LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
This work addresses image super-resolution for computer vision applications, presenting an incremental improvement by combining elements from ResNet and DenseNet.
The paper tackled image super-resolution by proposing LCSCNet, a network that uses linear compressing layers for skip connections and an adaptive fusion strategy, achieving results validated against state-of-the-art algorithms.
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.