SRCNN in Tensorflow

Tensorflow implementation of Image Super-Resolution Using Deep Convolutional Networks.

GT	Bicubic	SRCNN

Implementation Details

Our implementation used TensorFlow to train SRCNN. We used almost same methods as described in the paper. We trained the network with 91-image dataset and validated with Set5 dataset while training. At test time, to get the same result size for up-scaling factor 2, 3 and 4, we cropped the test image with 12, which is the Least Common Multiple. Also, we padded border of the ground truth and bicubic interpolated test image by 6 to make same size with SRCNN result.

According to the paper, the best performance on Set5 with upscaling factor 3 is the average PSNR value of 32.75dB with filter size 9-5-5 and ImageNet training dataset, but we were aim to 32.39dB which is the demonstrated average PSNR value when the model is trained with 91-image dataset, 9-1-5 filter size and Y only. After training 12,500 epoch, we got the same value to the paper, 32.39dB.

Pretrained-model with 91-image training dataset and up-scaling factor 3 is given.

Note that we trained and tested with Y-channel. If you want to train and test with 3-channels (YCbCr or RGB), you may add or implement some type-casting code.

Installation

git clone https://github.com/jinsuyoo/SRCNN-Tensorflow.git

Requirements

You will need the following to run the above:

• Tensorflow-gpu
• Python3, Numpy, Pillow, h5py, tqdm

To install quickly, use requirements.txt. Example usage:

pip install -r requirements.txt

Note that we run the code with Windows 10, Tensorflow-gpu 1.13.1, CUDA 10.0, cuDNN v7.6.0

Documentation

To pre-process the train and test dataset, you need to execute the Matlab code.

For those of who cannot execute the Matlab code,

Click here to download the pre-processed training data with 91 dataset. Put the file under SRCNN-Tensorflow directory.

The pre-processed test data with Set5 and Set14 is provided.

Training SRCNN

Use main.py to train the network. Run python main.py to view the training process. Training takes 7-8 hours on a NVIDIA GeForce GTX 1050. Example usage:

# Quick training
python main.py

# Example usage
python main.py --use_pretrained=False \
    --epoch=1000 \
    --scale=4 \

Testing SRCNN

Also use main.py to test the network. Pretrained-model with 91-image training dataset and up-scaling factor 3 is given. Example usage:

# Quick testing
python main.py --is_training=False \
    --use_pretrained=True

# Example usage
python main.py --is_training=False \
    --use_pretrained=True \
    --test_dataset=YOUR_DATASET \
    --scale=4

Please note that if you want to train or test with your own dataset, you need to execute the Matlab code with your own dataset first :)

Results

The average results of PSNR (dB) trained with up-scale factor 3

Code	Dataset	Scale	Bicubic	SRCNN
SRCNN	Set5	3x	30.39dB	32.39dB
SRCNN-Tensorflow	Set5	3x	30.38dB	32.39dB

Some of the result images

GT	Bicubic	SRCNN

References

• Official Website

  • We referred to the original Matlab and Caffe code.

• tegg89/SRCNN-Tensorflow

  • We highly followed the structure of this repository.