Recent restoration methods for handling real old photos have achieved significant improvements using generative networks. However, the restoration quality under the usual generative architectures is greatly affected by the encoded properties of latent space, which reflect pivotal semantic information in the recovery process. Therefore, how to find the suitable latent space and identify its semantic factors is an important issue in this challenging task. To this end, we propose a novel generative network with hyperbolic embeddings to restore old photos that suffer from multiple degradations. Specifically, we transform high-dimensional Euclidean features into a compact latent space via the hyperbolic operations. In order to enhance the hierarchical representative capability, we perform the channel mixing and group convolutions for the intermediate hyperbolic features. By using attention-based aggregation mechanism in a hyperbolic space, we can further obtain the resulting latent vectors, which are more effective in encoding the important semantic factors and improving the restoration quality. Besides, we design a diversity loss to guide each latent vector to disentangle different semantics. Extensive experiments have shown that our method is able to generate visually pleasing photos and outperforms state-of-the-art restoration methods.

中文翻译：

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学习旧照片修复潜在空间的紧凑双曲表示


最近处理真实老照片的修复方法已经利用生成网络取得了显着的改进。然而，通常的生成架构下的恢复质量很大程度上受到潜在空间的编码属性的影响，而潜在空间反映了恢复过程中的关键语义信息。因此，如何找到合适的潜在空间并识别其语义因素是这一具有挑战性的任务中的一个重要问题。为此，我们提出了一种具有双曲嵌入的新型生成网络，以恢复遭受多重退化的旧照片。具体来说，我们通过双曲运算将高维欧几里得特征转换为紧凑的潜在空间。为了增强层次表示能力，我们对中间双曲特征进行通道混合和组卷积。通过在双曲空间中使用基于注意力的聚合机制，我们可以进一步获得所得到的潜在向量，这些潜在向量可以更有效地编码重要的语义因素并提高恢复质量。此外，我们设计了多样性损失来引导每个潜在向量解开不同的语义。大量的实验表明，我们的方法能够生成视觉上令人愉悦的照片，并且优于最先进的修复方法。