A novel optimal fuzzy color image enhancement using particle swarm optimization

2013 ◽  
Author(s):  
M. Hanmadlu ◽  
Shaveta Arora ◽  
Gaurav Gupta ◽  
Latika Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Image Enhancement ◽  
Color Image ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Color Image Enhancement

scholarly journals An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoping Su ◽  
Wei Fang ◽  
Qing Shen ◽  
Xiulan Hao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Enhancement ◽  
Control Strategy ◽  
Color Image ◽  
Particle Swarm ◽  
Parameter Control ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Adaptive Parameter Control

Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in degraded images. In this paper, two novel objective functions based on the normalized incomplete Beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively. Using these objective functions, the parameters of transform functions are estimated by the quantum-behaved particle swarm optimization (QPSO). We also propose an improved QPSO with an adaptive parameter control strategy. The QPSO and the AQPSO algorithms, along with genetic algorithm (GA) and particle swarm optimization (PSO), are tested on several benchmark grayscale and color images. The results show that the QPSO and AQPSO perform better than GA and PSO for the enhancement of these images, and the AQPSO has some advantages over QPSO due to its adaptive parameter control strategy.

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

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.

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