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The reproduction of SRCNN method for super-resolution

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The directory structure:

.
├── datasets
|   └── Train
|   └── Test
|       ├── Set5
|       └── Set14
|── configs.py
|── networks.py
|── train.py
|── utils.py
└── README.md
        

training

  1. place the datasets(training:91 images, testing: Set5 and Set14) to datastes directory \
  2. running ultis.py to generate the traning dataset(h5 file)
  3. running train.py to start training the dataset could be obtained from this url(http://mmlab.ie.cuhk.edu.hk/projects/SRCNN/SRCNN_train.zip)

The iteration was set to 400,000,000. In the papper "Learning a Deep Convolutional Network for Image Super-Resolution, in Proceedings of European Conference on Computer Vision the iteration was set 10 1,200,000,000

this version exsist some problems which lead to bad psnr
to be continue...

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  • Python 100.0%