A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
| Hi-index | 0.00 |
The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non-orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image-fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet-based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the 'à trous', wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet-integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non-orthogonal), decimation or undecimation, and wavelet-decomposition levels.