FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion

Fabian Duffhauss, Ngo Anh Vien, Hanna Ziesche, G. Neumann
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Abstract

. Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior knowledge nor find regu-larities in a given dataset or they are restricted to a single application. We overcome this shortcoming by presenting a novel deep hierarchical variational autoencoder called FusionVAE that can serve as a basis for many fusion tasks. Our approach is able to generate diverse image samples that are conditioned on multiple noisy, occluded, or only partially visible input images. We derive and optimize a variational lower bound for the conditional log-likelihood of FusionVAE. In order to assess the fusion capabilities of our model thoroughly, we created three novel datasets for image fusion based on popular computer vision datasets. In our experiments, we show that FusionVAE learns a representation of aggregated information that is relevant to fusion tasks. The results demonstrate that our approach outperforms traditional methods significantly. Furthermore, we present the advantages and disadvantages of different design choices.
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用于RGB图像融合的深度分层变分自编码器
. 传感器融合可以显著提高许多计算机视觉任务的性能。然而,传统的融合方法不是数据驱动的,不能利用先验知识,也不能在给定的数据集中发现规律,或者它们仅限于单个应用。我们克服了这一缺点,提出了一种新的深层分层变分自编码器,称为FusionVAE,它可以作为许多融合任务的基础。我们的方法能够生成不同的图像样本,这些样本以多个噪声、遮挡或仅部分可见的输入图像为条件。我们推导并优化了FusionVAE条件对数似然的变分下界。为了全面评估模型的融合能力,我们基于流行的计算机视觉数据集创建了三个新的图像融合数据集。在我们的实验中,我们证明了FusionVAE学习了与融合任务相关的聚合信息的表示。结果表明,我们的方法明显优于传统方法。此外,我们还介绍了不同设计选择的优点和缺点。
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