针对小波变换容易造成细节信息丢失、非下采样轮廓波变换（NSCT）分解的低频子带系数不稀疏以及红外与可见光图像融合结果综合性能不佳的问题，提出了一种基于稀疏表示和NSCT-PCNN的红外与可见光图像融合算法。首先将源图像进行NSCT分解，获得低、高频子带；其次，利用K奇异值分解（K-SVD）算法对低频子带进行字典训练，实现低频子带的稀疏表示和低频稀疏系数的融合；然后，利用高频子带的空间频率激励脉冲耦合神经网络（PCNN），选择较大点火次数的系数作为高频子带的融合系数；最后对低、高频子带融合系数进行NSCT逆变换，得到融合的图像。实验结果表明,该算法在视觉效果和客观指标方面均具有较大优势，且融合结果综合性能优于现有算法。

In view of the problems of the loss of detailed information caused by wavelet transform, the non-sparsity of low-frequency subband coefficient decomposed by Non-Subsampled Contourlet Transform (NSCT), and the poor comprehensive performance of infrared and visible image fusion, an algorithm for fusion of infrared and visible images is proposed based on sparse representation, NSCT, and Pulse Coupled Neural Network (PCNN). Firstly, the original image is decomposed by NSCT to obtain the low-frequency and high-frequency subbands.Secondly, the K-SVD (Singular Value Decomposition) algorithm is used to carry out dictionary training on the low-frequency subband to realize the sparse representation of low-frequency subband and the fusion of low-frequency sparse coefficients. Then, the spatial frequency of the high-frequency subband is utilized to stimulate PCNN, and the coefficient with more ignition times is selected as the fusion coefficient of the high-frequency subband. Finally, the NSCT inverse transform is applied to the low and high frequency subband fusion coefficients to obtain the fused image.The experimental results show that the proposed algorithm has a great advantage in subjective visual effect and objective index evaluation, and its comprehensive performance is superior to that of the existing algorithm.