# Low-light Image Enhancement
[ArXiv](http://cs-chan.com/doc/SPIC2019.pdf)

### Official Matlab implementation of the paper: "Low-light Image Enhancement using Gaussian Process for Features Retrieval"

#### Signal Processing: Image Communication 2019

Released on June 1, 2019

## Description

In this work, we propose to model low-light enhancement as a set of localized functions using Gaussian Process that is trained at runtime using data from a simple Convolutional Neural Network (CNN) to provide the necessary feature information as reference. The CNN is in turn trained using large amount of synthetic data,
based upon the luminance distribution of real world low-light images to learn the relationship between features and pixels. 

<img src="SPIC.gif" height="200" > <img src="2015_00003.gif" height="150" > <img src="2015_02446.gif" height="150" > <img src="2015_06400.gif" height="150" >



## Citation

Please cite the following paper if you use this repository in your reseach:
```bibtex
@article{loh2019low,
  title = {Low-light image enhancement using Gaussian Process for features retrieval},
  author = {Loh, Yuen Peng and Liang, Xuefeng and Chan, Chee Seng},
  journal = {Signal Processing: Image Communication},
  volume = {74},
  pages = {175--190},
  year = {2019},
  publisher = {Elsevier}
}
```

## Dependencies

The codes are implemented in MATLAB using the prior version of [MatConvNet](http://www.vlfeat.org/matconvnet/) and the [Gaussian Process for Machine Learning](http://www.gaussianprocess.org/gpml/code/matlab/doc/index.html) toolboxes.

## Installation and Running

1. Extract GPR_v1.1.zip

2. Extract matconvnet-1.0-beta20.tar.gz
   - run vl_compilenn
   - run vl_setupnn
   
   (for problems installing the toolbox, please refer to [MatConvNet](http://www.vlfeat.org/matconvnet/))

3. Run demo.m

## Feedback
Suggestions and opinions of this work (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
The project is open sourced under BSD-3 license (see the ``` LICENSE ``` file). Codes can be used freely only for academic purposes.

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