FSRCNN-Tensorflow
TensorFlow implementation of the Fast Super-Resolution Convolutional Neural Network (FSRCNN). This implements two models, FSRCNN which is more accurate and FSRCNN-s which is faster (approaches real-time performance). Based on this project.
Prerequisites
- TensorFlow
- Scipy version > 0.18 ('mode' option from scipy.misc.imread function)
- h5py
- PIL
Usage
For training , python main.py
Can specify epochs, learning rate, python main.py --epochs 10
For testing, python main.py --is_train False
To use FSCRNN-s over FSCRNN , python main.py --fast True
Includes script expand_data.py which scales and rotates all the images in your training set to expand your dataset just like in the paper
python expand_data.py Train
Result
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:
