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Implementation of FSRCNN model inAccelerating the Super-Resolution Convolutional Neural Network paper with Tensorflow 2x.

Pytorch version:https://github.com/Nhat-Thanh/FSRCNN-Pytorch

I used Adam with optimize tuned hyperparameters instead of SGD + Momentum.

I implemented 3 models, FSRCNN-x2, FSRCNN-x3, FSRCNN-x4.

• Train
• Test
• Demo
• Evaluate
• References

You run this command to begin the training:

python train.py  --steps=200000             \                 --scale=2                  \                 --batch_size=128           \                 --save-best-only=0         \                 --save-every=1000          \                 --save-log=0               \                 --ckpt-dir="checkpoint/x2"

• --save-best-only: if it's equal to0, model weights will be saved everysave-every steps.
• --save-log: if it's equal to1,train loss, train metric, validation loss, validation metric will be saved everysave-every steps.

NOTE: if you want to re-train a new model, you should delete all files in sub-directories incheckpoint directory. Your checkpoint will be saved when above command finishs and can be used for the next times, so you can train a model on Google Colab without taking care of GPU time limit.

I trained 3 models on Google Colab in 200000 steps:

You can get the models here:

• FSRCNN-x2.h5
• FSRCNN-x3.h5
• FSRCNN-x4.h5

I useSet5 as the test set. After Training, you can test models with scale factorsx2, x3, x4, the result is calculated by compute average PSNR of all images.

python test.py --scale=2 --ckpt-path="default"

• --ckpt-path="default" means you are using default model path, akacheckpoint/x{scale}/FSRCNN-x{scale}.h5. If you want to use your trained model, you can pass yours to--ckpt-path.

After Training, you can test models with this command, the result is thesr.png.

python demo.py --image-path="dataset/test2.png" \               --ckpt-path="default"            \               --scale=2

• --ckpt-path is the same as inTest

I evaluated models with Set5, Set14, BSD100 and Urban100 dataset by PSNR. I use Set5's Butterfly to show my result:

• Accelerating the Super-Resolution Convolutional Neural Network:https://arxiv.org/abs/1608.00367
• T91, General100:http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_training_datasets.zip
• Set5:https://filebox.ece.vt.edu/~jbhuang/project/selfexsr/Set5_SR.zip
• Set14:https://filebox.ece.vt.edu/~jbhuang/project/selfexsr/Set14_SR.zip
• BSD100:https://filebox.ece.vt.edu/~jbhuang/project/selfexsr/BSD100_SR.zip
• Urban100:https://filebox.ece.vt.edu/~jbhuang/project/selfexsr/Urban100_SR.zip