An approach to enhancing image contrast using genetic algorithm

AN APPROACH OF CONTRAST
ENHANCEMENT USING GENETIC
ALGORITHM
Supervised By
Dr. Taskeed Jabid
Assistant Professor
Department of Computer Science & Engineering
East West University
Presented By
S.M.Sadman Sadid
Nayeem Hasan

OVERVIEW
Introduction
Objectives
Application of IE
Working Method
Experimental Result
Conclusion
Future Work
Reference

INTRODUCTION
Image Enhancement
Image enhancement is basically the method of manipulating or
improving an image where the visual appearance of output image is
more compatible than original image for a specific or dynamic
application.
Image Enhancement Techniques
Spatial Domain Enhancement
Frequency Domain Enhancement

INTRODUCTION CONT..
Genetic Algorithm
A heuristic search technique used in computing and Artificial
Intelligence to find optimized solutions to search problems using
techniques inspired by evolutionary biology- Initialize, Selection,
Fitness, Crossover Mutation.
A genetic algorithm (GA) is extraordinary for exploring solutions for
complex search issues.

INTRODUCTION CONT..
Fundamental Working Procedure of GA
Initialization
Fitness Calculation
Selection
Crossover
Mutation
Termination

OBJECTIVES
Main Objective:
Make an image more suitable than original image
Contrast Enhancement
Natural looking image
To make image suitable for dynamic field

APPLICATIONS OF IE CONT..
Medical Sector

Forensics

Astrophotography

Atmospheric Science

Misuse of IE
 In e-commerce

GENETIC ALGORITHM IMPLEMENTATION
There are five basic function of genetic algorithm
• Chromosome structure.
• Fitness function
• Selection algorithm
• Crossover
• Mutation

CONT..
Chromosome Structure
• First, convert a RGB image to grey level image where the intensity of
the image is between 0 and 255.
• For implementing the algorithm first we create an array to store the
image intensity value.
• Then we remove the duplicate value from the array and sort it in
ascending order.
• After removing the duplicate value we get a new array and create
chromosome from it.

CONT..
Fig 1: An array generated from image with index
17 25 13 11 29 17 21 25 31 91
1 2 3 4 5 6 7 8 9 10
11 13 17 21 25 31 29 91
1 2 3 4 5 6 7 8
Fig 2: First chromosome generated from the image

CONT..
• Then create an array of random numbers of chromosome size where
the first index value is initialized to 0 and the last index value is
initialized to 255.
• Then sort the random array in ascending order and put random array
value in the image to create the initial population for the genetic
algorithm.

CHROMOSOME STRUCTURE
0 52 91 151 153 202 221 225
1 2 3 4 5 6 7 8
Fig 3: Random array of chromosome size
17 25 13 11 29 17 21 25 31 91
1 2 3 4 5 6 7 8 9 10
(a)
0 51 91 151 153 0 202 52 221 225
1 2 3 4 5 6 7 8 9 10
(b)
Fig 4: (a) The image array (b) The image array after putting random value

FITNESS FUNCTION
• The fitness of the chromosome of the is calculated by the following:
fitness(x)=log(log(E(I(x)))) * n_edges(I(x))
E(I(x)) = 𝑥 𝑦 𝛿ℎ1(𝑥, 𝑦)2+𝛿𝑣1(𝑥, 𝑦)2
• Where,
𝛿ℎ1(𝑥, 𝑦)= g1(x+1,y-1) + 2g1(x+1,y) + g1(x+1,y+1) - g1(x-1,y-1) - 2g1(x-1,y) - g1(x-
1,y+1)
𝛿𝑣1(𝑥, 𝑦)= g1(x-1,y+1) + 2g1(x,y+1) + g1(x+1,y+1) - g1(x-1,y-1) - 2g1(x,y-1) - g1(x+1,y-
1)

SELECTION ALGORITHM
• Selection of individual is based on their fitness value. The individual
which have more fitness value than other have more chance of going
to the next generation.
• The main idea of this process is to crate stronger generation than
previous based on fitness value. So the individual which have grater
fitness value than other have grater possibility of survival.
• On the contrary the weaker individual have less possibility of survival.

SELECTION ALGORITHM
• Selection of individual is based on this equation:
PS – (PS * PC)
• So the number of individual that are selected for next generation is PS
* PC. The process is preformed until the terminating condition is meet.

CONT..
• Crossover:
For crossover two chromosome have selected randomly and
crossover operation has proceeded.
Form the crossover operation two children have created.
Therefore
PS * PC individual has selected from the selection process.
Two points have selected randomly from them. Finally, each
new individuals are sorted in ascending order.

CONT..
• Crossover:
0 29 19 161 201 75 83 51 166 225
0 55 91 151 153 202 22 221 24 225
(b)
(a)

CONT..
• Crossover:
Random Value: 4
0 29 19 161 201 75 83 51 166 225
0 55 91 151 153 202 22 221 24 225
(b)
(a)

CONT..
• Crossover:
Random Value: 4
0 55 91 151 201 75 83 51 166 225
0 29 19 151 153 202 22 221 24 225
(d)
(c)

CONT..
• After Crossover the value will be sorted:
0 51 55 75 83 91 151 166 201 225
0 19 22 24 29 151 153 202 221 225
(d)
(c)

CONT..
• Mutation:
For mutation, a random number is selected and the number of the
element is selected for mutation. If fitness value is less than mutation
constant, mutation process will be preceded.
 Five percent of the individual elements are selected randomly for
mutation. For each element, a random integer number that should be
less than or equal to the next element value and more than or equal
to the previous element value is generated.
This random number is replaced by element. The value of mutation
constant Pm should be between 0.015 to 0.02.

CONT..
• Mutation:
0 29 19 161 201 75 83 51 166 225
(a)

CONT..
• After Mutation and sorting:
0 29 65 72 83 122 161 166 201 225
(a)

TERMINATING CONDITION
• In genetic algorithm the number of iteration is infinity. So a terminating
condition is used to stop this process and iteration.
When the iteration meet the maximum number of iteration.
When the difference of best fitness in two consecutive generation
is less than ϵ. The value of ϵ has been considered as 0.02 *
best_fitness.

CONCLUSION
• So, we found genetic algorithm for image contrast enhancement
especially when input image has low dynamic range. This method
works on simple chromosome structure to enhance the image. Some
iteration has done on the process to get the enhanced image. To
confirm the result is compared to some standards. The experiment
result is satisfactory though it consumes a lot processing time.
Besides, experiment results demonstrated that the enhanced images
are suitable for different application field.

FUTURE WORK
• In future we were going to combine some other algorithm and to
compare those result with this one.

REFERENCE
[1] Sara Hashemi, Soheila Kaiani, Navid Noroozi, Mohsen Ebrahimi Moghaddam, An image
contrast enhancement method based on genetic algorithm, 11 Dec 2009, Available:
http://www.elsevier.com/locate/patrec
[2] M. Abdullah-Al-Wadud, Md. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae, A dynamic
histogram equalization for image contrast enhancement, IEEE Trans. Consumer Electron., vol. 53,
no. 2, pp. 593- 600, May 2007.
[3] Gaurav Mohan Singh, Mahipal Singh Kohli and Manoj Diwakar, A Review of Image
Enhancement Techniques in Image Processing, HCTL Open IJTIR, Volume 5, September 2013.
[4] S.S. Bedi, Rati Khandelw, Various Image Enhancement Techniques-A Critical Review,
IJARCCE, Vol. 2, Issue 3, March 2013.

An approach to enhancing image contrast using genetic algorithm

An approach to enhancing image contrast using genetic algorithm

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