Post on 12-Sep-2021
IMAGE FUSION USING EVOLUTIONARY ALGORITHM (GA)
V Jyothi 1), B Rajesh Kumar 1), P Krishna Rao 2), D V Rama Koti Reddy 2)
1) GITAM University, Visakhapatnam, AP, India, burrajyothi@yahoo.co.in
2) Andhra University, Visakhapatnam, AP, India, rajesh_burra@gitam.edu
Abstract: Image fusion is the process of combining
images taken from different sources to obtain better
situational awareness. In fusing source images the
objective is to combine the most relevant information
from source images into composite image. Genetic
algorithm is used for solving optimization problems.
Genetic algorithm can be employed to image fusion where
some kind of parameter optimization is required.
In this paper we proposed genetic algorithm based
schemes for image fusion and proved that these schemes
perform better than the conventional methods through
comparison of parameters namely image quality index,
mutual information, root mean square error and peak
signal to noise ratio.
Keywords: Genetic Algorithm, Image quality Index,
Mutual Information.
1. INTRODUCTION For remotely sensed images, some have
good spectral information and the others have
geometric resolution, how to integrate these two
kinds of images into one image is a very
interesting thing in Image processing, which is
also called image fusion.
Image fusion is emerging as a vital
technology in many military, surveillance and
medical applications. It is a sub area of the more
general topic of data fusion, dealing with image
and video data. The ability to combine
complementary information from a range of
distributed sensors with different modalities can
be used to provide enhanced performance for
visualization, detection or classification tasks.
Multi-sensor data often present complementary
information about the scene or object of interest,
and thus image fusion provides an effective
method for comparison and analysis of such
data. There are several benefits of multi-sensor
image fusion: wider spatial and temporal
coverage, extended range of operation,
decreased uncertainty, improved reliability and
increased robustness of the system performance.
In several application scenarios, image
fusion is only an introductory stage to another
task, e.g. human monitoring. Therefore, the
performance of the fusion algorithm must be
measured in terms of improvement in the
following tasks. For example, in classification
systems, the common evaluation measure is the
number of the correct classifications. This
system
evaluation requires that the”true” correct
classifications are known. However, in
experimental setups the ground-truth data might
not be available. In many applications the
human perception of the fused image is of
fundamental importance and as a result the
fusion results are mostly evaluated by subjective
criteria. Objective image fusion performance
evaluation is a tedious task due to different
application requirements and the lack of a
clearly defined ground-truth. Various fusion
algorithms presented in this project. Several
objective performance measures for image
fusion have been proposed where the
knowledge of ground-truth is not assumed.
There are many Image Fusion
techniques based on signal, pixel, feature and
symbol level fusion. In many situations, a
single image cannot depict the scene properly.
In these cases, scene is captured through more
than one sensors, but human and machine
processing is better suited with a single image,
so therefore we need to fuse the images
obtained from different sensors to obtain a
single composite image which contains relevant
information of source images.
2. GENETIC ALGORITHM A variety of algorithms have been
evolved from nature. Genetic algorithm is one
of the simplest and most popular evolutionary
VJyothi,B.Rajesh Kumar,P.Krishna Rao,D.V.Rama Koti Reddy, Int. J. Comp. Tech. Appl., Vol 2 (2), 322-326
322
ISSN:2229-6093
algorithms. Genetic Algorithms (here onwards
called as GA) are based on natural selection
discovered by Charles Darwin. GA makes use
of the simplest representation, reproduction and
diversity mechanism. Optimization with GA is
performed through natural exchange of genetic
material between parents. Offspring’s are
formed from parent genes. Fitness of offspring’s
is evaluated. The fittest individuals are allowed
to breed only.
GA's are being used in different
applications such as function Optimization,
System Identification and Control, Image
Processing, Parameter Optimization of
Controllers, Multi-Objective Optimization, etc.
Algorithm
• Choose initial population
• Evaluate the fitness of each individual in
population
• Repeat
• Select best-ranking individuals to
reproduce a new population
• Breed new generation through crossover
and mutation to give birth to offspring
• Evaluate the individual fitness of the
offspring
• Replace worst ranked part of population
with offspring
• Until some termination condition is met
3. IMAGE FUSION TECHNIQUES
Pixel level Average method
This technique is a basic and straight forward
technique and fusion could be achieved by simple
averaging corresponding pixels in each input image
as:
Pixel level Weighted average method
We add some weights to the individual images and
perform the averaging technique as follows:
where W1 and W2 are the weights.
Pixel level weighted average method using GA
In this method the weights are estimated using the
GA and a new optimized image is obtained from the
average method using the optimized weights.
Where GA(W1) is the optimized value of weight
W1 and GA(W2) is the optimized value of weight
W2.
1. DWT based image fusion
In wavelet image fusion scheme, the source images
I1(a, b) and I2(a, b) are decomposed into
approximation and detailed coefficients at required
level using DWT. The approximation and detailed
coefficients of both images are combined using
fusion rule f. The fused image could be obtained by
taking the inverse discrete wavelet transform
(IDWT) as:
The fusion rule used is simply averages the
approximation coefficients and picks the detailed
coefficient in each sub band with the largest
magnitude.
2. Weighted average DWT based image fusion
In this method additional weights are selected along
with the DWT of the images. The fused image can
be obtained by taking the inverse discrete wavelet
transform (IDWT) as:
3. Weighted average DWT based image fusion using
GA
In this method additional weights are estimated
using GA along with the DWT of the images. The
fused image can be obtained by taking the inverse
discrete wavelet transform (IDWT) as:
4. EVALUATION CRITERIA
Objective image quality measures play an important
role in various image processing applications. There
are different types of object quality or distortion
assessment approaches. The fused images are
evaluated, taking the following parameters into
consideration.
Root Mean Square error (RMSE)
The root mean square error (RMSE) between each
unsharpened MS band and corresponding sharpened
band can also be computed as a measure of spectral
fidelity. It measures the amount of change per pixel
due to the processing.
VJyothi,B.Rajesh Kumar,P.Krishna Rao,D.V.Rama Koti Reddy, Int. J. Comp. Tech. Appl., Vol 2 (2), 322-326
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ISSN:2229-6093
The RMSE between a reference image R and the
fused image F is given by
There are different approaches to construct reference
image using input images. In our experiments, we
used the following procedure to compute RMSE.
First, RMSE value El is computed between source
image A and fused image F.
Similarly E2 is computed as RMSE between source
image B and fused image F.
Then the overall RMSE value is obtained by taking
the average of E1 and E2.
Smaller RMSE value indicates good fusion quality.
Peak Signal to Noise Ratio
PSNR can be calculated by using the formula
Where MSE is the mean square error and L is the
number of gray levels in the image.
Image Quality Index
IQI measures the similarity between two
images (I1 & I2) and its value ranges from -1 to 1.
IQI is equal to 1 if both images are identical. IQI
measure is given by
Where x and y denote the mean values of
images I1 and I2 and , , and denotes the
variance of I1 , I2 and covariance of I1 and I2.
Mutual Information
Mutual Information (MI) measures the
degree of dependence of two images. Its value is
zero when I1 and I2 are independent of each other.
MI between two source images I1 and I2 and fused
image F is given by
and PA(a) ,PB(b) and PF(f) are histograms of images
A, B and F,PFA(f,a) and PFB(f,b) are the joint
histograms of F and A, and F and B respectively.
Higher MI value indicates good fusion results.
RESULTS
We have taken a medical image to evaluate the
results by Averaging method and Satellite
images for evaluating the images by DWT
method.
Input image 1
Figure : CT image
Input Image 2
Figure 2 : MR image
Fused Image by Averaging method
VJyothi,B.Rajesh Kumar,P.Krishna Rao,D.V.Rama Koti Reddy, Int. J. Comp. Tech. Appl., Vol 2 (2), 322-326
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ISSN:2229-6093
Image Fused by GA Average Method
Input Image 1
Input Image 2
Fused Image by DWT Method
Fused Image by GA – DWT
VJyothi,B.Rajesh Kumar,P.Krishna Rao,D.V.Rama Koti Reddy, Int. J. Comp. Tech. Appl., Vol 2 (2), 322-326
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ISSN:2229-6093
Performance Comparison of Proposed
Schemes
METHOD IQI MI RMSE PSNR
GA_AVG 0.9851 1.1293 12.3288 26.3124
GA_DWT 0.9468 1.0042 20.6849 21.8177
CONCLUSION
They are many ways of fusing images. We have
compared the regular image fusion techniques
with the Genetic Algorithm based techniques. It
can be seen from the above table and the image
results that the GA based techniques are having
much better results when compared with the
conventional techniques.
Two Genetic Algorithm based image fusion
algorithms are introduced and their objective
and subjective comparison with other classical
techniques is carried out. It is concluded from
experimental results that GA based image
fusion schemes perform better than existing
schemes.
6. REFERENCES [1] Aqeel Mumtaz*, Abdul Majid, Adeel Mumtaz
“Genetic Algorithm and its applications to
Image Processing”. 2008 International
Conference on Emerging Technologies, IEEE-
ICET 2008
[2] A.Haq Nishat, "Multi-Sensor Image Fusion and
Image Colorization for Better Situation
Assessment", Master Thesis, GIKI Pakistan,
Dec 2005. (PI)
[3] A M Khan, A Khan,” Fusion of Visible and
Thermal Images using Support Vector
MachinesT. Scientist. Title of the paper.
Proceedings of the Workshop “Intelligent Data
Acquisition and Advanced Computing Systems:
Technology and Applications
(IDAACS’2001)”, Ternopil, Ukraine 1-4 July
2001, pp.123-127.
[4] G. Piella, “A general framework for multiresolution image fusion: from pixels to regions,” Information Fusion, vol. 4, pp.
VJyothi,B.Rajesh Kumar,P.Krishna Rao,D.V.Rama Koti Reddy, Int. J. Comp. Tech. Appl., Vol 2 (2), 322-326
326
ISSN:2229-6093