Matlab code for CCR: Clustering and Collaborative Representation for Fast Single Image Super-Resolution
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Aplus_x3_1024atoms2048nn_5mil.mat mat Aug 21, 2017
CCR_x3_K1024_max2048_d6_lambda30.mat mat Aug 21, 2017
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Example_Zeyde.m matlab Aug 21, 2017
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PP_CCR_Set10_TMM_demo.m matlab Aug 21, 2017
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README.md bib Aug 21, 2017
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README_CCR.txt bib Aug 21, 2017
Temp_coding.m matlab Aug 21, 2017
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calc_PeakSNR.m matlab Aug 21, 2017
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collect.m matlab Aug 21, 2017
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scaleup_CCR.m matlab Aug 21, 2017
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README.md

CCR-SISR

README for CCR Updated on 2016/07/05, by Yulun Zhang, yulun100@gmail.com

Reproduce the results presented in our TMM2016 paper 'CCR: Clustering and Collaborative Representation for Fast Single Image Super-Resolution'

Just run 'PP_CCR_Set10_TMM_demo.m' to get a start.

This demo code is based on the codes released by Timofte et al.. Many thanks to them!

Please cite: [1] Radu Timofte, Vincent De Smet, Luc Van Gool: A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014.

[2] Radu Timofte, Vincent De Smet, Luc Van Gool: Anchored Neighborhood Regression for Fast Example-Based Super-Resolution, ICCV 2013.

[3] Yulun Zhang, Yongbing Zhang, Jian Zhang, and Qionghai Dai: CCR: Clustering and collaborative representation for fast single image super-resolution, TMM 2016.

@article{zhang2016ccr, title={CCR: Clustering and collaborative representation for fast single image super-resolution}, author={Zhang, Yulun and Zhang, Yongbing and Zhang, Jian and Wang, Haoqian and Dai, Qiongdai}, journal={{IEEE} Trans. Multimedia}, volume={18}, number={3}, pages={405--417}, month={Mar.}, year={2016}, publisher={IEEE} }

For more information about the code (e.g., OMPBox and KSVDBox), please contact me or read the following README from A+.