Image fusion based on wavelet transform with genetic algorithms and human visual system

A novel wavelet-based approach for multi-focus image fusion is presented, which is developed by taking into not only account the characteristics of human visual system (HVS) but also consider the optimization of image quality index to meet the human perception. After the multi-focus images to be fused are decomposed by the wavelet transform, different-fusion schemes for combining the coefficients are proposed: coefficients in low-frequency band are using the genetic algorithms to estimate the optimal weight according to the Edge-Association Index, and coefficients in high-frequency bands are weighted fusion by the texture masking of human visual system. To overcome the presence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performed by a window-based consistency verification process. The fused image is finally constructed by the inverse wavelet transform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing fusion methods are carried out in the paper. Experimental results on simulated and real images indicate that the proposed method is effective and can get satisfactory fusion results.