We address an approach called “DepthFuseNet” for the fusion of thermal and visible images using convolutional neural networks (CNN). The thermal image acquires radiating energy of the sensed objects and hence it can easily distinguish the objects from its background. However, the visible image (i.e., the image acquired within the range of visible wavelength of electromagnetic spectrum) provides a more visual context of the objects with high spatial resolution. Due to this complement nature of thermal and visible images, it is always an interest of the community to combine those two images to obtain more meaningful information from the individual source images. In DepthFuseNet method, features are extracted from given source images using CNN architecture, and they are integrated using the different fusion strategies. The auto-weighted sum fusion strategy performs better than that obtained using the other existing methods. To reduce the complexity of the architecture, we use depthwise convolution in the network. The experimental evaluation demonstrates that the proposed method exhibits salient features from the source images, and hence it performs better than the other state-of-the-art fusion methods in terms of qualitative and quantitative assessments.

中文翻译：

---

DepthFuseNet：一种使用卷积神经网络融合热图像和可见图像的方法

我们使用卷积神经网络（CNN）解决了一种称为“ DepthFuseNet”的方法，用于热图像和可见图像的融合。热图像获取感测到的物体的辐射能，因此它可以轻松地将物体与其背景区分开。然而，可见图像（即，在电磁光谱的可见波长范围内采集的图像）以较高的空间分辨率为对象提供了更可视的上下文。由于热图像和可见图像的这种互补性，将这两个图像结合起来以从单个源图像中获取更多有意义的信息一直是社区的利益。在DepthFuseNet方法中，使用CNN架构从给定的源图像中提取特征，并使用不同的融合策略对其进行集成。自动加权和融合策略的性能优于使用其他现有方法获得的融合策略。为了降低体系结构的复杂性，我们在网络中使用深度卷积。实验评估表明，所提出的方法从源图像中展现出显着特征，因此，在定性和定量评估方面，其性能优于其他最新融合方法。