The Effects of Different Types of Wavelets on Image Fusion

Image fusion is a tool for integrating a high-resolution panchromatic image with a multispectral image, in which the resulting fused image contains both the high-resolution spatial information of the panchromatic image and the color information of the multispectral image. Wavelet transformation, originally a mathematical tool for signal processing, is now popular in the field of image fusion. Recently, many image fusion methods based on wavelet transformation have been published. The wavelets used in image fusion can be categorized into three general classes: Orthogonal, Biorthogonal and Nonorthogonal. Although these wavelets share some common properties, each wavelet leads to unique image decomposition and a reconstruction method which leads to differences among wavelet fusion methods. This paper focuses on the comparison of the image fusion methods which utilize the wavelets of the above three general classes. The typical wavelets from the above three general classes – Daubechies (Orthogonal), spline biorthogonal (Biorthogonal), and À trous (Nonorthogonal) – are selected as the mathematical models to implement image fusion algorithms. When wavelet transformation alone is used for image fusion, the fusion result is often not good. However, if wavelet transform and IHS transform are integrated, better fusion results may be achieved. Because the substitution in IHS transform is limited to only the intensity component, integrating of the wavelet transform to improve or modify the intensity and the IHS transform to fuse the image can make the fusion process simpler and faster. This integration can also better preserve color information. The fusion method based on the above IHS and wavelet integration concept is employed in this paper. IKONOS image data are used to evaluate the three different kinds of wavelet fusion methods mentioned above. The fusion results are compared graphically, visually, and statistically.