FSRCNN-TensorFlow

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.

Prerequisites

• Python 2.7
• TensorFlow
• Scipy version > 0.18
• h5py
• PIL

Usage

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 --epochs 10 --learning_rate 0.0001 --data_dir Train
Check main.py for all the possible flags

Also includes script expand_data.py which scales and rotates all the images in the specified training set to expand it

Result

Original butterfly image:

Bicubic interpolated image:

Super-resolved image:

TODO

• Add RGB support (Increase each layer depth to 3)
• Speed up pre-processing for large datasets
• Set learning rate for deconvolutional layer to 1e-4 (vs 1e-3 for the rest)

References

• tegg89/SRCNN-Tensorflow

• liliumao/Tensorflow-srcnn

• carpedm20/DCGAN-tensorflow