基于双分支卷积神经网络的红外与可见光图像局部融合算法

红外图像和可见光图像均存在一定的局限性，依靠单个种类图像无法满足工程实际需求，可通过引入图像融合技术，获取高质量的融合图像。为更好保障输出信息特征的多样性，本文引入一种双分支卷积神经网络实现红外与可见光图像局部融合；在双分支卷积神经网络基础上，同时从红外图像、可见光图像得到跨渠道信息、渠道内信息种特征，增加了融合图像的信息量。采用整数小波变换方法进行图像压缩。建立颜色空间模型时，合理调节t因子的数值，获得理想的融合图像。实验结果表明，与现有方法相比，本方法融合后图像边缘信息得到充分保留，图像细节得到增强，红外与可见光图像融合效果更好。

Local Fusion Algorithm of Infrared and Visible Light Images Based on Double-Branch Convolutional Neural Network

Both infrared and visible images have certain limitations, and relying on individual types of images cannot meet the practical needs of engineering. Instead, high-quality fused images can be obtained by introducing image fusion techniques. To better guarantee the diversity of the output information features, this study introduces a dual-branch convolutional neural network to achieve local fusion of infrared and visible images. Based on the dual-branch convolutional neural network, red and blue features are obtained from infrared images and visible light images simultaneously, thereby increasing the amount of information in the fusion image. The integer wavelet transform method is used for image compression. When the color-space model is built, the value of the t-factor is adjusted to obtain an ideal fusion image. The experimental results show that the edge information of the image after the fusion of this method is fully preserved, image detail information is enhanced, and fusion effect of infrared and visible images is improved, compared with the existing methods.