| Recently, with the development of new imaging sensors, computer technology and information processing technology, image fusion is utilized successfully in a broad variety of fields including military, remote sensing, automatic target recognition, machine vision, medical image processing, and etc. A general definition of image fusion is combination of two or more different images to form a new image by using a certain algorithm. The fused image contains more accurate descriptions of the scene than any of the individual source images. Because the wavelet transform has good local property in time and frequency fields, it is widely used in image fusion. In this paper, wavelet analysis is reviewed and multi-source remote-sensing image fusion is realized using wavelet transform.First, wavelet analysis theories are systematically reviewed including multi-resolution analysis, Mallat algorithm, lifting scheme and multiwavelet. Secondly, according to typical existent fusion algorithms based on classical wavelet transform, a new pixel-level fusion algorithm based on local directional contrast of source images is proposed. Standard deviation, entropy, average grads and root mean square error are advised to be the objective evaluation parameters. Experiment results show that this newly proposed fusion algorithm is superior to the same kind of algorithms. Thirdly, because the image fusion method based on classical wavelet transform has higher algorithmic complexity and more auxiliary memory consuming, a novel fast image fusion algorithm based on directional contrast and weighted activity is proposed. Lifting scheme is used and the computation of some parameters is simplified, so the algorithmic complexity is effectively reduced with good fusion performance. An application of this algorithm is also given. Finally, the principle of multi-spectral image fusion method based on multiwavelet transform is introduced, and experimental results of some classical algorithm are given. |
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