Evaluation of wavelet transform algorithms for multi-resolution image fusion

Wavelet transforms can be used for multi-resolution image fusion at the pixel level, as they work both in spatial and spectral domains and result in the preservation of spatial of spectral details of input images. Different wavelet transform algorithms have been developed and applied to a variety of applications such as noise suppression, filtering, image restoration, image compression, and astronomical applications. This paper explores the use of current developed wavelet transform algorithms for multi-resolution fusion of satellite images. The aim is to investigate how appropriate these wavelet transform algorithms are for this multi-resolution image fusion. Five different types of wavelet transform algorithms are selected and the results are evaluated by comparing their spatial and spectral quality with the spatial and spectral qualities of their source satellite images (i.e. Ikonos Panchromatic and Multispectral, and Landsat TM). The findings show that different wavelet transform algorithms have a "preservation tradeoff" between the spatial quality and spectral quality. Due to the frequency shift limitation of the wavelet transform, it can preserve the spatial and/or spectral details of the input images for a certain number of levels.