Figures 2 and 3 show the HRM images synthesized by the proposed method and the existing algorithms, such as IHS,2 substitute wavelet intensity (SWI),8 and IAWP.5 The reference HRM and input LRM images are also included. In these figures, the input LRM images are upsampled by four and interpolated for the purpose of comparison. In Fig. 2, color distortion is noticeable for the IHS method, whereas it is not noticeable for the wavelet-based methods (SWI, IAWP), including the proposed method. It can also be seen that the details are well restored in the synthesized HRM image by the proposed algorithm when compared with the others (Fig. 3). For the objective evaluation, we compute various visual quality metrics,1 such as correlation coefficient (CC), root mean squared error (RMSE), mean structural similarity (MSSIM), universal image quality index (UIQI), quality nonrequiring reference (QNR), spectral angle mapper (SAM), ERGAS, and peak signal-to-noise ratio (PSNR). For the metrics CC, UIQI, MSSIM, QNR, and PSNR, a larger value means a better performance, whereas a smaller value implies better performance for RMSE, ERGAS, and SAM. The overall visual quality assessments are summarized in Tables 2 and 3. The CPU times for the MATLAB® implementation on a personal computer (Intel Core i5 CPU 750 @2.67 GHz) are also measured for the assessment of the computational complexity. Approximately 80% of the time for the proposed algorithm is occupied by the extraction of the wavelet planes for the HRP, LRM, and LRP images. IHS, Gram-Schmidt adaptive (GSA), GIHSA, AdapIHS, AdapCS, and MMSE are not based on the wavelet decomposition, whereas SWI, AWLP, IAWP, NAW, and the proposed algorithms are based on the wavelet decomposition. In Tables 2 and 3, the best two results for each assessment are highlighted in bold. The assessments show that the proposed algorithm is included in the best two for all the objective evaluation metrics except for the UIQI and QNR tests in the QuickBird images, where the proposed algorithm takes third place for both cases. The performance of the proposed method is comparable to the MMSE method9 for the IKONOS data and the proposed method is slightly better than the MMSE except for UIQI in the QuickBird test. The full-size synthesized images for all the algorithms used in the comparison and MATLAB p-codes for the proposed algorithm are available at http://ispl.snu.ac.kr/~idealgod/image_fusion.