# Exclusively-Dark-Image-Dataset

**Repository Path**: lgcgithub/Exclusively-Dark-Image-Dataset

## Basic Information

- **Project Name**: Exclusively-Dark-Image-Dataset
- **Description**: Exclusively Dark (ExDARK) dataset which to the best of our knowledge, is the largest collection of low-light images taken in very low-light environments to twilight (i.e 10 different conditions) to-date with image class and object level annotations.
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2020-05-10
- **Last Updated**: 2023-05-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Exclusively Dark (ExDark) Image Dataset (Official Site)
[ArXiv](http://cs-chan.com/doc/cviu.pdf)

Updated on June 02, 2019 (Code for low-light image enhancement is released)

Updated on Oct. 31, 2018 (Accepted for publication in CVIU)

Released on May 29, 2018

## Description

In order to facilitate a new object detection and image enhancement research particularly in the low-light environment, we introduce the Exclusively Dark (ExDark) dataset [(CVIU2019)](http://cs-chan.com/doc/cviu.pdf). The Exclusively Dark (ExDARK) dataset is a collection of 7,363 low-light images from very low-light environments to twilight (i.e 10 different conditions) with 12 object classes (similar to PASCAL VOC) annotated on both image class level and local object bounding boxes. 

![demo](Exdark.gif)

## Source Code 
The source code for our work on low-light image enhancement is now available. Please refer to the [SPIC](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/tree/master/SPIC) folder.

<img src="SPIC/2015_00003.gif" height="150" > <img src="SPIC/2015_02446.gif" height="150" > <img src="SPIC/2015_06400.gif" height="150" >


## Citation
If you find this dataset useful for your research, please cite
```
@article{Exdark,
  title={Getting to Know Low-light Images with The Exclusively Dark Dataset},
  author={Loh, Yuen Peng and Chan, Chee Seng},
  journal={Computer Vision and Image Understanding},
  volume={178},
  pages={30-42},
  year={2019},
  doi={https://doi.org/10.1016/j.cviu.2018.10.010}
}
```

## Feedback
Suggestions and opinions on this dataset (both positive and negative) are greatly welcomed. Please contact the authors by sending an email to
`lexloh2009 at hotmail.com`or `cs.chan at um.edu.my`.

## License and Copyright
The project is open source under BSD-3 license (see the ``` LICENSE ``` file). 

For commercial purpose usage, please contact Dr. Chee Seng Chan at `cs.chan at um.edu.my`

&#169;2018-2019 Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, University of Malaya.