Research On Image Fusion Technology Based On Multiscale Analysis & Intelligent Optimization Algorithm

Image fusion technology can effectively enhance the utilization ratio of image information,the accuracy of target recognition and the ability of image interpretation.Due to the rapidly development of image processing technology,the traditional fusion methods have not meet the processing requirements of complex images.Therefore,Grey Wolf Optimizer(GWO)and Whale Optimization Algorithm(WOA)are applied into image fusion based on the fusion techniques using multiscale geometric analysis tools.In this paper,we use the pyramid decomposition and wavelet transform for image preprocessing,and then two image fusion methods are proposed.The main research work and innovative points are as follows:1.First of all,the relevant theories of both pyramid decomposition and GWO are further studied.Second,according to the analysis of the data features and the requirements of image fusion,an approach of image fusion based on pyramid decomposition with grey wolf optimizer is proposed in this paper.In this algorithm,the fitness function is constructed by using information entropy.Furthermore,GWO is mainly used for optimizing the subband coefficients.Values of its parameters are given depending on experiences.2.Similarly,the basic theory of wavelet transform and WOA and their applications are researched profoundly.Secondly,on the basis of both the requirements of image fusion and the imaging characteristics,a new fusion method based on wavelet transform and whale optimization algorithm is proposed.The fitness function is constructed by using information entropy.In the fusion process,WOA is applied to search for the optimal weight coefficients through the procedure that current whales always follow the excellent whale.In conclusion,both of these two methods proposed in this paper have obtained good performance in image fusion.It can be seen that such methods have certain validity by comparing the evaluation index.