论文标题
引导深度解码器：无监督的图像对融合
Guided Deep Decoder: Unsupervised Image Pair Fusion
论文作者
论文摘要
输入和指导图像的融合在其信息方面具有权衡（例如，高光谱和RGB图像融合或Pansharpening）的融合可以解释为一个一般问题。但是，先前的研究应用了特定于任务的手工制作的先验，并且没有通过统一方法解决这些问题。为了解决这一限制，在这项研究中，我们提出了一个指导的深层解码器网络，作为一般的先验。所提出的网络由一个编码器码头网络组成，该网络可利用指导图像的多规模特征和生成输出图像的深度解码器网络。这两个网络通过特征改进单元连接，将指南图像的多尺度特征嵌入到深度解码器网络中。提出的网络允许无需培训数据以无监督的方式优化网络参数。我们的结果表明，拟议的网络可以在各种图像融合问题中实现最先进的性能。
The fusion of input and guidance images that have a tradeoff in their information (e.g., hyperspectral and RGB image fusion or pansharpening) can be interpreted as one general problem. However, previous studies applied a task-specific handcrafted prior and did not address the problems with a unified approach. To address this limitation, in this study, we propose a guided deep decoder network as a general prior. The proposed network is composed of an encoder-decoder network that exploits multi-scale features of a guidance image and a deep decoder network that generates an output image. The two networks are connected by feature refinement units to embed the multi-scale features of the guidance image into the deep decoder network. The proposed network allows the network parameters to be optimized in an unsupervised way without training data. Our results show that the proposed network can achieve state-of-the-art performance in various image fusion problems.
