Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
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发表时间：
2016-03
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通讯作者：
Xiao-Jiao Mao;Chunhua Shen;Yubin Yang
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作者：
Xiao-Jiao Mao;Chunhua Shen;Yubin Yang
在本文中，我们提出了一种非常深的全卷积编码解码框架，用于图像恢复，例如去噪和超分辨率。该网络由多层卷积和反卷积算子组成，学习从损坏图像到原始图像的端到端映射。卷积层充当特征提取器，捕获图像内容的抽象，同时消除噪声/损坏。然后使用反卷积层来恢复图像细节。我们建议通过跳层连接对称地连接卷积层和反卷积层，这样训练收敛得更快，并获得更高质量的局部最优。首先，跳跃连接允许信号直接反向传播到底层，从而解决了梯度消失的问题，使深度网络的训练变得更容易，从而实现恢复性能增益。其次，这些跳跃连接将图像细节从卷积层传递到反卷积层，这有利于恢复原始图像。值得注意的是，凭借大容量，我们可以使用单一模型处理不同级别的噪声。实验结果表明，我们的网络比之前报道的所有最先进的方法取得了更好的性能。
In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. De-convolutional layers are then used to recover the image details. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. First, The skip connections allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to de-convolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than all previously reported state-of-the-art methods.