Recently, the end-to-end learning-based methods have been proven effective for the blind image deblurring. Without human-made assumptions or numerical algorithms, they are able to restore images with fewer artifacts and better perceptual quality. However, in practice, these methods suffer from limited performance under complex motion scenario and produces unnatural results sometimes. In this paper, in order to overcome their limitations, we propose to integrate deep convolution neural networks into a conventional deblurring framework. Specifically, we propose Stacked Estimation Residual Net (SEN) to estimate the motion flow map and Recurrent Prior Generative and Adversarial Net (RP-GAN) to learn the implicit image prior for the optimization. Comparing with the state-of-the-art end-to-end learning-based methods, the proposed method restores image content more naturally and shows better generalization ability.

中文翻译：

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将神经网络集成到盲去模糊框架中，以与基于端到端学习的方法竞争

最近，已经证明了基于端到端学习的方法对于盲图像去模糊是有效的。没有人为的假设或数值算法，它们就能够以更少的伪像和更好的感知质量恢复图像。但是，实际上，这些方法在复杂的运动场景下性能有限，有时会产生不自然的结果。在本文中，为了克服它们的局限性，我们建议将深度卷积神经网络集成到常规的去模糊框架中。具体来说，我们提出了堆叠估计残差网（SEN）来估计运动流图，而递归先验生成和对抗网（RP-GAN）则要先学习隐式图像以进行优化。与最新的端到端基于学习的方法相比，