Global and Local Contrast Enhancement for Image by Genetic Algorithm and Wavelet Neural Network

2006 ◽  
pp. 910-919 ◽  
Author(s):  
Changjiang Zhang ◽  
Xiaodong Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Contrast Enhancement ◽  
Wavelet Neural Network ◽  
Local Contrast ◽  
Local Contrast Enhancement ◽  
Global And Local

scholarly journals Adaptive Multi-Scale Entropy Fusion De-hazing based on Fractional Order

2018 ◽  
Author(s):  
Uche A. Nnolim
Keyword(s):  
Contrast Enhancement ◽  
Fractional Order ◽  
Local Contrast ◽  
Multi Scale ◽  
Fully Adaptive ◽  
Local Contrast Enhancement ◽  
Fractional Filter ◽  
Global And Local ◽  
Enhancement Algorithm ◽  
Experimental Comparisons

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled colour correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields better edge and contrast enhancement results without halo effect, colour degradation and is faster and more adaptive than all other algorithms from the literature.

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

scholarly journals Clinical evaluation of ‘local contrast enhancement’ for oral fluorescein angiograms

Eye ◽  
2000 ◽  
Vol 14 (3) ◽  
pp. 318-318 ◽  
Author(s):  
Richard S B Newsom ◽  
Chanjira Sinthanayothin ◽  
James Boyce ◽  
Anthony G Casswell ◽  
Tom H Williamson
Keyword(s):  
Contrast Enhancement ◽  
Clinical Evaluation ◽  
Local Contrast ◽  
Local Contrast Enhancement

The Coal Mine Flooding Emergency Ability Evaluation Based on GA-WNN

2012 ◽  
Vol 452-453 ◽  
pp. 782-788
Author(s):  
Jin Feng Wang ◽  
Li Jie Feng ◽  
Zhao Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Evaluation Model ◽  
Wavelet Neural Network ◽  
Time Frequency ◽  
Model Based ◽  
Time Frequency Localization ◽  
Mine Flooding ◽  
Ability Evaluation

For the coal resources working which are affected by the coal mine flooding seriously, this paper make an analysis on the factors which affect the coal mine flooding emergency ability evaluation model based on GA-WNN is established through the wavelet neural network value which is optimized with genetic algorithm. This model combined the global optimization ability of genetic algorithm with the time-frequency localization of wavelet neural network. This combination can make up for many defects (for example, the neural network structure should be given artificially, the function can got local minimum easily and so on). Therefore, the local mine flooding emergency ability evaluation model based on genetic algorithm and wavelet neural network have higher reliability and calculation ability, and is beneficial to the pre-control management for coal mine flooding rescue.

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