Single Image Super-Resolution via Similarity Between Spatially Scattered Features

The development of convolutional neural networks (CNN) has remarkably improved the current research on single image super-resolution (SISR). Several high-quality studies have been performed on reconstruction accuracy and perceptual quality, which are the two main issues in SISR. Nevertheless, numerous problems in SISR remain unsolved. SISR is inherently an ill-posed problem owing to insufficient information, and as the scale factor increases, the lack of information becomes even more pronounced. We have studied ways to solve the local characteristics of CNN to deal with additional useful information. A CNN uses a convolution layer designed based on local features, and repeatedly accumulates these features to expand a receptive field. We have explored network structures that can directly handle global information even at lower layers, which are not covered by the receptive field of a CNN. In this paper, we propose a non-local attention SISR network (NASR) that generates and utilizes the globally scattered similarity information of features. In addition, we propose a very deep architecture based on dense blocks that does not suffer from gradient vanishing without any normalization. Experimental results on standard benchmark datasets indicate the effectiveness of the proposed network, which exhibits state-of-the-art performance in terms of reconstruction accuracy and perceptual quality.