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[CVPRW 2023] SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing

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# <p align=center> [CVPRW 2023] SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing</p>

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[![Paper](https://img.shields.io/badge/SCANet-Paper-blue.svg)](https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/html/Guo_SCANet_Self-Paced_Semi-Curricular_Attention_Network_for_Non-Homogeneous_Image_Dehazing_CVPRW_2023_paper.html)
[![ArXiv](https://img.shields.io/badge/SCANet-ArXiv-red.svg)](http://arxiv.org/abs/2304.08444)
[![Poster](https://img.shields.io/badge/SCANet-Poster-green.svg)](https://github.com/gy65896/SCANet/blob/main/poster/SCANet_poster.png)
[![Video](https://img.shields.io/badge/SCANet-Video-orange.svg)](https://drive.google.com/file/d/1KsfrAPUKTZR2QPqO9X8QDdmyl9AHJC7t/view)

</div>

---
>**SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing**<br>
>[Yu Guo](https://scholar.google.com/citations?user=klYz-acAAAAJ&hl=zh-CN), [Yuan Gao](https://scholar.google.com.hk/citations?user=4JpRnU4AAAAJ&hl=zh-CN), [Ryan Wen Liu<sup>*</sup>](http://mipc.whut.edu.cn/index.html), [Yuxu Lu](https://scholar.google.com/citations?user=XXge2_0AAAAJ&hl=zh-CN), [Jingxiang Qu](https://scholar.google.com/citations?user=9zK-zGoAAAAJ&hl=zh-CN), [Shengfeng He](http://www.shengfenghe.com/), Wenqi Ren<br>
>(* Corresponding Author)<br>
>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops

> **Abstract:** *The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-occluded regions. Our approach consists of an attention generator network and a scene reconstruction network. We use the luminance differences of images to restrict the attention map and introduce a self-paced semi-curricular learning strategy to reduce learning ambiguity in the early stages of training. Extensive quantitative and qualitative experiments demonstrate that our SCANet outperforms many state-of-the-art methods.*
<hr />

## Requirement

- Python 3.7
- Pytorch 1.9.1

## Network Architecture
![fig_scanet](https://user-images.githubusercontent.com/48637474/232728784-74728cd8-c18e-40b8-a275-1b2ca24a05e7.png)

## Train

* Place the training and test image pairs in the `data` folder.
* Run `data/makedataset.py` to generate the `NH-Haze20-21-23.h5` file.
* Run `train.py` to start training.

## Test

* Place the pre-training weight in the `checkpoint` folder.
* Place test hazy images in the `input` folder.
* Modify the weight name in the `test.py`.<br> 
```
parser.add_argument("--model_name", type=str, default='Gmodel_40', help='model name')
```
* Run `test.py`
* The results are saved in `output` folder.

## Pre-training Weight Download

* The [weight40](https://drive.google.com/file/d/15-M7bGwZkXtCato_kEfLi1VOq-tjblPL/view?usp=share_link) for the NTIRE2023 val/test datasets, i.e., the weight used in the NTIRE2023 challenge.
* The [weight105](https://drive.google.com/file/d/1ATye3j81n62VHXwGihShazYnMoEbTMLd/view?usp=share_link) for the NTIRE2020/2021/2023 datasets.
* The [weight120](https://drive.google.com/file/d/1sC81YfqOa82irk_Dy37I9oxpX4zniS2z/view?usp=share_link) for the NTIRE2020/2021/2023 datasets (Add the 15 tested images as the training dataset).

## Citation

```
@inproceedings{guo2023scanet,
  title={SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing},
  author={Guo, Yu and Gao, Yuan and Liu, Wen and Lu, Yuxu and Qu, Jingxiang and He, Shengfeng and Ren, Wenqi},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={1884--1893},
  year={2023}
}
```

#### If you have any questions, please get in touch with me (guoyu65896@gmail.com).

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