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

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

9 Sep 2019arXiv:1909.03573archive 2025-07-28

Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue, Qingmin Liao

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.

Paper PDF Code

Code

XuechenZhang123/LCSC officialmentioned in paperpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Image Super-Resolution Super-Resolution

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Super-Resolution	BSD100 - 3x upscaling	LCSCNet	PSNR	28.87	#19 of 21	Archive leaderboard	report
Image Super-Resolution	Set14 - 3x upscaling	LCSCNet	PSNR	29.87	#20 of 24	Archive leaderboard	report
Image Super-Resolution	Set5 - 3x upscaling	LCSCNet	PSNR	33.99	#26 of 32	Archive leaderboard	report
Image Super-Resolution	Urban100 - 3x upscaling	LCSCNet	PSNR	27.24	#21 of 22	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization LSTM Max Pooling ReLU Residual Block Residual Connection Sigmoid Activation Softmax Tanh Activation

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections