Regularizing Image Reconstruction for Gradient-Domain Rendering with Feature Patches

2016 ◽  
Vol 35 (2) ◽  
pp. 263-273 ◽  
Author(s):  
M. Manzi ◽  
D. Vicini ◽  
M. Zwicker
Keyword(s):  
Image Reconstruction ◽  
Gradient Domain

Unsupervised Image Reconstruction for Gradient‐Domain Volumetric Rendering

2020 ◽  
Vol 39 (7) ◽  
pp. 193-203
Author(s):  
Zilin Xu ◽  
Qiang Sun ◽  
Lu Wang ◽  
Yanning Xu ◽  
Beibei Wang
Keyword(s):  
Image Reconstruction ◽  
Gradient Domain ◽  
Volumetric Rendering

Image reconstruction from few-view CT data by gradient-domain dictionary learning

2016 ◽  
Vol 24 (4) ◽  
pp. 627-638 ◽  
Author(s):  
Zhanli Hu ◽  
Qiegen Liu ◽  
Na Zhang ◽  
Yunwan Zhang ◽  
Xi Peng ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Dictionary Learning ◽  
Gradient Domain ◽  
Ct Data

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
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