| # Low-light Image Enhancement |
| [ArXiv](http://cs-chan.com/doc/SPIC2019.pdf) |
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| ### Official Matlab implementation of the paper: "Low-light Image Enhancement using Gaussian Process for Features Retrieval" |
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| #### Signal Processing: Image Communication 2019 |
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| Released on June 1, 2019 |
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| ## Description |
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| 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. |
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| <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" > |
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| ## Citation |
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| 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} |
| } |
| ``` |
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| ## Dependencies |
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| 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. |
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| ## Installation and Running |
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| 1. Extract GPR_v1.1.zip |
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| 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/)) |
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| 3. Run demo.m |
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| ## 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` |
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