A Survey on Image Enhancement Techniques: Classical Spatial Filter, Neural Network, Cellular Neural Network, and Fuzzy Filter

Author(s):  
D. H. Rao ◽  
Patavardhan Prashant Panduranga
2020 ◽  
Vol 50 (12) ◽  
pp. 1850
Author(s):  
Xiaofang HU ◽  
Shukai DUAN ◽  
Yue ZHOU ◽  
Li LUO ◽  
Yawen ZHENG

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.


2008 ◽  
Vol 21 (2-3) ◽  
pp. 349-357 ◽  
Author(s):  
Hisashi Aomori ◽  
Tsuyoshi Otake ◽  
Nobuaki Takahashi ◽  
Mamoru Tanaka

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