Single image super-resolution based on mapping-vector clustering and nonlinear pixel-reconstruction
作者：
Highlights：
• To obtain more reasonable sample sets for mapping-learning, we classify external samples by clustering the mapping-vector of LR-HR patch pairs based on fractional-norm.
• A decision-tree branched by lightweight networks is learned to choose one reasonable class for each testing LR patch.
• In our mapping-learning stage, pixel-wise nonlinear mappings are represented as several full connected networks, which can provide satisfying generalization ability for LR patch reconstruction.
摘要
•To obtain more reasonable sample sets for mapping-learning, we classify external samples by clustering the mapping-vector of LR-HR patch pairs based on fractional-norm.•A decision-tree branched by lightweight networks is learned to choose one reasonable class for each testing LR patch.•In our mapping-learning stage, pixel-wise nonlinear mappings are represented as several full connected networks, which can provide satisfying generalization ability for LR patch reconstruction.
论文关键词：Super resolution,Patch reconstruction,Decision-tree,Nonlinear-mapping learning
论文评审过程：Received 16 June 2020, Revised 13 July 2021, Accepted 14 September 2021, Available online 29 September 2021, Version of Record 20 October 2021.
论文官网地址：https://doi.org/10.1016/j.image.2021.116501
