scholarly journals A New Method of Image Reconstruction for PET Using a Combined Regularization Algorithm

2020 ◽  
pp. 178-185
Author(s):  
Abdelwahhab Boudjelal ◽  
Abderrahim El Moataz ◽  
Zoubeida Messali
Keyword(s):  
Image Reconstruction ◽  
New Method ◽  
Regularization Algorithm

scholarly journals Phase and Image Reconstruction from Pupil-Plane Observations in the Near Infrared: Integration of the Phasors

1994 ◽  
Vol 158 ◽  
pp. 197-200
Author(s):  
J.-L. Monin ◽  
N. Ageorges ◽  
L. Desbat ◽  
C. Perrier
Keyword(s):  
Image Reconstruction ◽  
Near Infrared ◽  
New Method ◽  
Data Set ◽  
Preliminary Results ◽  
Cross Spectrum ◽  
The Cross ◽  
Simulated Images ◽  
Pupil Plane

A new method to reconstruct the phase of bidimensional interferograms, obtained through pupil-plane interferometry is presented. We compute the average complex phasor components of the cross-spectrum on a data set to reconstruct the original unperturbed phase. We present preliminary results on simulated images which visibility phases are distorted using a model of atmospheric perturbed wavefronts.

A new method of image reconstruction with high resolution in x-ray coded aperture imaging

2005 ◽  
Author(s):  
Jiasheng Hu ◽  
Lihong Cheng ◽  
Xu Wu ◽  
Yi Sun ◽  
Yuhong Bai
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
New Method ◽  
Coded Aperture ◽  
X Ray ◽  
Coded Aperture Imaging

scholarly journals An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xiezhang Li ◽  
Guocan Feng ◽  
Jiehua Zhu
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Numerical Experiments ◽  
Projection Data ◽  
Hard Thresholding ◽  
L1 Norm ◽  
Regularization Algorithm ◽  
Alternating Direction ◽  
Algebraic Framework ◽  
Ct Image Reconstruction

The l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l1-norm and l0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l0-norm regularization.

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