Abstract:
Recently, deep convolutional neural network (CNN) have achieved promising performance for single image super-resolution (SISR). However, they usually extract features on a single scale and lack sufficient supervision information, leading to undesired artifacts and unpleasant noise in super-resolution (SR) images. To address this problem, we first propose a hierarchical feature extraction module (HFEM) to extract the features in multiple scales, which helps concentrate on both local textures and global semantics. Then, a hierarchical guided reconstruction module (HGRM) is introduced to reconstruct more natural structural textures in SR images via intermediate supervisions in a progressive manner. Finally, we integrate HFEM and HGRM in a simple yet efficient end-to-end framework named hierarchical generative adversarial networks (HSRGAN) to recover consistent details, and thus obtain the semantically reasonable and visually realistic results. Extensive experiments on five common datasets demonstrate that our method shows favorable visual quality and superior quantitative performance compared to state-of-the-art methods for SISR.
