Redundant versus orthogonal wavelet decomposition for multisensor image fusion
The wavelet decomposition has become an attractive tool for fusing multisensor images. Usually, the input images are decomposed with an orthogonal wavelet in order to extract features, which are combined through an appropriate fusion rule. The fused image is then reconstructed by applying the inverse wavelet transform. In this paper, we investigate the use of the nonorthogonal (or redundant) wavelet decomposition as an alternative approach for feature extraction. By using test and remote sensing images, various fusion rules are considered and the detailed comparison indicates the superiority of this approach compared to the standard orthogonal wavelet decomposition for image fusion.
orthogonal wavelet decomposition image fusion fusion rule redundant wavelet decomposition  trous algorithm