LCSCNet：基于线性压缩的图像超分辨率跳接网络
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
摘要：
在本文中，我们开发了一个简洁而高效的网络架构称为线性压缩的跳过连接网络（LCSCNet）的图像超分辨率。与ResNet和DenseNet这两种具有代表性的跳跃连接网络结构相比，LCSCNet设计了一个线性压缩层，用于跳跃连接，连接以前的特征图，并将它们与新开发的特征图区分开来。这样，建议LCSCNet享有DenseNet的区别特征处理和ResNet的参数经济形式的优点。此外，为了更好地利用深度模型中各种感受野的低层次和高层次信息，受LSTM中门单元的启发，我们还提出了一种具有多监督训练的自适应元素融合策略。实验结果与现有算法的比较验证了LCSCNet的有效性。
英文摘要：
In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parameter-economic form of ResNet. Moreover, to better exploit hierarchical information from both low and high levels of various receptive fields in deep models, inspired by gate units in LSTM, we also propose an adaptive element-wise fusion strategy with multi-supervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.
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