Review on Different Methods of Multisource Image Fusion Techniques

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. The double-density dual-tree complex wavelet transform (CWT), which is a DWT that combines the double-density DWT and the double density dual-tree wavelet transform (DDDTWT), each of which has its own characteristics and advantages. Double-density Dual-tree Complex Wavelet transform is introduced to image fusion based on multiresolution, images are decomposed by double-density dual tree complex wavelet transform with multi-level, multidirection and shift-invariance and according to characters of low and high frequency coefficients respectively, different combination rules are adopted to fuse images and composite coefficients are reconstructed by double-density dual-tree complex wavelet inverse transform. Some fusion experiments are done by several sets of images with different modalities and objective performance assessments are fulfilled to evaluate fusion results. The experimental results indicate that the proposed approach can significantly outperform the traditional image fusion method based on Laplacian pyramid and discrete wavelet transform.