Welcome! This is the official implementation of our paper: Implicit Neural Representation for Cooperative Low-light Image Enhancement
Authors: Shuzhou Yang, Moxuan Ding, Yanmin Wu, Zihan Li, Jian Zhang*.
- (2023.7.17): Our code has been released❗️
- (2023.7.14): 🎉🎉🎉 Our paper has been accepted to ICCV 2023❗️
- Linux or macOS
- Python 3.8
- NVIDIA GPU + CUDA CuDNN
Type the command:
pip install -r requirements.txt
You need create a directory ./saves/[YOUR-MODEL]
(e.g., ./saves/LSRW
).
Download the pre-trained models and put them into ./saves/[YOUR-MODEL]
.
Here we release two versions of the pre-trained model, which are trained on LSRW and LOL datasets respectively:
- Create directories
./dataset/testA
and./dataset/testB
. Put your test images in./dataset/testA
(And you should keep whatever one image in./dataset/testB
to make sure program can start.) - Test the model with the pre-trained weights:
CUDA_VISIBLE_DEVICES=0 python test.py --dataroot ./dataset --name [YOUR-MODEL] --preprocess=none
- The test results will be saved to a directory here:
./results/[YOUR-MODEL]/test_latest/images
, and will also be displayed in an html file here:./results/[YOUR-MODEL]/test_latest/index.html
.
- Download training low-light data and put it in
./dataset/trainA
. - Randomly adopt hundreds of normal-light images and put them in
./dataset/trainB
. - Train a model:
cd NeRCo-main
mkdir loss
CUDA_VISIBLE_DEVICES=0 python train.py --dataroot ./dataset --name [YOUR-MODEL]
- Loss curve can be found in the directory
./loss
. - To see more intermediate results, check out
./saves/[YOUR-MODEL]/web/index.html
.
If you find this code useful for your research, please use the following BibTeX entry.
@InProceedings{Yang_2023_ICCV,
author = {Yang, Shuzhou and Ding, Moxuan and Wu, Yanmin and Li, Zihan and Zhang, Jian},
title = {Implicit Neural Representation for Cooperative Low-light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {12918-12927}
}