关键词: 图像融合, 曲波变换, 脉冲耦合神经网络, 最优化评价准则

Abstract: Curvelet has the ability of acquiring high dimension singularity of image. Utilizing this characteristic, an algorithm of image fusion based on Pulse Coupled Neural Networks（PCNN） and optimization of evaluated rule in curvelet domain is proposed. By using curvelet transform, the input images are decomposed into sub-images with various scales. Then, based on the global coupled character of PCNN, a fusion rule is given and the high frequency fused coefficients can be generated by the rule. After that, the low frequency fused coefficients can be generated by optimization of some evaluated rules and the fused image is obtained by inverse curvelet transform. Its validity and advantage are shown by experimental results and the analysis of comparing with other algorithms.

Key words: image fusion, curvelet transform, pulse coupled neural networks, optimization of evaluated rule