关键词: 图像融合, 非下采样Contourlet变换, 脉冲耦合神经网络, Xydeas-Petrovic指标

Abstract: A novel method based on Non-Sampled Contourlet Transform（NSCT） is presented for the fusion of infrared and visual images to retain their thermal target information and spatial information and to improve their observability and visual effect. Firstly, infrared and visual images are decomposed by the NSCT to get lowpass subband coefficients and bandpass directional subband coefficients. Lowpass subband coefficients are fused by the adaptive Pulse Coupled Neural Network（PCNN） to extract target and the bandpass directional subband coefficients are fused based on the region variance matching. And the first fusion result is obtained through the inverse NSCT. Then the Xydeas-Petrovic index and entropy between original images and intermediate fused image are computed. Finally, in accordance with the Xydeas-Petrovic index and entropy, the original images are fused for the second time and the final fused decomposed are obtained. The experimental results show that the method is better in fusing infrared and visual images than some current multi-resolution transform based methods. Compared with the NSCT method in two group images, their quality indexes have been increased by 261.06%, 48.31%, 5.15%, 142.95%, 21.62% and 372.85%, 54.62%, 4.73%, 163.07%, 25.40% respectively. The algorithm can get a good fusion image with more clearly details such as edges. Besides, the fusion image is more conform to the requirements of human vision.

Key words: image fusion, Non-Sampled Contourlet Transform（NSCT）, Pulse Coupled Neural Network（PCNN）, Xydeas-Petrovic index