Recent learning-based intrinsic image decomposition methods have achieved remarkable progress. However, they usually require massive ground truth intrinsic images for supervised learning, which limits their applicability on real-world images since obtaining ground truth intrinsic decomposition for natural images is very challenging. In this paper, we present an unsupervised framework that is able to learn the decomposition effectively from a single natural image by training solely with the image itself. Our approach is built upon the observations that the reflectance of a natural image typically has high internal self-similarity of patches, and a convolutional generation network tends to boost the self-similarity of an image when trained for image reconstruction. Based on the observations, an unsupervised intrinsic decomposition network (UIDNet) consisting of two fully convolutional encoder-decoder sub-networks, i.e., reflectance prediction network (RPN) and shading prediction network (SPN), is devised to decompose an image into reflectance and shading by promoting the internal self-similarity of the reflectance component, in a way that jointly trains RPN and SPN to reproduce the given image. A novel loss function is also designed to make effective the training for intrinsic decomposition. Experimental results on three benchmark real-world datasets demonstrate the superiority of the proposed method.

中文翻译：

---

使用内部自相似性线索的无监督内在图像分解。

最近基于学习的内在图像分解方法取得了显着进展。然而，它们通常需要大量的地面真实内在图像来进行监督学习，这限制了它们在真实世界图像上的适用性，因为获得自然图像的地面真实内在分解非常具有挑战性。在本文中，我们提出了一个无监督框架，该框架能够通过仅使用图像本身进行训练来有效地从单个自然图像中学习分解。我们的方法是建立在观察到的自然图像的反射率通常具有高内部自相似性的补丁，并且卷积生成网络在训练图像重建时倾向于提高图像的自相似性。根据观察，一种由两个全卷积编码器-解码器子网络，即反射率预测网络（RPN）和阴影预测网络（SPN）组成的无监督内在分解网络（UIDNet），旨在通过促进内部图像将图像分解为反射率和阴影。反射分量的自相似性，以一种联合训练 RPN 和 SPN 再现给定图像的方式。还设计了一种新颖的损失函数来有效地进行内在分解的训练。三个基准真实世界数据集的实验结果证明了所提出方法的优越性。旨在通过促进反射分量的内部自相似性将图像分解为反射和阴影，以联合训练 RPN 和 SPN 再现给定图像的方式。还设计了一种新颖的损失函数来有效地进行内在分解的训练。三个基准真实世界数据集的实验结果证明了所提出方法的优越性。旨在通过促进反射分量的内部自相似性将图像分解为反射和阴影，以联合训练 RPN 和 SPN 再现给定图像的方式。还设计了一种新颖的损失函数来有效地进行内在分解的训练。三个基准真实世界数据集的实验结果证明了所提出方法的优越性。