LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 31567083.
- Also identified by DOI 10.1109/TIP.2019.2940679.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
In this paper, we develop a concise but efficient network architecture called linear compressing based skipconnecting 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 parametereconomic 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 multisupervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.