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.

该研究论文提出了一种无监督学习方法，它通过显式地从噪声数据中学习不变展示来重建清晰图像，并通过引入离散解缠表示和敌对域自适应等额外的自监督模块实现。实验表明该方法在合成和真实噪声去除任务方面表现与最先进的监督和无监督方法相当，同时比其他领域适应方法具有更快和更稳定的收敛性。