Matlab code for Collaborative Representation Cascade for Single-Image Super-Resolution
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Set10Dong
Set14
Set5
SetSRF
ksvdbox
methods
ompbox
.gitattributes
.gitignore
Aplus_x2_1024atoms2048nn_5mil.mat
Aplus_x3_1024atoms2048nn_5mil.mat
Aplus_x4_1024atoms2048nn_5mil.mat
App_x2_1024atoms2048nn_5mil_lam1_90_lam2_100.mat
App_x3_1024atoms2048nn_5mil_lam1_90_lam2_100.mat
App_x4_1024atoms2048nn_5mil_lam1_90_lam2_100.mat
Demo_CRC.m
Example_Zeyde.m
FeatureSIM.m
ICR_App_1_x2_1024atoms2048nn_5mil.mat
ICR_App_1_x3_1024atoms2048nn_5mil.mat
ICR_App_1_x4_1024atoms2048nn_5mil.mat
ICR_App_2_x2_1024atoms2048nn_5mil.mat
ICR_App_2_x3_1024atoms2048nn_5mil.mat
ICR_App_2_x4_1024atoms2048nn_5mil.mat
ICR_x2_1024atoms2048nn_5mil.mat
ICR_x3_1024atoms2048nn_5mil.mat
ICR_x4_1024atoms2048nn_5mil.mat
PP_PSNR.m
PP_RMSE.m
PP_collectSamplesScales.m
PP_glob.m
PP_learn_dict.m
PP_learn_dict_MIKSVD.m
README.md
README_CRC.txt
calc_PSNR.m
collect.m
collectSamplesScales.m
conf_ICR_1024_finalx2.mat
conf_ICR_1024_finalx3.mat
conf_ICR_1024_finalx4.mat
conf_ICR_App_1_1024_finalx2.mat
conf_ICR_App_1_1024_finalx3.mat
conf_ICR_App_1_1024_finalx4.mat
conf_ICR_App_2_1024_finalx2.mat
conf_ICR_App_2_1024_finalx3.mat
conf_ICR_App_2_1024_finalx4.mat
conf_MIKSVD_1024_x2_lam1_90.mat
conf_MIKSVD_1024_x3_lam1_90.mat
conf_MIKSVD_1024_x4_lam1_90.mat
conf_Zeyde_1024_finalx2.mat
conf_Zeyde_1024_finalx3.mat
conf_Zeyde_1024_finalx4.mat
extract.m
glob.m
go_prepare_image.m
go_prepare_image_Aplus.m
go_prepare_images_paper_x3.m
go_prepare_images_paper_x3_Aplus.m
learn_dict.m
learn_dict_MIKSVD.m
load_images.m
modcrop.m
overlap_add.m
process_scores_Tex.m
progress.m
qmkdir.m
resize.m
run_comparison.m
run_comparisonRGB.m
run_comparisonRGB_PSNR.m
sampling_grid.m
scaleup_APP_Zhang_MIKSVD.m
scaleup_ICR.m
scaleup_ICR_MIKSVD.m
scaleup_ICR_one.m
scaleup_ICR_one_MIKSVD.m
shave.m
split_path.m
yima.m

README.md

CRC-SISR

Matlab code for Collaborative Representation Cascade for Single-Image Super-Resolution

% Collaborative Representation Cascade for Single-Image Super-Resolution % Example code

% This code is built on the example code of "Anchored Neighborhood % Regression for Fast Example-Based Super-Resolution".

% Please refere to papers: % [1] Radu Timofte, Vincent De Smet, Luc Van Gool. % Anchored Neighborhood Regression for Fast Example-Based Super-Resolution. % International Conference on Computer Vision (ICCV), 2013.

% [2] Radu Timofte, Vincent De Smet, Luc Van Gool. % A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. % Asian Conference on Computer Vision (ACCV), 2014.

% [3] Yongbing Zhang, Yulun Zhang, Jian Zhang, Dong Xu, Yun Fu, Yisen Wang, Xiangyang Ji, Qionghai Dai % Collaborative Representation Cascade for Single-Image Super-Resolution % IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC), vol. PP, no. 99, pp. 1-16, 2017.

% [4] Yulun Zhang, Kaiyu Gu, Yongbing Zhang, Jian Zhang, Qionghai Dai % Image Super-Resolution based on Dictionary Learning and Anchored Neighborhood Regression with Mutual Inconherence % IEEE International Conference on Image Processing (ICIP2015), Quebec, Canada, Sep. 2015.

% [5] Yulun Zhang, Yongbing Zhang, Jian Zhang, Haoqian Wang, Qionghai Dai % Single Image Super-Resolution via Iterative Collaborative Representation % Paci?c-Rim Conference on Multimedia (PCM2015), Gwangju, Korea, Sep. 2015.

% For any questions, email me by yulun100@gmail.com

% Usage Run 'Demo_CRC.m'.

upscaling = 2; % the magnification factor x2, x3, x4...

input_dir = 'Set10Dong'; % Set5, Set14, Set10Dong, SetSRF

pattern = '*.bmp'; % Pattern to process