Research On Image Fusion Algorithm Based On Multiple Features

lmage fusion technology can fuse multiple images,and a more comprehensive and accurate target image is obtained.Today,this technology has been widely used in remote sensing,medical image processing,military defense?robotics,security and surveillance.A variety of image features are analyzed,and a corresponding image fusion algorithm based on multiple features is proposed respectively for three types of images.The specific content is as follows:A multi-focus image fusion algorithm based on multiple features is proposed.The average gradient,edge intensity and standard variance are used as the basis for multi-focus image fusion.Two input images are decomposed by the Contourlet and wavelet transform.The high-frequency coefficients are fused based on weighted sum of the three features,and the low-frequency coefficients are fused based on the pixel values.The multi-ob.jective particle swarm optimization algorithm is used to optimize the weight of each feature of the image.Finally,a fused image is obtained through inverse wavelet transform and inverse Contourlet transform.8 groups of images are used in experiments,and compared with image fusion algorithm based on combined features and multi-feature image fusion algorithm using dbl wavelet basis.The experinental results show that the fusion image obtained by this algorithm is clearer,the information volume is more abundant,and the evaluation parameters are better.A medical image fusion algorithm based on multiple features is proposed.The information entropy,texture features,and average gradient are used as the basis for medical image fusion.Two input images are decomposed by the Contourlet and wavelet transfonm.The high-frequency coefficients are fused based on weighted sum of the three features,and the low-frequency coefficients are fused based on the pixel values.The multi-objective particle swarm optimization algorithm is used to optimize the weight of each feature of the image.Finally,a fused image is obtained through inverse wavelet transform and inverse Contourlet transform.8 groups of images are used in experiments,and compared with image fusion algorithm based on combined features and multi-feature image fusion algorithm using dbl wavelet basis.The experimental results show that the image texture of the fusion image obtained by this algorithm is clearer,the target information is richer,the fusion effect is better.and the evaluation parameters are better.A remote sensing image fusion algorithm based on multiple features is proposed.The average gradient,edge intensity,and fractal features are used as the basis for medical image fusion.Two input images are decomposed by the Contourlet and wavelet transform.The high-frequency coefficients are fused based on weighted sum of the three features,and the low-frequency coefficients are fused based on the pixel values.The multi-objective particle swarm optimization algorithm is used to optimize the weight of each feature of the image.Finally,a fused image is obtained through inverse wavelet transform and inverse Contourlet transform.8 groups of images are used in experiments,and compared with image fusion algorithm based on combined features and multi-feature image fusion algorithm using dbl wavelet basis.The experimental results show that the fusion image obtained by this algorithm has rich information of texture and color,the image fusion effect is better,and the evaluation parameters are better.An image fusion algorithm based on multiple features proposed in this paper greatly improves the quality of the fused images,and achieves a better fusion effect in multi-focus images,medical images and remote sensing image.It will have a wider application.