A new multiwavelet-based approach to image fusion

Image fusion refers to the techniques that integrate complementary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and the compute-processing tasks. In this paper, a new image fusion algorithm based on multiwavelet transform to fuse multisensor images is presented. The detailed discussions in the paper are focused on the two-wavelet and two-scaling function multiwavelets. Multiwavelets are extensions from scalar wavelet, and have several unique advantages in comparison with scalar wavelets, so that multiwavelet is employed to decompose and reconstruct images in this algorithm. In this paper, the image fusion is performed at the pixel level, other types of image fusion schemes, such as feature or decision fusion, are not considered. In this fusion algorithm, a feature-based fusion rule is used to combine original subimages and to form a pyramid for the fused image. When images are merged in multiwavelet 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 SPOT multispectral XS1\XS3 images, multifocus digital camera images, multisensor of VIS\IR images, and medical CT\MRI images are presented in this paper. In this paper, mutual information is employed as a means of objective assessing image fusion performance. The experiment results show that this fusion algorithm, based on multiwavelet transform, is an effective approach in image fusion area.