In this paper, we propose a deep CNN to tackle the image restoration problem by learning formatted information. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a residual formatting layer and an adversarial block to format the information to structured one, which allows the network to converge faster and boosts the performance. Furthermore, we propose a cross-level loss net to ensure both pixel-level accuracy and semantic-level visual quality. Evaluations on public datasets show that the proposed method performs favorably against existing approaches quantitatively and qualitatively.

中文翻译：

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FormNet：图像恢复的格式化学习。


在本文中，我们提出了一种深度 CNN，通过学习格式化信息来解决图像恢复问题。以前基于深度学习的方法直接学习从损坏图像到干净图像的映射，并且可能会遇到深度神经网络的梯度爆炸/消失问题。我们建议通过学习结构化细节并从损坏图像和潜在图像之间的共享信息一起恢复潜在的干净图像来解决图像恢复问题。此外，我们建议添加一个残差格式化层和一个对抗性块来将信息格式化为结构化信息，而不是学习纯粹的差异（损坏），从而使网络更快地收敛并提高性能。此外，我们提出了一个跨级损失网络来确保像素级精度和语义级视觉质量。对公共数据集的评估表明，所提出的方法在数量和质量上都优于现有方法。