Multifocus Image Fusion Based on Multiwavelet and Immune Clonal Selection

Based on multiwavelet transform and the clonal selection theory in the natural immune system, a novel pixel-level multifocus image fusion optimization algorithm is presented in this paper. Source images are first decomposed into low-frequency coarse information and high-frequency detail information via discrete multiwavelet transform. The high-frequency detail information adopts the absolute-values maximum selection. And then the immune clonal selection is introduced to optimize the weights of fusing the low-frequency four coarse subbands adaptively and separately. Image fusion performances of Daubechies-4 (Db4) scalar wavelet, Geronimo, Hardin and Massopust (GHM) multiwavelets and Chui and Lian (CL) multiwavelets are compared quantitatively, which have the same approximation order. Experimental results show that the proposed image fusion algorithm have clear edges, abundance details and few artificial artifacts.