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Bi-path network coupling for single image super-resolution

机译：单路径超分辨率的双路径网络耦合

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摘要

Recent researches have shown that deep convolutional neural networks can significantly boost the performance of single-image super-resolution (SISR). In particular, residual network and densely convolutional network can improve performance remarkably. The residual network enables feature re-usage and the dense skip connections enables new features exploration, which are both favor for feature extraction. In order to alleviate the vanishing-gradient problem in very deep convolution networks. In this paper, a bi-path network coupling is presented for SISR by combining the residual network and the dense skip connections in a very deep network. More specifically, the feature maps in the proposed network are split into two paths, one path is propagated in the form of residual connections, and another is propagated by dense skip connections. In addition, we input the feature maps obtained from the two paths into the coupling layer for feature fusion. Finally, the deconvolution layers are integrated into the network to upscale the feature map for significantly accelerating the network, that the mapping is learned from the low-resolution image to the high-resolution image directly. The proposed network was evaluated on four benchmark datasets and has achieved competing or even higher peak signal-to-noise ratio (PSNR) than most of state-of-the-art methods.
机译：最近的研究表明，深度卷积神经网络可以显着提高单图像超分辨率（SISR）的性能。特别是，残留网络和密集卷积网络可以显着提高性能。残差网络可启用要素重用，密集的跳过连接可启用新要素探索，这两者都有助于要素提取。为了缓解非常深的卷积网络中的消失梯度问题。本文通过将残差网络和非常深层网络中的密集跳过连接相结合，提出了一种用于SISR的双路径网络耦合。更具体地说，将所提出的网络中的特征图分为两条路径，一条路径以残余连接的形式传播，另一条通过密集的跳过连接传播。此外，我们将从两条路径获得的特征图输入到耦合层中以进行特征融合。最后，将反卷积层集成到网络中，以放大特征图，从而显着加速网络，即直接从低分辨率图像学习到高分辨率图像。拟议的网络在四个基准数据集上进行了评估，与大多数最新方法相比，其竞争性甚至更高的峰值信噪比（PSNR）。

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