Progressive residual networks for image super-resolution

• DOI: 10.1007/s10489-019-01548-8
• Publication Date: 2020-02-01T04:55:31Z
• Authors: Jin Wan; Hui Yin; Ai-Xin Chong; Zhi-Hao Liu
• Keywords: 0211 other engineering and technologies; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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 ×.