Awesome

GLADNet

This is a Tensorflow implantation of GLADNet

GLADNet: Low-Light Enhancement Network with Global Awareness. In FG'18 Workshop FOR-LQ 2018 <br> Wenjing Wang*, Chen Wei*, Wenhan Yang, Jiaying Liu. (* indicates equal contributions)<br>

Paper, Project Page

Requirements

1. Python
2. Tensorflow >= 1.3.0
3. numpy, PIL

Testing Usage

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

python main.py 
    --use_gpu=1 \                           # use gpu or not
    --gpu_idx=0 \
    --gpu_mem=0.5 \                         # gpu memory usage
    --phase=test \
    --test_dir=/path/to/your/test/dir/ \
    --save_dir=/path/to/save/results/ \

Training Usage

First, download training data set from our project page. Save training pairs of our LOL dataset under ./data/train/low/, and synthetic pairs under ./data/train/normal/. Then, start training by

python main.py
    --use_gpu=1 \                           # use gpu or not
    --gpu_idx=0 \
    --gpu_mem=0.8 \                         # gpu memory usage
    --phase=train \
    --epoch=50 \                           # number of training epoches
    --batch_size=8 \
    --patch_size=384 \                       # size of training patches
    --base_lr=0.001 \                      # initial learning rate for adm
    --eval_every_epoch=5 \                 # evaluate and save checkpoints for every # epoches
    --checkpoint_dir=./checkpoint           # if it is not existed, automatically make dirs
    --sample_dir=./sample                   # dir for saving evaluation results during training

Experiment Results

Subjective Results

Objective Results

We use the Naturalness Image Quality Evaluator (NIQE) no-reference image quality score for quantitative comparison. NIQE compares images to a default model computed from images of natural scenes. A smaller score indicates better perceptual quality.

Computer Vision Application

We test several real low-light images and their corresponding enhanced results on Google Cloud Visio API. GLADNet helps it to identify the objects in this image. <br> <br> <br> <br>

Citation

@inproceedings{wang2018gladnet,
 title={GLADNet: Low-Light Enhancement Network with Global Awareness},
 author={Wang, Wenjing and Wei, Chen and Yang, Wenhan and Liu, Jiaying},
 booktitle={Automatic Face \& Gesture Recognition (FG 2018), 2018 13th IEEE International Conference},
 pages={751--755},
 year={2018},
 organization={IEEE}
}

Related Follow-Up Work

Deep Retinex Decomposition: <b>Deep Retinex Decomposition for Low-Light Enhancement</b>. Chen Wei*, Wenjing Wang*, Wenhan Yang, Jiaying Liu. (* indicates equal contributions) In BMVC'18 (Oral Presentation) Website Github