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