Yang, W;
Zhang, X;
Tian, Y;
Wang, W;
Xue, J-H;
Liao, Q;
(2019)
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution.
IEEE Transactions on Image Processing
10.1109/tip.2019.2940679.
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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.
| Type: | Article |
|---|---|
| Title: | LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tip.2019.2940679 |
| Publisher version: | https://doi.org/10.1109/tip.2019.2940679 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Single-image super-resolution , deep convolutional neural networks , skip connection , feature fusion |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10082729 |
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