image super resolution

enviornment

tensorflow
ubuntu 18.04

data

create a dir name data and put the training data in here.
If you want to generate more data for data augmentation, run python3 augmentation.py --dataset="your dataset dir" --augment_level="you can choose 4/8".

train

First, go to arg.py to set the parameter you want, like scale, lr, number of filter... and start training.

inference

After training, you must use the same parameter to test the model, run python3 sr.py --file="your image path" and you can get upscale image for the low resolution image in the output dir.
It will genertate 6 images for each lr image, the image named result is the upscale one.

result

low resolution image

high resolution image (x3)

reference

https://github.com/jiny2001/dcscn-super-resolution