UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution

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. Green open access

[img]
Preview
Text
arxiv-1909.03573.pdf - Accepted version

Download (3MB) | Preview

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 > 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.ucl.ac.uk/id/eprint/10082729
Downloads since deposit
21Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item