**您好！從粗體 introducing 部分開始 我有疑問 謝謝！** In this study, we present a novel single image super resolution method by **introducing** dense skip connections in a very deep network. #1
super resolution methods can be significantly boosted by using deep convolutional neural networks.
超分辨率領域中，使用深度卷積網路極大地增強了網路的效能
您好！從粗體 introducing 部分開始 我有疑問 謝謝！
In this study, we present a novel single image super resolution method by introducing dense skip connections in a very deep network.
提出密集跳躍連接方法
In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low level features and high level features to boost the reconstruction performance.
In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing gradient problem of very deep networks.
Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process.
Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency.
We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.
AlexZou14
commented
2 years ago
super resolution methods can be significantly boosted by using deep convolutional neural networks. 超分辨率領域中，使用深度卷積網路極大地增強了網路的效能 您好！從粗體 introducing 部分開始 我有疑問 謝謝！ In this study, we present a novel single image super resolution method by introducing dense skip connections in a very deep network. 提出密集跳躍連接方法 In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low level features and high level features to boost the reconstruction performance. In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing gradient problem of very deep networks. Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process. Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency. We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.