NTIRE 2020 NonHomogeneous Dehazing Challenge: UNIST VIP Lab
Introduction
This is our project repository for CVPR 2020 workshop.
"Physical Encoder-Decoder Network for Image Dehazing"
Network Architecture
Obtained R(x) and L(x) are used to make output(clear image) like following:
Dataset Preparation
You can download NTIRE 2020 NonHomogeneous Dehazing Challenge dataset after participating the challenge in the following link: https://competitions.codalab.org/competitions/22236
Your dataset directory should be composed of three directories like following:
dataset_directory
|-- train
| |-- HAZY
| | |-- 01
| | |-- 02
| | `-- ...
| `-- GT
| |-- 01
| |-- 02
| `-- ...
|-- val
| |-- HAZY
| | `-- ...
| `-- GT
| `-- ...
`-- test
`-- HAZY
`-- ...
Train
You can start training your model by following:
$ python main.py train
Additional arguments:
--data-dir: Dataset directory
--batch-size: Training batch size
--epochs: The number of total epochs
--lr: Initial learning rate
--step: Step size for learning rate decay
--weight-decay: Weight decay factor
--crop-size: Random crop size for training
Test
You can test your pretrained model by following:
$ python main.py test -d [data path] --resume [pretrained model path] --phase test --batch-size 1
Download pretrained model: [download]
Results
Metrics | Test Scores (#51~55) |
---|---|
PSNR | 18.77 |
SSIM | 0.54 |
Run time[s] per img. | 0.04 |