Overview

Abstract: In recent years, as computer vision tasks have increasingly relied on high-quality image inputs, the task of image dehazing has received significant attention. Previously, many methods based on priors and deep learning have been proposed to address the task of image dehazing. Ignoring the domain gap between different data, former de-hazing methods usually adopt multiple datasets for explicit training, which often makes the methods themselves be violated. To address this problem, we propose a novel method of internal and external data augmen- tation to improve the existing dehazing methodology. By using cross-data external augmentor. The dataset inherits samples from different domains that are firmly aligned, making the model learn more robust and generalizable features. By using the internal data augmentation method, the model can fully exploit local information within the images, thereby obtaining more image details. To demonstrate the effectiveness of our proposed method, we conduct training on both the Natural Image Dataset (NID) and the Remote Sensing Image Dataset (RSID). Experimental results show that our method clearly resolves the domain gap in different dehazing datasets and presents a new pipeline for joint training in the dehazing task. Our approach significantly outperforms other advanced methods in dehazing and produces dehazed images that are closest to real haze-free images.

Visual

Preparation

Install

We test the code on PyTorch 1.10.2 + CUDA 11.3.

1. Create a new conda environment

conda create -n scaleupdehazing python=3.7
conda activate scaleupdehazing

2. Install dependencies

conda install pytorch=1.10.2 torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Download

You can download the pretrained models on [Baidu Drive]

You can download the dataset on [trinity]

The file path should be the same as the following:

┬─ save_models
│   ├─ nid
│   │   ├─ scaleupdehazing.pth
│   │   └─ ... (model name)
│   └─ ... (exp name)
└─ data
    ├─ NID
    │   ├─ train
    │   │   ├─ GT
    │   │   │   └─ ... (image filename)
    │   │   └─ hazy
    │   │       └─ ... (corresponds to the former)
    │   └─ test
    │       └─ ...
    └─ ... (dataset name)

Train

You can obtain the images after channel transfer:

python ct.py

You can modify the training settings for each experiment in the configs folder. Then run the following script to train the model:

python trainct.py --model scaleupdehazing --dataset NID --exp nid

Then you have obtained an initial pre-trained model. We will continue to perform self-supervised training on the obtained model.

python trainssl.py --model scaleupdehazing --dataset NID --exp nid

Test

Run the following script to test the trained model:

python test.py --model scaleupdehazing --dataset NID --exp nid

Acknowledgement

This project is build based on Dehazeformer. We thank the authors for sharing their code.