The color image enhancement using SSGA steady state genetic algorithm
Other Title(s)
تحسين الصورة الملونة باستخدام الخوارزمية الجينية المنتظمة
Dissertant
Thesis advisor
Comitee Members
al-Huraybat, Muhammad
Abu Dalhum, Abd al-Latif
University
Middle East University
Faculty
Faculty of Information Technology
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2012
English Abstract
This thesis presents a model for enhancing the color image using the steady state genetic algorithm.
We modified the fitness function to get more accurate result and less noise.
In this research we will use the Hue saturation intensity (HSV) color model, after enhancing the S, H and V components, the transformation will be made to RGB color model.
We have developed three models contrasts, brightness and saturation for enhancing the colourful and chromaity of the image with different types of input - output and different type of parameter.
The three models are compared based on their ability to train with lowest error values.
To use these models the input RGB color image is converted to an intensity image using Space Variant Luminance Map SVLM.
The 2D gamma correction used to enhance the Luminance component.
We enhanced Luna image using SSGA method then we compare the enhanced image performance with previous methods images by calculating the PSNR peak signal noise ratio and MSE mean square error The saturation enhancement is done by two phases : finding the most saturation color and adjusting the saturation ratio, where the contrast component enhanced using the adaptive factor.
The results in this thesis enhanced the previous results due to the combination of color and chromaity of the image.
We used the Matlab ver.
(7) with C++ language.
Three image quality metrics are applied to the color enhanced images.
They are PSNR (Peak Signal-to-Noise ratio), RMSE (root mean square error) and MSE (mean square error).
The comparison is made with PSNR, RMSE and MSE values.
Finally it has been observed that our model, SSGA, yields better results than the previous results for enhancing the color image.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
110
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature survey and related work.
Chapter Three : Genetic algorithm.
Chapter Four : Methodology.
Chapter Five : Results of the enhancement.
Chapter Six : Conclusion and future work.
References.
American Psychological Association (APA)
al-Sabah, Ala Abd Allah Awad. (2012). The color image enhancement using SSGA steady state genetic algorithm. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-700154
Modern Language Association (MLA)
al-Sabah, Ala Abd Allah Awad. The color image enhancement using SSGA steady state genetic algorithm. (Master's theses Theses and Dissertations Master). Middle East University. (2012).
https://search.emarefa.net/detail/BIM-700154
American Medical Association (AMA)
al-Sabah, Ala Abd Allah Awad. (2012). The color image enhancement using SSGA steady state genetic algorithm. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-700154
Language
English
Data Type
Arab Theses
Record ID
BIM-700154
