scholarly journals Sparse spatial filter via a novel objective function minimization with smooth ℓ1 regularization

2013 ◽  
Vol 8 (3) ◽  
pp. 282-288 ◽  
Author(s):  
Ibrahim Onaran ◽  
N. Firat Ince ◽  
A. Enis Cetin
2013 ◽  
Vol 443 ◽  
pp. 22-26
Author(s):  
Yong Xing Lin ◽  
Xiao Yan Xu ◽  
Xian Dong Zhang

In the paper, we discuss the image demising models, based on partial differential equations. It is through the use of the concept of variations in the calculus of the objective function minimization problem, defines the image processing tasks. The results show that the model expands 2d thermal diffusion equation. Therefore, it is easy to get solution is to use a simple iterative process.


Author(s):  
David A. Ansley

The coherence of the electron flux of a transmission electron microscope (TEM) limits the direct application of deconvolution techniques which have been used successfully on unmanned spacecraft programs. The theory assumes noncoherent illumination. Deconvolution of a TEM micrograph will, therefore, in general produce spurious detail rather than improved resolution.A primary goal of our research is to study the performance of several types of linear spatial filters as a function of specimen contrast, phase, and coherence. We have, therefore, developed a one-dimensional analysis and plotting program to simulate a wide 'range of operating conditions of the TEM, including adjustment of the:(1) Specimen amplitude, phase, and separation(2) Illumination wavelength, half-angle, and tilt(3) Objective lens focal length and aperture width(4) Spherical aberration, defocus, and chromatic aberration focus shift(5) Detector gamma, additive, and multiplicative noise constants(6) Type of spatial filter: linear cosine, linear sine, or deterministic


1981 ◽  
Vol 64 (9) ◽  
pp. 113-120
Author(s):  
Kohji Takemura ◽  
Suteo Tsutsumi

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