A Novel Super-resolution Method Based on Patch Reconstruction with Simk Clustering and Nonlinear Mapping

Abstract

In this paper, we propose a patch-wise super-resolution (SR) method that combines an external-sample classification tree and a nonlinear-mapping learning stage to simultaneously guarantees reconstruction quality and speed at the stage of patch representation and mapping. We use the low-resolution (LR) to high-resolution (HR) mapping kernel of each patch-pair sample (called SIMK) to complete classification by binary tree branching and provide reasonable training sets for mapping-learning. Then a high accuracy but low cost lightweight network is learned for each tree node to choose the reasonable branch path for the testing LR patches. In the mapping-learning stage, the nonlinear mapping for each class is represented as a full-connected network, which provides satisfying generalization ability for LR patch reconstruction. Comparing with state-of-the-art methods, our approach achieves real-time (>24fps) SR of realistic vision and high quality for different upscaling factors.