Feature Fusion Based on Sparse Block for Image Super-resolution

Recently, deep neural networks have been widely used in the task of single image super-resolution. However, existing deep neural networks always take huge parameters to map low-resolution images to high-resolution ones. In addition, most of them only consider high-level features to reconstruct high-resolution images. These two methodologies not only cause the difficulty of practical applications but also the inefficiency of restoring image details. Therefore, in this work, the authors propose a novel sparse block to learn high-level features. Based on this block, a fusion method is proposed to fuse features from multiple levels. By incorporating these two approaches, a lightweight neural network, i.e. Sparse Block Fusion Network (SBFN), is proposed for end-to-end training. Through extensive experiments, it is demonstrated the proposed methods can achieve comparable performance with few parameters. By making comprehensive comparisons, effectiveness of SBFN is also verified in multiple benchmark datasets.