image fusion using evolutionary algorithm (ga) - Semantic Scholar

ISSN:2229-6093 VJyothi,B.Rajesh Kumar,P.Krishna Rao,D.V.Rama Koti Reddy, Int. J. Comp. Tech. Appl., Vol 2 (2), 322-326

IMAGE FUSION USING EVOLUTIONARY ALGORITHM (GA) V Jyothi 1), B Rajesh Kumar 1), P Krishna Rao 2), D V Rama Koti Reddy 2) 1) 2)

GITAM University, Visakhapatnam, AP, India, [email protected] Andhra University, Visakhapatnam, AP, India, [email protected]

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.

1. INTRODUCTION

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

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

Keywords: Genetic Algorithm, Image quality Index, Mutual Information.

A variety of algorithms have been evolved from nature. Genetic algorithm is one of the simplest and most popular evolutionary

322


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’s1. 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 2. • 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. 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. 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. 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:

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. 323


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.

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

Similarly E2 is computed as RMSE between source image B and fused image F.

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

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

Figure : CT image Input Image 2

Where x and y denote the mean values of images I1 and I2 and denotes the , , and 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

Figure 2 : MR image Fused Image by Averaging method

324


Image Fused by GA Average Method

Fused Image by DWT Method

Fused Image by GA – DWT Input Image 1

Input Image 2

325


Performance Schemes

Comparison

of

Proposed

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

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.

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, IEEEICET 2008 [2] A.Haq Nishat, "Multi-Sensor Image Fusion and Image Colorization for Better Situation

326