Learning single image deraining (SID) networks from an unpaired set of clean
and rainy images is practical and valuable as acquiring paired real-world data
is almost infeasible. However, without the paired data as the supervision,
learning a SID network is challenging. Moreover, simply using existing unpaired
learning methods (e.g., unpaired adversarial learning and cycle-consistency
constraints) in the SID task is insufficient to learn the underlying
relationship from rainy inputs to clean outputs as there exists significant
domain gap between the rainy and clean images. In this paper, we develop an
effective unpaired SID adversarial framework which explores mutual properties
of the unpaired exemplars by a dual contrastive learning manner in a deep
feature space, named as DCD-GAN. The proposed method mainly consists of two
cooperative branches: Bidirectional Translation Branch (BTB) and Contrastive
Guidance Branch (CGB). Specifically, BTB exploits full advantage of the
circulatory architecture of adversarial consistency to generate abundant
exemplar pairs and excavates latent feature distributions between two domains
by equipping it with bidirectional mapping. Simultaneously, CGB implicitly
constrains the embeddings of different exemplars in the deep feature space by
encouraging the similar feature distributions closer while pushing the
dissimilar further away, in order to better facilitate rain removal and help
image restoration. Extensive experiments demonstrate that our method performs
favorably against existing unpaired deraining approaches on both synthetic and
real-world datasets, and generates comparable results against several
fully-supervised or semi-supervised models.

本文提出了一种探索非配对样本的双重对比学习方式，在深度特征空间中了解非配对样本的共同属性，开发出有效的无配对单影像去雨敌对框架称为 DCD-GAN。它包括双向翻译分支 (BTB) 和对比指导分支 (CGB)，通过计算机实验表明，与现有的无配对去雨方法相比，在合成和真实世界数据集上，我们的方法表现优异，并且与多个全监督或半监督模型产生可比较的结果。