (PR10) ◽  
pp. Pr10-85-Pr10-87
Author(s):  
V. M. Vinokur
Keyword(s):  
Random Environment ◽  
Periodic Structures

Driven elastic system in a random environment

1999 ◽  
Vol 09 (PR10) ◽  
pp. Pr10-69-Pr10-71 ◽  
Author(s):  
P. Chauve ◽  
T. Giamarchi ◽  
P. Le Doussal
Keyword(s):  
Random Environment ◽  
Elastic System

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

MICROWAVE IMAGING OF EMBEDDED OBJECT USING AN ENHANCED IMAGE RECONSTRUCTION ALGORITHM

2015 ◽  
Vol 74 (20) ◽  
pp. 1793-1801
Author(s):  
Sidi Mohammed Chouiti ◽  
Lotfi Merad ◽  
Sidi Mohammed Meriah ◽  
Xavier Raimundo
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Microwave Imaging ◽  
Image Reconstruction Algorithm
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