针对红外与可见光图像融合时细节信息提取不充分、算法复杂度高等缺点，本文提出一种降低算法复杂度、丰富细节信息的基于非降采样剪切波变换 (NSST)和非负矩阵分解 (NMF)的红外与可见光图像融合算法。该方法根据 NSST算法对源图像分别进行多尺度、多方向稀疏分解，分别得到低频部分和高频部分。对低频部分采用基于改进的 NMF融合规则；对高频部分采用拉普拉斯能量和视觉敏感度系数相结合的融合规则。最后，对低频融合部分和高频融合部分执行 NSST逆变换得到最终的融合图像。实验结果表明，该融合方法不仅可以保证融合图像的清晰度，同时还可以缩短算法的运行时间。

Aiming at insufficient details information extraction and higher complexity algorithm when the infrared and visible light image fusion is processed, an infrared and visible image fusion algorithm based on the Non-subsampled Shearlet Transform (NSST) and the improved Non-negative Matrix Factorization (NMF) is proposed. Making use of NSST to decompose source images on multi-direction and multi-scale sparse, low-frequency components and high frequency components are obtained. The fusion method of the improved NMF is adopted in the low frequency subband. The fusion rule for the combination of the Laplace energy and visual sensitivity coefficient is used to the high frequency components. Finally, the fusion image is obtained after executing the NSST inverse transformation. Experimental results show that the fusion method can not only guarantee the definition of the fused image, but also shorten the running time of the algorithm.