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This repository for DBDN in the following paper

The code is built on EDSR Torch7, under Ubuntu16.04 cuDNN environment with Titan X GPUs


Prepare training data

  1. Download DIV2K training data (800 training +100 validation) from Download Flickr2K training data from
  2. Place HR images in 'code/Prepare_TrainData/DIV2K/DIV2K_HR' and 'code/Prepare_TrainData/Flickr2K/Flickr2K_HR'
  3. Run 'Prepare_TrainData_HR_LR_DIV2K.m' and 'Prepare_TrainData_HR_LR_Flickr2K.m' in matlab to generate LR images
  4. cd code/Prepare_TrainData and Run "th png_to_t7.lua' to convert .png images to .t7 files in new folder 'DIV2K_decoded' and 'Flickr2K_decoded'

Begin train

  1. Download models for the paper from "" and shore them into 'experiment/model/'
  2. cd 'code', use the scripts in file '' to train the models


  1. Download models for the paper from "" and shore them into 'test/model'
  2. Prepare test data, Run 'Prepare_TestData_HR_LR' in Matlab to generate HR/LR images with different degradation models.
  3. Run'Test.lua' based on the code in the 'Test_scripts'.
  4. Run' Evaluation_PSNR_SSIM.m' to obtain PSNR/SSIM results.


if you find the code helpful in your research or work, please cite the following papers.

  title={Deep Bi-Dense Networks for Image Super-Resolution},
  author={Wang, Yucheng and Shen, Jialiang and Zhang, Jian},
  journal={arXiv preprint arXiv:1810.04873},
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