[1]CHENG Deqiang,ZHU Xingguang,KOU Qiqi,et al.Image super-resolution reconstruction by fusing layered features with residual distillation connections[J].CAAI Transactions on Intelligent Systems,2023,18(6):1173-1184.[doi:10.11992/tis.202304011]
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Image super-resolution reconstruction by fusing layered features with residual distillation connections

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1173-1184 Column: 学术论文—机器学习 Public date: 2023-11-05
Title:
Image super-resolution reconstruction by fusing layered features with residual distillation connections
Author(s):
CHENG Deqiang1;  ZHU Xingguang1;  KOU Qiqi2;  CHEN Liangliang1;  WANG Xiaoyi1;  ZHAO Jiamin1
1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Computer Science & Technology, China University of Mining and Technology, Xuzhou 221116, China
Keywords:
image processing; super-resolution reconstruction; U-network; residual connectivity; neural network; feature fusion; attention mechanism; subpixel convolution
CLC:
TP391
DOI:
10.11992/tis.202304011
Abstract:
Aiming at the issue that several current image super-resolution reconstruction algorithms cannot fully utilize the feature information by adopting a single-channel network structure, a super-resolution reconstruction algorithm that fuses hierarchical features and residual distillation connections is proposed. In this method, a connection method that combines layered features with residual connections is adopted to fully fuse the deep and shallow features of an image, improving the utilization of feature information by the network. Further, a residual distillation attention module is used, which enables the network to focus on the key features of the image efficiently, allowing efficient recovery of the detailed features of the reconstructed image. The experimental results showed that the proposed algorithmic model exhibits better objective evaluation indexes on four test sets and has a superior reconstruction effect on the subjective visual effect. Specifically, on the Set14 test set, the peak signal-to-noise ratio of the four-fold reconstruction results of the model is improved by 0.85 dB on average relative to the comparison model and the structural similarity is improved by 0.034 on average, demonstrating the effectiveness of the algorithmic model.
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