TensorFlow implementation of the Fast Super-Resolution Convolutional Neural Network (FSRCNN). This implements two models: FSRCNN which is more accurate but slower and FSRCNN-s which is faster but less accurate. Based on this project.

• Python 3
• TensorFlow-gpu >= 1.8
• CUDA & cuDNN >= 6.0
• Pillow
• ImageMagick (optional)
• Wand (optional)

For training: python main.py
For testing: python main.py --train False

To use FSCRNN-s instead of FSCRNN: python main.py --fast True

Can specify epochs, learning rate, data directory, etc:
python main.py --epoch 100 --learning_rate 0.0002 --data_dir Train
Check main.py for all the possible flags

Original butterfly image:

Ewa_lanczos interpolated image:

Super-resolved image:

• General-100

• Add RGB support (Increase each layer depth to 3)

• tegg89/SRCNN-Tensorflow

• liliumao/Tensorflow-srcnn

• carpedm20/DCGAN-tensorflow