Image Super-Resolution Using Multi-Resolution Attention Network

In recent years, single image super-resolution based on convolution neural network (CNN) has been extensively researched. However, most CNN-based methods only focus on mining features at a single resolution, which will cause the loss of some useful information. Besides, most of them still have difficulty in training and obtaining high-quality images for large scale factors. To address these issues, we propose a multi-resolution attention network (MRAN), which progressively reconstructs images at large scale factors by aggregating features from multiple resolutions. Specially, a multi-resolution residual block (MRRB) is designed as basic block to specialize features at different resolutions and share information across different resolutions, improving the representation ability of features. Simultaneously, we design a resolution-wise attention block (RAB) to evaluate the importance of features from different resolutions, making the use of features more effective and enhancing feature fusion. Experimental results show that our proposed method is superior to the state-of-the-art methods.