Obtaining point spread function of penumbral encoding aperture with “expectation maximization” algorithm based on matched source-image pair experiment

2010 ◽  
Vol 81 (5) ◽  
pp. 053502 ◽  
Author(s):  
Y. L. Wu ◽  
H. S. Hu ◽  
T. K. Zhang ◽  
Z. H. Li ◽  
D. Chen
Keyword(s):  
Point Spread Function ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Source Image ◽  
Image Pair ◽  
Point Spread ◽  
Spread Function

The Optimization of Mathematic Model of Penumbral Imaging System’s Point Spread Function

2010 ◽  
Author(s):  
Ren-qiang Jiang ◽  
Yue-lei Wu ◽  
Zhi-rong Lu ◽  
Tie-jun Zhang
Keyword(s):  
Point Spread Function ◽  
Inertial Confinement Fusion ◽  
Imaging System ◽  
Mathematic Model ◽  
Diagnostic Methods ◽  
Source Image ◽  
Inertial Confinement ◽  
Point Spread ◽  
Penumbral Imaging ◽  
Spread Function

In inertial confinement fusion (ICF) project, there are many diagnostic methods. Neutron penumbral imaging is one of the important technologies to diagnose the information about neutron spatial and temporal distribution in burn region of the core of a compressed pellet in the low yield fusion. In this study, a linear space invariant neutron penumbral imaging system was designed and established with Monte Carlo method, the system’s point spread function (PSF) was obtained. By fitting the obtained PSF, several mathematic models were obtained and compared. The improved “logistic function” mathematic model was chosen to reconstruct the coded penumbral image and the original neutron source image “T” was successfully was obtained.

The Point Spread Function Reconstruction –II The Smooth PCA

2021 ◽  
Author(s):  
Lin Nie ◽  
Guoliang Li ◽  
John R Peterson ◽  
Chengliang Wei
Keyword(s):  
Principal Components ◽  
Point Spread Function ◽  
Expectation Maximization ◽  
High Efficiency ◽  
Cosmological Parameters ◽  
Weak Lensing ◽  
Reconstruction Accuracy ◽  
Point Spread ◽  
Spread Function ◽  
Star Images

Abstract Accurate shear measurement is a key topic in weak lensing community. Point Spread Function (PSF), which smears the observed galaxy image, plays one of the main roles in the systematic errors in shear measurement and must be treated carefully to avoid bias and errors in cosmological parameters. In this paper, we present new PSF measurement methods, Smooth-PCA (SPCA) and Improved-SPCA (iSPCA), which can reconstruct smooth PSFs with high efficiency. Our methods decompose the star images into smooth principal components by using the Expectation-Maximization-PCA (EMPCA) method, and the smooth principal components are composed by Moffatlets basis functions, which are derived from the Moffat function. We demonstrate our approaches based on simulated Moffat PSFs and PhoSim star images. The constructed smooth principal components show flexible and efficient as the same as EMPCA, and have more stable patterns than EMPCA under noises contamination. We then check the reconstruction accuracy on the shape of PSFs. We find that our methods are able to reconstruct the PSFs at the same precision as the EMPCA method which indicates and iSPCA are promising for weak lensing shear measurement.

Point spread function based feature optimization and fusion for underwater objet recognition

2013 ◽  
Vol 26 (11) ◽  
pp. 944-952 ◽  
Author(s):  
Huibin Wang ◽  
Rong Zhang ◽  
Zhe Chen ◽  
Lizhong Xu ◽  
Jie Shen
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Spread Function ◽  
Feature Optimization
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