Progressive residual networks for image super-resolution

The recent advances in deep convolutional neural networks (DCNNs) have convincingly demonstrated high-capability reconstruction for single image super-resolution (SR). However, it is a big challenge for most DCNNs-based SR models when the scaling factor increases. In this paper, we propose a novel P...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2020-05, Vol.50 (5), p.1620-1632
Main Authors: Wan, Jin, Yin, Hui, Chong, Ai-Xin, Liu, Zhi-Hao
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Computer Science
Convolution
Feature extraction
Image quality
Image reconstruction
Image resolution
Image restoration
Machines
Manufacturing
Mechanical Engineering
Modules
Processes
Scaling factors
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Summary:The recent advances in deep convolutional neural networks (DCNNs) have convincingly demonstrated high-capability reconstruction for single image super-resolution (SR). However, it is a big challenge for most DCNNs-based SR models when the scaling factor increases. In this paper, we propose a novel Progressive Residual Network (PRNet) to integrate hierarchical and scale features for single image SR, which works well for both small and large scaling factors. Specifically, we introduce a Progressive Residual Module (PRM) to extract local multi-scale features through dense connected up-sampling convolution layers. Meanwhile, by embedding residual learning into each module, the relative information between high-resolution and low-resolution multi-scale features is fully exploited to boost reconstruction performance. Finally, the scale-specific features are fused to the reconstruction module for restoring the high-quality image. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate that our PRNet achieves superior performance and in particular obtains new state-of-the-art results for large scaling factors such as 4 × and 8 ×.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-019-01548-8