Awesome
DLN (Lighting Network for Low-Light Image Enhancement)
By Li-Wen Wang, Zhi-Song Liu, Wan-Chi Siu, and Daniel P. K. Lun
This repo provides simple testing codes, pretrained models and the network strategy demo.
We propose a single image low-light enhancement method based on back-projection theory and attention mechanism. to achieve good enhancing performance.
BibTex
@ARTICLE{DLN2020,
author={Li-Wen Wang and Zhi-Song Liu and Wan-Chi Siu and Daniel P.K. Lun},
journal={IEEE Transactions on Image Processing},
title={Lightening Network for Low-light Image Enhancement},
year={2020},
doi={10.1109/TIP.2020.3008396},
}
Complete Architecture
The complete architecture of Deep Lighten Network (DLN) is shown as follows, The rectangles and cubes denote the operations and feature maps respectively.
#Implementation
Prerequisites
- Python 3.5
- NVIDIA GPU + CUDA
- [optional] sacred+ mongodb (experiment control)
Getting Started
Installation
- Install PyTorch and dependencies from http://pytorch.org
- Install python libraries:
pip install pillow, opencv-python, scikit-image, sacred, pymongo
- Clone this repo
Testing
- A few example test images are included in the
./test_img
folder. - Please download trained model
- Test the model by:
python test.py --modelfile models/DLN_pretrained.pth
# or if the task towards real low-light image enhancement
python test.py --modelfile models/DLN_finetune_LOL.pth
The test results will be saved to the folder: ./output
.
Dataset
- Download the VOC2007 dataset and put it to "datasets/VOC2007/".
- Download the LOL dataset and put it to "datasets/LOL".
Training
It needs to manually switch the training dataset:
- first, train from the synthesized dataset,
- then, load the pretrained model and train from the real dataset
python train.py
Quantitative Comparison
We tested the proposed method on the LOL real dataset for evaluation. We have achieve better performance.
Visual Comparison
At LOL dataset: