Deep hybrid residual learning with statistic priors for single image super-resolution

This paper considers single image super-resolution (SISR), which is an important low-level vision task and has various applications in multimedia society. Recently, deep neural networks have archived good performance on this field. But most of existing deep models are based on the fully data-dependent network architecture, thus missing majority of domain-knowledge of the super-resolution task. To address this limitation, we develop a new hybrid residual learning approach to leverage priors of SISR within the maximum a posteriori framework for network architecture design. We demonstrate that it can incorporate both image priors and data fidelity into the network, leading to a novel cascaded residual learning system for SISR process. Extensive experimental results on real-world images show that the proposed algorithm performs favorably against state-of-the-art methods.