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Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration

2019-03-21 03:09:19
Xing Liu, Masanori Suganuma, Zhun Sun, Takayuki Okatani

Abstract

In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of paired operations, e.g., up- and down-sampling or convolution with large- and small-size kernels. We design a modular block implementing this connection style; it is equipped with two containers to which arbitrary paired operations are inserted. Adopting the "unraveled" view of the residual networks proposed by Veit et al., we point out that a stack of the proposed modular blocks allows the first operation in a block interact with the second operation in any subsequent blocks. Specifying the two operations in each of the stacked blocks, we build a complete network for each individual task of image restoration. We experimentally evaluate the proposed approach on five image restoration tasks using nine datasets. The results show that the proposed networks with properly chosen paired operations outperform previous methods on almost all of the tasks and datasets.

Abstract (translated)

本文研究了用于图像恢复的深层神经网络的设计。我们提出了一种称为“双重剩余连接”的新型剩余连接,它利用了成对操作的潜力,例如对大小内核进行上下采样或卷积。我们设计了一个实现这种连接风格的模块化块;它配备了两个容器,任意成对的操作插入其中。采用Veit等人提出的剩余网络的“未分解”观点,我们指出,建议的模块化块的堆栈允许块中的第一个操作与任何后续块中的第二个操作交互。在每一个堆叠块中指定两个操作,我们为每一个单独的图像恢复任务建立一个完整的网络。我们使用九个数据集对五个图像恢复任务的方法进行了实验评估。结果表明,在几乎所有的任务和数据集上,选择适当的成对操作的网络都优于以前的方法。

URL

https://arxiv.org/abs/1903.08817

PDF

https://arxiv.org/pdf/1903.08817.pdf


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