Single-image super-resolution via selective multi-scale network

In this paper, we aim to improve the performance of single-image super-resolution (SISR) by designing a more effective feature extraction module and a better fusion scheme for integrating hierarchical features. Firstly, we propose a selective multi-scale module (SMsM) to adaptively aggregate multi-scale features via self-learned weights and thus extract more distinctive representation. Then, we design an attentive global feature fusion (AGFF) scheme to reduce the redundant information inside the extracted hierarchical features by employing a gate mechanism (in the form of group convolution) and adaptively re-calibrate the features with channel-wise attention weights before fusion. Stacked SMsMs and AGFF compose a novel network which is termed selective multi-scale network (SMsN). Extensive experimental results demonstrate that our SMsN model outperforms some state-of-the-art SISR methods in terms of accuracy and efficiency.

Automated summary

This paper aims to enhance the performance of single-image super-resolution (SISR) by introducing a selective multi-scale network (SMsN) model which combines selective multi-scale module (SMsM) and attentive global feature fusion (AGFF) scheme. The SMsN model outperforms some state-of-the-art SISR methods in terms of accuracy and efficiency, as demonstrated by extensive experimental results.