A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Currently available image fusion techniques applied to the merging of fine resolution panchromatic and multispectral images are still not able to minimize colour distortion and maximize spatial detail. In this study, a new fusion method, based on Bidimensional Empirical Mode Decomposition (BEMD), is proposed. Unlike other multiresolution analysis tools, such as the discrete wavelet transform (DWT), which normally examines only horizontal, vertical and diagonal orthonormal details at each decomposed scale, the BEMD produces a fully two-dimensional decomposition of the panchromatic and multispectral images, based purely on spatial relationships between the extrema of the image. These are decomposed into a certain level of Intrinsic Mode Functions (IMFs) and residual images with the same number of columns and rows as the original image. In consequence, by injecting all the IMF images from the panchromatic image into the residue of the corresponding multispectral image, the fusion image may be reconstructed. The fusion results are evaluated and compared with other popular methods in terms of both the visual examination and the quantitative assessment of the merged images. Preliminary results show that BEMD is optimal and provides a delicate balance between spectral information preservation and enhancement of spatial detail.