基于仿生视觉机制的红外与可见光图像融合

Infrared and Visible Image Fusion Based on Bionic Vision Imaging Mechanism

为实现红外图像与可见光图像的融合,设计了以响尾蛇的视觉成像机制为基础的红外图像与可见光图像融合神经网络结构。首先根据双模式细胞的6种响应模式,得到红外和可见光图像的6种响应结果,然后以视觉感受野数学模型为基础,将6种双模式细胞响应输入到由ON对抗系统和OFF对抗系统组成的双层网络结构中,最后输出R、G和B 3个通道的映射值及伪彩色图像增强结果。分别对4组经过配准的红外和可见光图像进行融合,将该方法融合结果与经典的Waxman方法融合结果进行了对比,实验结果表明,所设计的网络结构得到的融合图像效果较好,信息熵和平均梯度均优于经典的Waxman方法融合结果。

In order to realize the fusion of infrared image and visible image,a neural network structure of infrared image and visible image fusion based on the visual imaging mechanism of rattlesnake is designed.Firstly,according to the six response modes of dual-mode cells,six response results of infrared and visible image are obtained. Then,based on the mathematical model of visual receptive field,the neural network structure of infrared image and visible image fusion is designed,and six kinds of dual-mode cell responses are input into a two-layer network structure composed of on countermeasure system and off countermeasure system. Finally,the mapping values of R,G and B channels and the pseudo color image enhancement results are output. Four groups of registered infrared and visible images are fused respectively. And the fusion results are compared with the classical Waxman method. The experimental results show that the fusion image effect of the designed network structure is better,and the information entropy and average gradient are better than the classical Waxman method.