Jointly Optimized Regressors for Image Super-resolution

@article{Dai2015JointlyOR,
  title={Jointly Optimized Regressors for Image Super-resolution},
  author={Dengxin Dai and Radu Timofte and Luc Van Gool},
  journal={Comput. Graph. Forum},
  year={2015},
  volume={34},
  pages={95-104}
}
Learning regressors from low-resolution patches to high-resolution patches has shown promising results for image super-resolution. We observe that some regressors are better at dealing with certain cases, and others with different cases. In this paper, we jointly learn a collection of regressors, which collectively yield the smallest superresolving error for all training data. After training, each training sample is associated with a label to indicate its ‘best’ regressor, the one yielding the… CONTINUE READING
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Adjusted anchored neighborhood regression for fast superresolution

  • R. TIMOFTE, V. DE SMET, L AVANGOOL
  • ACCV
  • 2014

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