Of the many image fusion methods, the discrete wavelet transform (DWT) and various pyramids (e.g., the Laplacian pyramid) are among the most common and effective. For quantitative evaluation of the quality of fused imagery, the root mean square error (RMSE) is the most reasonable measure of quality if a "ground truth" image is available; otherwise, the entropy, spatial frequency, or image quality index (IQI) can be calculated and evaluated. Here, an advanced discrete wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is presented. Specifically, a principle vector is derived from two input images and then applied to two of the images' approximation coefficients at the highest DWT transform scale. For the detail coefficients at each transform scale, the larger absolute values are chosen and subjected to a neighborhood morphological processing procedure that serves to verify the selected pixels by using a "filling" and "cleaning" operation. Furthermore, the aDWT has two adjustable parameters—the number of DWT decomposition levels and the length of the selected wavelet that determinately affect the fusion result. An iterative fusion process that was optimized with the established metric—IQI—is then implemented. Experimental results tested on four types of inhomogeneous imagery show that the iterative aDWT achieves the best fusion compared to the pyramid or the DWT methods judged on both the IQI metric and visual inspection.