Abstract: Simple average processing of low-pass subbands was usually adopted in multi-scale transform based image fusion, which could not protect the saliency information in source images very well. To solve this problem, an image fusion method using the saliency computation to drive adaptive pulse-coupled neural network (PCNN) was proposed to optimize the low-pass subbands fusion. First, source images were decomposed by morphological un-decimated wavelet, and low-frequency coefficients and high-frequency coefficients were obtained. Second, low-frequency coefficients were fused by rule based on saliency computation and PCNN, and high-frequency coefficients were selected by strong absolute value. Finally, the fusion image was got by inverse transform. Experimental results indicated that saliency information of source images was obtained to some extent and the fusion indicators, such as  mutual information, entropy, average gradient and edge preservation degree were improved.

Key words: image fusion, adaptive, multi-scale, morphological un-decimated wavelet, saliency computation, PCNN