Image Fusion Algorithm Based on Features Motivated Multi-channel Pulse Coupled Neural Networks
Pulse coupled neural networks (PCNN) is a mammal visual cortex-inspired artificial neural networks. Owing to the coupling links in neurons, PCNN is successful to utilize the local information, thus it has been successfully employed in image fusion. However, in traditional PCNN for image fusion, value of per pixel is used to motivate per neuron. In this paper, image feature of per pixel, e.g. gradient and local energy, is used to motivate per neuron and generate firing maps. Each firing map is corresponding to one type feature. Furthermore, a new multichannel PCNN is presented to combine these firing maps via a weighting function which measures the contribution of these features to the fused image quality. Finally, pixels with maximum firing times, when firing times of source images are compared, are selected as the pixels of the fused image. Experimental results demonstrate that the proposed algorithm outperforms Waveletbased and Wavelet-PCNN-based fusion algorithms.
Pulse Coupled Neural Networks image fusion wavelet transform image processing
Qu Xiaobo Yan Jingwen
Dept. of Communication Engineering, Xiamen University. Fujian, P.R.China, 361005 Key Lab of Digital Signal and Image Processing of Guangdong Province, Shantou University. Guangdong,
国际会议
上海
英文
2103-2106
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）