Wavelet transform applications

A image fusion algorithm based on wavelet transform to fuse multisensor images is presented. When images are merged in wavelet space, different frequency ranges are processed differently. It can merge information from original images adequately and improve abilities of information analysis and feature extraction. Extensive experiments including the fusion of registered multiband images, multispectral images, multifocus digital camera images, multisensor of VISMR images and medical CTMRI images are presented. The evolution of image fusion research begins from Simple image fusion attempts, Pyramid-decomposition-based image fusion and Wavelet-transform-based image fusion. Several types of pyramid decomposition are used or developed for image fusion, such as Laplacian Pyramid, Ratio-of-low-pass Pyramid and Gradient Pyramid. Since then, image fusion began to receive increasing attention. More recently, with the development of wavelet theory, people began to apply wavelet multiscale decomposition to take the place of pyramid decomposition for image fusion. Actually, wavelet transform can be taken as one special type of pyramid decompositions. This paper presents comparison between different methods of image fusion such as Brovey method, proposed RGB, Haar wavelet, Daubechies wavelet methods. The final result shows that which methods is suitable for required application.