/TLcR-RL

Maltab code for TLcR-RL based face hallucination (IEEE TCYB, 2018)

Primary LanguageMATLAB

We have updated the code on Oct 5, 2018. Now, the performance is the same as in our paper.

The code is for the work (it achieves the state-of-the-art perfromance for patch based face super-resolution):

@inproceedings{jiang2017context,
  title={Context-patch based face hallucination via thresholding locality-constrained representation and reproducing learning},
  author={Jiang, Junjun and Yu, Yi and Tang, Suhua and Ma, Jiayi and Qi, Guo-Jun and Aizawa, Akiko},
  booktitle={ICME 2017},
  pages={469--474},
  year={2017},
  organization={IEEE}
}

@article{jiang2018context,
  title={Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning},
  author={Jiang, Junjun and Yu, Yi and Tang, Suhua and Ma, Jiayi and Aizawa, Akiko and Aizawa, Kiyoharu},
  journal={IEEE Transactions on Cybernetics},
  year={2018}
}

You can run the Demo_TLcR_RL.m

Note that all the results in our paper were conducted in MATLAB R2014a.

We also provide the results of all comparison methods, including Wang et al.'s method [16], NE [14], LSR [4], SR [5], LcR [6], LINE [15], SRCNN [9], TLcR, and the proposed TLcR-RL, in the file of 'other results'.

Demo_other_methods.m is implementation of Wang et al.'s method [16], NE [14], LSR [4], SR [5], LcR [6], and LINE [15].