设计了一种压缩感知耦合梯度下降的IR-VI图像自适应融合方案。引入S-函数对IR图像进行预处理，增强其对比度。利用非下采样Contourlet变换对IR与VI图像分解，分别得到低频与高频系数。对低频系数，利用自适应区域平均能量准则对其进行融合，以减少边缘模糊。对于高频部分，引入压缩感知进行稀疏采样，再采用绝对最大值选择与自适应高斯区域标准差的融合规则，通过高斯模糊隶属度建立的自适应控制融合过程，并利用基于梯度下降迭代算法来求解稀疏信号，形成高频融合系数。通过逆NSCT生成最终融合图像。实验表明，与当前流行的红外-可见光融合算法比较，所提算法具有更高的融合质量，输出图像的信息更丰富，边缘与纹理更为清晰。所提算法具有较高的融合质量，在红外、安防以及模式识别等领域具有一定的应用价值。

An adaptive fusion scheme of infrared-visible light images with compressed sensing coupling gradient descent was proposed. The S- function was used to preprocess the IR image, and its contrast was enhanced. Use the Non-subsampled contourlet transform (NSCT) to decompose the IR and the visible images, and get the low frequency and high frequency coefficients respectively, respectively. Contourlet and NSCT can be used to get the low frequency and high frequency coefficients respectively. Then, for the low frequency part, the adaptive regional average energy criterion is used to fuse the low frequency coefficients to reduce the edge blur. For the high-frequency part, the fusion rule of absolute maximum standard selection and adaptive Gauss regional difference, adaptive fuzzy control membership can be used to establish the fusion process by Gauss, the introduction of compressed sensing sparse sampling, and is solved by sparse signal gradient descent based on iterative algorithm. The final fusion image is generated by the inverse NSCT reconstruction. Experiments show that compared with the current popular infrared visible light fusion algorithm, the image information obtained in this scheme is richer, the edges and texture are clearer, the contrast and spatial resolution are improved, and the system is more consistent with the human vision system, with high efficiency and strong robustness. This algorithm has high fusion quality which has certain application value in IR, security and pattern recognition.