提出了一种基于变分法和梯度增强的红外与可见光图像融合算法。首先对红外与可见光的梯度特征进行自适应加权融合，得到初始融合梯度场；其次构建梯度特征增强模型，获得融合图像增强的梯度场；最后通过变分法将融合问题转换为最优化问题，得到最接近增强后梯度场的融合图像。实验结果表明，相比基于多分辨率框架下进行融合的拉普拉斯分解、小波变换及常见的基于变分的融合算法，所提出的算法得到的融合图像梯度特征最大，视觉效果最好，证明了算法的有效性。

We propose an algorithm to fuse infrared and visible images based on variational method and gradient enhancement. Firstly, the gradient features of infrared and visibe images are fused in an adaptirely weighted way, and the original fused gradient field is obtained. Then the enhancement model for gradient features is constructed to get the enhanced gradient field of fusion image. Finally, the fusion problem is transformed to an optimization problem by using variational method. Thus a fusion image with the gradient field approaching the preriously obtained enhanced gradient field can be achieved. Experimental results show that, in comparison with existing fusion algoritnms based on multi-resolution concept such as Laplace decomposition, wavelet transform and variation-based fusioin, our algorithm results in the maximal gradient feature and the best visual effects. The effectiveness of the proposed algorithm is proved.