基于SqueezeNet的轻量级图像融合方法

Light-weight image fusion method based on SqueezeNet

现有深度红外和可见光图像融合模型网络参数多,计算过程需要耗费大量计算资源和内存,难以满足移动和嵌入式设备上的部署要求。针对上述问题,提出了一种基于SqueezeNet的轻量级图像融合方法,该方法利用轻量级网络SqueezeNet提取红外和可见光图像特征,并通过该网络提取的特征获得权重图并进行加权融合,进而获得最后的融合图像。通过与ResNet50方法进行比较发现,该方法在保持融合图像质量相近的情况下,模型大小和网络参数量分别被压缩为ResNet50方法的1/21和1/204,运行速度加快了4倍。实验结果表明,该方法不仅降低了融合模型的大小,加快了图像融合速度,同时得到了比其他传统融合方法更好的融合效果。

The existing deep learning based infrared and visible image fusion methods have too many parameters and require large amounts of computing resources and memory.These methods cannot meet the deployment demand of resource constrained edge devices such as cell phones and embedded devices.In order to address these problems,a light-weight image fusion method based on SqueezeNet was proposed.SqueezeNet was used to extract image features,then the weight map was obtained by these features,and the weighted fusion was performed,finally the fused image was generated.By comparing with the ResNet50 method,it is found that the proposed method compresses the model size and network parameter amount to 1/21 and 1/204 respectively,and improves the running speed to 5 times while maintaining the quality of fused images.The experimental results demonstrate that the proposed method has better fusion effect compared to existing traditional methods as well as reduces the size of fusion model and accelerates the fusion speed.