Image super-resolution using supervised multi-scale feature extraction network

Abstract

Image super-resolution using deep convolutional networks have recently achieved great successes. However, previous studies have failed to consider the spatial information by simply using a single-size filter, and they do not take full advantage of hierarchical features from low-resolution images, thereby these results are unsatisfactory. In this paper, the supervised convolutional network with multi-scale feature extraction is presented to further improve accuracy. First, the spatial information of the image can be better utilized by different filter sizes. This enhances the adaptability of the network. Second, dense connections are introduced to alleviate the vanishing-gradient problem and accelerate the convergence speed. Third, by adding auxiliary supervised connections to these intermediate layers, they provide additional regularization and increase the backpropagation gradient signal. Extensive experiments on the open challenge datasets confirm the effectiveness of proposed network. Our algorithm can restore high-quality high-resolution images quickly and outperform other methods by a large margin.