Abstract Single image rain removal plays an important role in numerous multimedia applications. Existing algorithms usually tackle the deraining problem by the way of signal removal, which lead to over-smoothness and generate unexpected artifacts in de-rained images. This paper addresses the deraining problem from a completely different perspective of feature-wise disentanglement, and introduces the interactions and constraints between two disentangled latent spaces. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN) to progressively disentangle the rainy image into background and rain spaces in feature level through a two-branch structure. Each branch employs a two-stage synthesis strategy and interacts asynchronously by exchanging feed-forward information and sharing feedback gradients, achieving complementary adversarial optimization. This ‘adversarial’ is not only the ‘adversarial’ between the generator and the discriminator, but also means that the two generators are entangled, and interact with each other in the optimization process. Extensive experimental results demonstrate that AI-GAN outperforms state-of-the-art deraining methods and benefits various typical multimedia applications such as Image/Video Coding, Action Recognition, and Person Re-identification.

中文翻译：

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AI-GAN：用于单幅图像去雨的异步交互式生成对抗网络

摘要 单幅图像除雨在众多多媒体应用中发挥着重要作用。现有算法通常通过去除信号的方式来解决去雨问题，这会导致去雨图像中的过度平滑并产生意想不到的伪影。本文从特征解缠结的完全不同的角度解决了去雨问题，并介绍了两个解缠结的潜在空间之间的相互作用和约束。具体来说，我们提出了一种异步交互式生成对抗网络 (AI-GAN)，通过两分支结构将雨天图像逐步分解为特征级别的背景和雨天空间。每个分支采用两阶段合成策略，并通过交换前馈信息和共享反馈梯度进行异步交互，实现互补的对抗优化。这种“对抗”不仅是生成器和判别器之间的“对抗”，还意味着两个生成器在优化过程中相互纠缠、相互作用。大量实验结果表明，AI-GAN 优于最先进的去雨方法，并有利于各种典型的多媒体应用，如图像/视频编码、动作识别和人员重新识别。