论文标题
学习无监督图像修复的不变表示形式
Learning Invariant Representation for Unsupervised Image Restoration
论文作者
论文摘要
最近，跨域转移已用于无监督的图像恢复任务。但是，由于缺乏有效的监督，直接应用现有框架将导致翻译图像中的域移动问题。取而代之的是，我们提出了一种无监督的学习方法，该方法明确地从嘈杂的数据中学习了不变的演示，并重建了清晰的观察结果。为此，我们在通用域转移框架中引入了离散的分离表示和对抗域的适应性，并在额外的自我监督模块的帮助下，包括背景和语义一致性约束，在双重域约束下学习可靠的表示，例如特征和图像域。关于合成和真实噪声删除任务的实验表明，所提出的方法与其他最先进的监督和无监督的方法可相当，同时比其他域的适应方法更快，更稳定。
Recently, cross domain transfer has been applied for unsupervised image restoration tasks. However, directly applying existing frameworks would lead to domain-shift problems in translated images due to lack of effective supervision. Instead, we propose an unsupervised learning method that explicitly learns invariant presentation from noisy data and reconstructs clear observations. To do so, we introduce discrete disentangling representation and adversarial domain adaption into general domain transfer framework, aided by extra self-supervised modules including background and semantic consistency constraints, learning robust representation under dual domain constraints, such as feature and image domains. Experiments on synthetic and real noise removal tasks show the proposed method achieves comparable performance with other state-of-the-art supervised and unsupervised methods, while having faster and stable convergence than other domain adaption methods.
