Multi-resolution Image Fusion Algorithm Based on Improved Regional Cross Entropy and Morphology

Based on image contents, the better to simulate the process pattern of human eyes vision, an image fusion method that emphasizing edge preserving is proposed. Through wavelet transform, an improved regional cross entropy fusion rule is used for the low-frequency component which reflects approximate contents, the fusion method for incorporation of the maximum morphology edge measuring and weighted variance analysis is used for the high-frequency component which reflects detail features of image. Finally, the fusion image is reconstructed through an inverse transform of wavelet. Experimental results show that by using this algorithm, the mutual information between the images can be fused organically, the image clarity is raised, the details of fusion image are enhanced, and the edge information are reappeared better. Strong support for the follow-up information analysis and extractive ability of the images are provided. Introduction Image fusion is the process to comprehend organically the complementary information and redundant data from several sensors images to obtain the image with richer details, more reliable and easier-to-understand and read for the same scene. There are many image fusion methods, among others, the wavelet transform is fully used for image fusion[1-6] because of its good time frequency localization property and multi-scale analytical ability. The multi-resolution wavelet transform is adopted. The wavelet coefficient and scale coefficient on pixel level are analyzed, treated and fused. The fusion rule based on improved region cross entropy is proposed for the low-frequency component, this method is by means of judging the similarity of being fused images local region, and adopts different fusion strategy to fuse. It can reflect the approximate contents of the image better. The fusion rule based on maximum morphology edge detection and weighted variance is proposed for the high-frequency component. It obtains the fusion effect with better visual property and richer and obvious details, especially for the edge information. Wavelet Decomposition of Image Because the wavelet transform is possessed of the good localization property both in frequency domain and time domain simultaneously, it is used widely in image treatment and analyses. Mallat proposed the algorithm of wavelet transform rapid-decomposition and reconstruction, using two one-dimension filters realized the wavelet rapid-decomposition for the two-dimension image, and then using two one-dimension reconstruction filters realized the image reconstruction. Mallat algorithm decomposition equation under the scale j is [7]: Advanced Engineering Forum Online: 2012-09-26 ISSN: 2234-991X, Vols. 6-7, pp 589-594 doi:10.4028/www.scientific.net/AEF.6-7.589 © 2012 Trans Tech Publications, Switzerland This is an open access article under the CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0/)