Tensorflow implementation of Convolutional Neural Networks for super-resolution. The original Matlab and Caffe from official website can be found here.

• Tensorflow
• Scipy version > 0.18 ('mode' option from scipy.misc.imread function)
• h5py
• matplotlib

This code requires Tensorflow. Also scipy is used instead of Matlab or OpenCV. Especially, installing OpenCV at Linux is sort of complicated. So, with reproducing this paper, I used scipy instead. For more imformation about scipy, click here.

For training, python main.py
For testing, python main.py --is_train False --stride 21

After training 15,000 epochs, I got similar super-resolved image to reference paper. Training time takes 12 hours 16 minutes and 1.41 seconds. My desktop performance is Intel I7-6700 CPU, GTX970, and 16GB RAM. Result images are shown below.

Original butterfly image:
Bicubic interpolated image:
Super-resolved image:

• liliumao/Tensorflow-srcnn
  • • I referred to this repository which is same implementation using Matlab code and Caffe model.

• carpedm20/DCGAN-tensorflow
  • • I have followed and learned training process and structure of this repository.