Single Image Super-Resolution via Locally Regularized Anchored Neighborhood Regression and Nonlocal Means

@article{Jiang2017SingleIS,
  title={Single Image Super-Resolution via Locally Regularized Anchored Neighborhood Regression and Nonlocal Means},
  author={Junjun Jiang and Xiang Ma and Chen Chen and Tao Lu and Zhongyuan Wang and Jiayi Ma},
  journal={IEEE Transactions on Multimedia},
  year={2017},
  volume={19},
  pages={15-26}
}
The goal of learning-based image super resolution (SR) is to generate a plausible and visually pleasing high-resolution (HR) image from a given low-resolution (LR) input. The SR problem is severely underconstrained, and it has to rely on examples or some strong image priors to reconstruct the missing HR image details. This paper addresses the problem of learning the mapping functions (i.e., projection matrices) between the LR and HR images based on a dictionary of LR and HR examples. Encouraged… CONTINUE READING
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