An image super-resolution deep learning network based on multi-level feature extraction module

Due to the lack of depth of the super-resolution (SR) method based on shallow networks, the feature maps of different convolutional layers have similar receptive fields, so that the performance improvement is not obvious. To solve this problem effectively, we propose an image SR reconstruction deep...

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Bibliographic Details
Published in:Multimedia tools and applications 2021-02, Vol.80 (5), p.7063-7075
Main Authors: Yang, Xin, Zhang, Yifan, Guo, Yingqing, Zhou, Dake
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Experiments
Feature extraction
Feature maps
Image reconstruction
Image resolution
Machine learning
Modules
Multimedia
Multimedia Information Systems
Neural networks
Special effects
Special Purpose and Application-Based Systems
Visual effects
Visual perception
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Summary:Due to the lack of depth of the super-resolution (SR) method based on shallow networks, the feature maps of different convolutional layers have similar receptive fields, so that the performance improvement is not obvious. To solve this problem effectively, we propose an image SR reconstruction deep model based on a new multi-level feature extraction module in this paper. The method constructs an improved multi-level feature extraction module using the dense connection to obtain a deeper network and richer hierarchical feature maps for the SR task. In addition, we apply the loss function combined with the perceptual characteristics to improve the visual effect of the reconstructed image. Experiments show that the proposed method works well at reconstructed images with different magnification.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09958-4