Optimization in image fusion using genetic algorithm

Day by day, the advancement in sensor technology is increasing which is used for image acquisition. Different sensors can acquire the information of different wavelength. These sensors are not able to capture the complete information from the scene. Thus it is necessary to combine the images from different sensors to produce more informative image. Image fusion is the process of combing the information from input images. According to the application or need, image fusion technique can be used. Number of techniques with varieties of solutions is available for image fusion process. And thus it becomes difficult task to find an optimal solution for image fusion. Genetic algorithm is an optimization technique used for searching solution for large number of complex problems [15]. This paper gives the quality index of image fusion obtained using the combinations of different selection methods and crossover techniques in genetic algorithm. These techniques have been compared using root mean square error to obtain information about relative performance. The experimental result on some standard test images shows that performance parameters i.e. root mean square error (RMSE) and peak signal to noise ratio (PSNR) are good for multifocus and multisensor image fusion.