Lightweight image super-resolution with a feature-refined network

In recent years, deep Convolutional Neural Networks (CNNs) have achieved impressive successes on the Single Image Super-Resolution task (SISR). However, it remains difficult to apply these CNN-based SISR methods in embedded devices due to their high memory and computational requirements. To alleviate this issue, we focus on lightweight SISR methods. The observed similarity between the feature maps in CNNs serves as inspiration to explore the design of a cost-efficient module to obtain feature maps whose representation ability is roughly equivalent to that of a conventional convolutional layer. We thus propose a shadow module applying simple linear transformations with a lower cost to generate similar feature maps. Based on this module, we design a Feature-Refined Block (FRB) to learn more representative features. Besides, we propose a Global Dense Feature Fusion (GDFF) structure to construct a Feature-Refined Network (FRN) with such FRBs for lightweight SISR. Extensive experimental results demonstrate the superior performance of the proposed FRN in comparison with the state-of-the-art lightweight SISR methods, while consuming relatively low memory and computation resources.