Low Light Face Image Enhancement

For advanced CV project in University of Houston. Goals:

1. reimplement the state-of-art paper "Deep Retinex Decomposition for Low-Light Enhancement".
2. test it on acquired low light faces.
3. run a pretrained face&landmark detection model to test improvement on low light faces.

Here is the paper website: https://daooshee.github.io/BMVC2018website/
The author also has published their code and dataset. You can find it on above website.

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Documents

Final presentation
Final report
lowlight.ipynb: implementation of Retinex-Net
Traditional_enhance.ipynb: implemenation of Gamma enahncement
landmark.ipynb: face and landmark deteciton

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Dependency

This work is done by tensorflow 1.10 + Python3.6.6
Training can be finished in 1 hour, with a GTX1060(6G RAM).

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Network architecture

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Result for image decomposition

{low Light, normal light, reflectance, illumination}

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Result for image enhancement