关键词: 脉冲耦合神经网络（PCNN）, 区域特征, 图像融合, 非下采样Contourlet变换（NSCT）, 红外与可见光

Abstract: In allusion to the low contrast and the unconspicuous infrared target can easily arise in infrared and visible image fusion, a new algorithm is proposed. The algorithm innovatively applies improved PCNN and region characters to the selection of low frequency subband coefficients and bandpass directional subband coefficients in NSCT domain. The subband coefficients of images to be fused are obtained by using NSCT. The decomposed subband coefficients are processed by using PCNN and the subband coefficients’ fire mapping images are obtained. Low frequency subband coefficients’ fire mapping image uses the method based on region standard deviation to choose the fusion coefficients. Bandpass directional subband coefficients’ fire mapping image uses the method based on region energy to choose the fusion coefficients. The fusion image can be obtained by inverse transform of NSCT. Compared with other algorithms, simulation experiment shows that the proposed algorithm can get a prominent infrared target and better quality fusion image and the objective evaluation indexes of fusion image improve obviously.

Key words: Pulse Coupled Neural Network（PCNN）, region characters, image fusion, Nonsubsampled Contourlet Transform（NSCT）, infrared and visible