In order to improve the identification ability of targets, the fusion of infrared and visible images is widely applied to the night vision system. With Compressive Sensing (CS), the complete information of signal can be kept by small amounts of linear projection, such that the contradiction between the cost of infrared detector and the image resolution can be resolved. The reconstruction error and calculated amount can be reduced, if the image fusion can be performed based on the measurements in CS directly. Thus, this paper proposes a novel fusion technology of infrared and visible images in CS. Firstly, our algorithm proposes a new cost function, which considers both conformity degree between the fused image and o-riginal images and the preservation degree of original image features. Secondly, the sparse coefficients of fused image can be acquired by L, -norm optimization. Finally, the fused image can be reconstructed with the sparse coefficients and the dictionary. In comparison with several fusion methods in CS, our algorithm has a significant advantage in terms of several metrics, as well as in the visual quality. This method provides a new useful tool for the image fusion in CS.%为了提高夜间对目标的识别能力,红外和可见光图像融合技术被广泛应用到夜视系统中.使用压缩传感技术可以通过获取信号的少量线性投影来保留信号的完整信息,解决红外成像中红外探测器件与图像分辨率之间的矛盾.以压缩传感测量值作为图像内容特征,直接进行图像融合,可以减少重构误差和计算量.因此本文提出一种压缩传感条件下的红外和可见光图像融合算法.首先,本文算法同时考虑融合图像和原始图像的相似度和对原始图像特征的保留程度,提出一个新颖的代价函数.然后,采用L1 范数优化求解该代价函数,得到融合图像对应的稀疏系数.最后,利用字典和该稀疏系数重构为融合图像.通过和几种压缩传感条件下的融合算法比较,可以看出本文算法在主观视觉效果和客观评价方面均具有显著优势.该算法为压缩传感条件下的图像融合提供一种新的有效手段.
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