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 arc 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. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.