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Dec 3, 2019

MBLLEN

This is a Keras implementation of “MBLLEN: Low-light Image/Video Enhancement Using CNNs” in BMVC 2018, by Feifan Lv, Feng Lu, Jianhua Wu and Chongsoon Lim. This page provides more experiments on real low-light images compared with latest methods.

Paper and Project page

Requirements

  • python 3
  • Tensorflow 1.6.0
  • Keras 2.2.0
  • Opencv-python 3.4.2

Usage

Testing

To quickly test your own low-light images with our model, you can just run through

cd main
python test.py -i <input folder> -r <output folder> -m <model name>

By default, the code takes the data in the "../input/" folder, loads the "Syn_img_lowlight_withnoise.h5" model and saves results in the "../result/" folder. Please read the code to see other parameter settings.

Training:

First, prepare your own dataset or download our synthetic low-light dataset from our Project page. Second, change the load images path of "train.py" and "data_load.py". Then, you can just run through

cd main
python train.py

By default, the code takes the data in the "../dataset/" folder and save weights in the "./models/" folder. Please read the code to see other parameter settings.

Model

  • Syn_img_lowlight_withnoise.h5 (This model is trained using synthetic lowlight images based on Poisson noise model. It is using for enhancement and denoising simultaneously.)
  • Syn_img_lowlight.h5 (This model is trained using synthetic lowlight images without additional noise. It is using only for enhancement.)
  • LOL_img_lowlight.h5 (This model is fine-tuned using LOL Dataset.)

Performance on Real Lowlight Images

To obtain better enhancement result, we linearly amplify the output of the network to improve contrast. Please read the code to see other parameter settings.

Performance on LOL Dataset

Our LOL fine-tuned version performs well on LOL test images.

Performance on DeepUPE Dataset

Our model is comparable with DeepUPE. Notice that, our models are not fine-tuned using DeepUPE's images (training images are not provided).

Bibtex

If you use this code for your research, please cite our paper.

@inproceedings{Lv2018MBLLEN,
 title={MBLLEN: Low-light Image/Video Enhancement Using CNNs},
 author={Feifan Lv, Feng Lu, Jianhua Wu, Chongsoon Lim},
 booktitle={British Machine Vision Conference (BMVC)},
 year={2018}
}

Related Follow-Up Work

Feifan Lv, Yu Li and Feng Lu. Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset. IJCV, 2021. Paper and Project page

About

Code for “MBLLEN: Low-light Image/Video Enhancement Using CNNs”, BMVC 2018.

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