In recent years, deep convolutional neural networks (CNNs) have been widely applied to handle low-level vision problems. However, most existing CNN-based approaches can either handle single degeneration each time or treat them jointly through feature entangling, thus likely leading to poor performance when the actual degradation is inconsistent with hypothetical degradation condition. Furthermore, feature coupling will bring a large amount of computation, which may make the methods impractical to real-time mobile scenarios. In order to address these problems, we propose a deep decoupled cooperative learning model which can not only develop the corresponding recover network to deal with each degradation, but also flexibly handle multiple degradations at the same time. Thus, our approach can achieve disentangling and synthesizing single image super-resolution and motion deblurring, which has high practicability. We evaluate the proposed approach on various benchmark datasets, covering both natural images and synthetic images. The results demonstrate its superiority, compared to the state-of-the-art, where image SR and motion deblurring can be accomplished effectively concurrently. The source code of the work is available at https://github.com/hengliusky/Cooperative-Learning-Deblur-SR.

近年来，深度卷积神经网络（CNN）已广泛应用于处理低级视觉问题。但是，大多数现有的基于CNN的方法既可以每次处理一次退化，也可以通过特征纠缠共同处理它们，因此，当实际退化与假设的退化条件不一致时，很可能导致性能下降。此外，特征耦合将带来大量的计算，这可能会使这些方法不适用于实时移动场景。为了解决这些问题，我们提出了一种深度解耦的合作学习模型，该模型不仅可以开发相应的恢复网络来处理每个退化，而且可以灵活地同时处理多个退化。从而，我们的方法可以解开并合成单幅图像的超分辨率和运动去模糊，具有很高的实用性。我们在各种基准数据集上评估提出的方法，涵盖自然图像和合成图像。结果表明，与可以同时有效完成图像SR和运动去模糊的最新技术相比，它具有优越性。作品的源代码可在https://github.com/hengliusky/Cooperative-Learning-Deblur-SR上找到。