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

esjo93 / ntire_2020_dehazing_challenge