Multifocus Image Fusion Using Biogeography-Based Optimization

Abstract

For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations.

Multifocus Color Image Fusion Based on NSST and PCNN

“…Each component can be regarded as a grayscale image, and the quality of the fused color image strongly depends on the grayscale image quality. In this paper, we take the average of the three component's evaluation indexes as the final color image evaluation index, which is the basic indicators of image 1 and 2 show the evaluation of the fused image quality with space frequency (SF), average gradient (AG), entropy (EN), mean value (MV), standard deviation (SD), and mutual information (MI), and indicates how much edge information is reserved in the fused image [19][20][21][22][23].…”