Reflections often degrade the quality of the image by obstructing the background scene. This is not desirable for everyday users, and it negatively impacts the performance of multimedia applications that process images with reflections. Most current methods for removing reflections utilize supervised-learning models. However, these models require an extensive number of image pairs to perform well, especially on natural images with reflection, which is difficult to achieve in practice. In this paper, we propose a novel unsupervised framework for single-image reflection separation. Instead of learning from a large dataset, we optimize the parameters of two cross-coupled deep convolutional networks on a target image to generate two exclusive background and reflection layers. In particular, we design a new architecture of the network to embed semantic features extracted from a pre-trained deep classification network, which gives more meaningful separation similar to human perception. Quantitative and qualitative results on commonly used datasets in the literature show that our method's performance is at least on par with the state-of-the-art supervised methods and, occasionally, better without requiring large training datasets. Our results also show that our method significantly outperforms the closest unsupervised method in the literature for removing reflections from single images.

中文翻译：

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使用感知深度图像先验的无监督单图像反射分离

反射通常会阻碍背景场景，从而降低图像质量。这对于日常用户来说是不可取的，并且会对处理带有反射的图像的多媒体应用程序的性能产生负面影响。消除反射的大多数当前方法利用监督学习模型。然而，这些模型需要大量的图像对才能表现良好，尤其是在具有反射的自然图像上，这在实践中很难实现。在本文中，我们提出了一种用于单图像反射分离的新型无监督框架。我们不是从大型数据集学习，而是在目标图像上优化两个交叉耦合的深度卷积网络的参数，以生成两个专有的背景层和反射层。特别是，我们设计了一种新的网络架构，以嵌入从预先训练的深度分类网络中提取的语义特征，从而提供类似于人类感知的更有意义的分离。文献中常用数据集的定量和定性结果表明，我们的方法的性能至少与最先进的监督方法相当，有时甚至在不需要大型训练数据集的情况下更好。我们的结果还表明，我们的方法明显优于文献中最接近的无监督方法，用于从单个图像中去除反射。s 的性能至少与最先进的监督方法相当，有时甚至在不需要大型训练数据集的情况下更好。我们的结果还表明，我们的方法明显优于文献中最接近的无监督方法，用于从单个图像中去除反射。s 的性能至少与最先进的监督方法相当，有时甚至在不需要大型训练数据集的情况下更好。我们的结果还表明，我们的方法明显优于文献中最接近的无监督方法，用于从单个图像中去除反射。