论文标题
红外线和可见图像融合的交互式特征嵌入
Interactive Feature Embedding for Infrared and Visible Image Fusion
论文作者
论文摘要
用于红外和可见图像融合的一般深度学习方法依赖于无监督的机制，通过利用精心设计的损失函数来保留重要信息。但是，无监督的机制取决于设计良好的损失函数，该功能无法保证源图像的所有重要信息都充分提取。在这项工作中，我们提出了一种新颖的互动特征，将红外和可见图像融合的自我监管的学习框架嵌入，试图克服重要信息降级问题。借助自我监督的学习框架，可以有效提取源图像的层次结构表示。特别是，智能设计的交互式功能嵌入模型是在自我监督的学习与红外和可见图像融合学习之间建立桥梁的，从而实现了重要的信息。定性和定量评估表明，提出的方法对最新方法有利。
General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between the self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods.
