Iterative Regularization Techniques in Image Reconstruction

2000 ◽  
pp. 35-52 ◽  
Author(s):  
Martin Hanke
Keyword(s):  
Image Reconstruction ◽  
Iterative Regularization ◽  
Regularization Techniques

Accelerate Bregman MRI Image Reconstruction Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 2928-2931
Author(s):  
Xu Yang Wang ◽  
Ze Chao Zhong
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Mri Imaging ◽  
Iterative Regularization ◽  
Simulation Data ◽  
Regularization Algorithm ◽  
Sparse Model ◽  
Positive Meaning ◽  
Reconstruction Rate ◽  
Mri Image

CS used in MRI image reconstruction is a research hotspot recent year. For the problem that reconstruction rate slow in MRI image reconstruction based on CS .Type acceleration Bregman iterative regularization algorithm to solve the MRI imaging sparse model ,and use the accelerate gradient method and the Restring in processing . The simulation data express this algorithm effective enhance the reconstruction rate, It’s have positive meaning in MRI image reconstruction that have strict in time requirement.

scholarly journals Local and Non-local Regularization Techniques in Emission (PET/SPECT) Tomographic Image Reconstruction Methods

2015 ◽  
Vol 29 (3) ◽  
pp. 394-402 ◽  
Author(s):  
Munir Ahmad ◽  
Tasawar Shahzad ◽  
Khalid Masood ◽  
Khalid Rashid ◽  
Muhammad Tanveer ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Tomographic Image ◽  
Local Regularization ◽  
Tomographic Image Reconstruction ◽  
Reconstruction Methods ◽  
Regularization Techniques ◽  
Non Local

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.

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