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% This is the training demo of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
% Version: 1.0 (18/08/2016)
% Contact: Kai Zhang (e-mail:
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% Please consider the following citation if this code is useful to you.
title={Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising},
author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
journal={IEEE Transactions on Image Processing},
% ------ Contents -----------------------------------------------------
----Test (Set12 and Set68)
----Train400 (400 training images of size 180X180)
----GenerateData_model_64_25_Res_Bnorm_Adam.m (run this to generate training patches!)
Demo_Test_model_64_25_Res_Bnorm_Adam.m (test each model)
Demo_Train_model_64_25_Res_Bnorm_Adam.m (run this to train the model)
DnCNN_init_model_64_25_Res_Bnorm_Adam.m (initializate the model)
DnCNN_train.m (the main body of training code)
vl_nnloss.m (loss function)
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% I have tried to make the code as simple as possible. You can change
% 'cnn_train.m' or 'cnn_train_dag.m' in Matconvnet package if necessary.
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% Permission to use, copy, or modify this software and its documentation
% for educational and research purposes only and without fee is here
% granted, provided that this copyright notice and the original authors'
% names appear on all copies and supporting documentation. This program
% shall not be used, rewritten, or adapted as the basis of a commercial
% software or hardware product without first obtaining permission of the
% authors. The authors make no representations about the suitability of
% this software for any purpose. It is provided "as is" without express
% or implied warranty.
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% If you find any bug, please contact
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