Abstract:  In recent years, the convolutional neural network (CNN) has achieved desired results in the field of single image super-resolution (SISR). Deep networks can establish complex mapping between low-resolution and high-resolution images, considerably enhancing the quality of reconstructed images, compared with the traditional methods. Since the existing SISR methods mainly increase the receptive field of convolution kernels by deepening and widening the network structure, and employ equal processing methods in spatial domains and channel domains of varying importance, a large number of computing resources are wasted on unimportant features. In order to address the realistic problems of the existing models, the algorithm proposed in this paper captured implicit weight information in channel and space domains through dual attention modules, so as to allocate computing resources more effectively and speed up the network convergence. The fusion of global features through residual connections in this network not only focused on learning the high-frequency information of images that had been lost, but also accelerated the network convergence through effective feature supervision. In order to alleviate the defects of the MAE loss function, a special Huber loss function was introduced in the algorithm. The experimental results on benchmark show that the proposed algorithm can significantly improve the image reconstruction accuracy compared with the existent SISR methods. 

Key words:  , single image super-resolution, feature supervision, residual connection, channel attention, spatial attention