论文标题
使用对称的自动界面的图像融合，对抗常规仪
Image fusion using symmetric skip autoencodervia an Adversarial Regulariser
论文作者
论文摘要
通过结合可见图像的空间特征和红外图像的光谱含量来提取两全其美是一项艰巨的任务。在这项工作中，我们提出了一种空间约束的对抗自动编码器，该自动编码器从红外和可见图像中提取深度特征，以获得更详尽和更全局的表示。在本文中，我们提出了一个由残留对抗网络正规化的残留自动编码器体系结构，以生成更现实的融合图像。残差模块是编码器，解码器和对抗网络的主要建筑物，因为对称对称的Skip连接的添加执行了直接从编码器结构的初始层到网络解码器部分的空间特性的功能。红外图像中的光谱信息通过在融合结构的编码器部分中的几层中添加特征图来合并，从而分别推断了视觉和红外图像。为了有效地优化网络的参数，我们提出了一个对抗常规网络，该网络将对融合图像和原始视觉图像进行监督学习。
It is a challenging task to extract the best of both worlds by combining the spatial characteristics of a visible image and the spectral content of an infrared image. In this work, we propose a spatially constrained adversarial autoencoder that extracts deep features from the infrared and visible images to obtain a more exhaustive and global representation. In this paper, we propose a residual autoencoder architecture, regularised by a residual adversarial network, to generate a more realistic fused image. The residual module serves as primary building for the encoder, decoder and adversarial network, as an add on the symmetric skip connections perform the functionality of embedding the spatial characteristics directly from the initial layers of encoder structure to the decoder part of the network. The spectral information in the infrared image is incorporated by adding the feature maps over several layers in the encoder part of the fusion structure, which makes inference on both the visual and infrared images separately. In order to efficiently optimize the parameters of the network, we propose an adversarial regulariser network which would perform supervised learning on the fused image and the original visual image.
