Deep recursive up-down sampling networks for single image super-resolution

Abstract

Single image super-resolution (SISR) technology can reconstruct a high-resolution (HR) image from the corresponding low-resolution (LR) image. The emergence of deep learning pushes SISR to a new level. The successful application of the recursive network motivates us to explore a more efficient SISR method. In this paper, we propose the deep recursive up-down sampling networks (DRUDN) for SISR. In DRUDN, an original LR image is directly fed without extra interpolation. Then, we use the sophisticated recursive up-down sampling blocks (RUDB) to learn the complex mapping between the LR image and the HR image. At the reconstruction part, the feature map is up-scaled to the ideal size by a de-convolutional layer. Extensive experiments demonstrate that DRUDN outperforms the state-of-the-art methods in both subjective effects and objective evaluation.