8 August 2007 An EMD-IHS model for high resolution image fusion
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High resolution image fusion is a significant focus in the field of the image processing. A new image fusion model is presented based on the characteristic level of Empirical Mode Decomposition (EMD). The IHS transform of the multi-spectral image firstly gives the intensity image. Thereafter, the 2D EMD in terms of row-column extension of the 1D EMD model was used to decompose the detail scale image and coarse scale image from the high resolution band image and the intensity image. At last, fused intensity image is obtained by reconstruction with high frequency of high-resolution image and low frequency of intensity image and IHS inverse transform result in fused image. After presenting EMD principle, multi-scale decomposition and reconstruction algorithm of 2D EMD is defined and fusion technique scheme is advanced based on EMD. Panchromatic band and multi-spectral band3,2,1 of QUICKBIRD are used to assess the quality of the fusion algorithm. After selecting appropriate Intrinsic Mode Function(IMF) for the merger on the basis of EMD analysis on specific row (colum) pixel gray value series, the fusion scheme gives fused image, which is compared with generally used fusion algorithms (Wavelet, IHS,Brovey). The objectives of image fusion include enhancing the visibility of the image and improving the spatial resolution and the spectral information of the original images. For assessing quality of an image after fusion, information entropy and standard deviation are applied to assess spatial details of the fused images and correlation coefficient, bias index and warping degree for measuring distortion between the original image and fused image in terms of spectral information. For all proposed fusion algorithms, better results are obtained when EMD algorithm is used to perform the fusion experience.
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Jian Wang, Jian Wang, Changhui Xu, Changhui Xu, Jixian Zhang, Jixian Zhang, Zhengjun Liu, Zhengjun Liu, } "An EMD-IHS model for high resolution image fusion", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675209 (8 August 2007); doi: 10.1117/12.760475; https://doi.org/10.1117/12.760475


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