ISSR

This is a Tensorflow implement of ISSR

Integrating Semantic Segmentation and Retinex Model for Low Light Image Enhancement. In ACMMM'20
Minhao Fan, Wenjing Wang, Wenhan Yang, Jiaying Liu.

Project Page & Dataset

Requirements

1. Python >= 3.5.0
2. Tensorflow >= 1.9.0
3. numpy, PIL An available config of env for conda is in the environment.yaml

Testing Usage

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

python main.py --use_gpu=1 --gpu_idx=0 --gpu_mem=0.5 --phase=test --test_dir=/path/to/your/test/dir/ --save_dir=/path/to/save/results/ --decom=0

Training Usage

First, download train/val/test data set from our project page and unzip the files. You can organize your dataset structure and modify the corresponding part in main.py. Run

python main.py --use_gpu=1 --gpu_idx=0 --gpu_mem=0.5 --phase=train \
    --epoch=100 --batch_size=10 --patch_size=48 --start_lr=0.001 --eval_every_epoch=20 \
    --checkpoint_dir=./ckpts --sample_dir=./sample

Tips:

1. The enhancement performance is highly dependent on training parameters. So if you change the default parameters, you might get some weird results.

Citation

@inproceedings{FanWY020,
 author    = {Minhao Fan and
              Wenjing Wang and
              Wenhan Yang and
              Jiaying Liu},
 title     = {Integrating Semantic Segmentation and Retinex Model for Low-Light
              Image Enhancement},
 booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia, Virtual
              Event / Seattle, WA, USA, October 12-16, 2020},
 pages     = {2317--2325},
 publisher = {{ACM}},
 year      = {2020},
}