Image Fusion Algorithm based on Adaptive Pulse Coupled Neural Networks in Curvelet Domain

摘要：

Using the fast discrete curvelet transform, an image fusion algorithm based on adaptive pulse coupled neural networks (PCNNs) is proposed. PCNN is built in each highfrequency subband to simulate the biological activity of human visual system. Support vector machine is employed to achieve support values which represent subband features and then will be imported to motivate the neurons. The first firing time of each neuron is presented as the salience measure. Compared with traditional algorithms where the linking strength of each neuron is set as constant or always changed according to features of each pixel, in our algorithm, the linking strength as well as the linking range is determined by the prominence of corresponding lowfrequency coefficients, which not only reduces the calculation of parameters but also flexibly makes good use of global features of images. Experimental results indicate superiority of the proposed algorithm in terms of visual effect and objective evaluations.

关键词： image fusion fast discrete curvelet transform pulse coupled neural networks support value

作者: Cai Xi Zhao Wei Gao Fei

作者单位: School of Electronics and Information Engineering, Beihang University, Beijing, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 845-848

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）