ICIP2023 | Paper | Bibtex | Poster
(Released on June 28, 2023)
Input Image | Enhancement Process | Output Image |
![]() |
![]() |
![]() |
- We use LOL dataset as training data, which is available in RetinexNet repo
- We use LSRW dataset as testing data, which is available in R2RNet repo
- python 3.10
- pytorch == 1.11.0
- accelerate == 0.12.0
- wandb == 0.12.17 (used in model training)
Download the pretrained model and put it into ./checkpoints
- Download your training dataset
- Execute
train.py
(refertrain.py
to check what parameters/hyperparameters to run with)python train.py --dataset_dir=path/to/your/training/dataset --batch_size=32
-
Download your testing dataset
-
Put your model weight into
./checkpoints
-
Execute
test.py
(refertest.py
to check what parameters/hyperparameters to run with)python test.py --dataset_dir=path/to/your/testing/dataset --model_name=LLDE --timestep_respacing=25
-
The output images are saved in
./saved_images
by default
If you find this work useful for your research, please cite
@article{LLDE,
inproceedings = {LLDE: Enhancing Low-light Images With Diffusion Model},
author = {Ooi, Xin Peng and Chan, Chee Seng},
booktitle = {2023 IEEE international conference on image processing (ICIP)},
year = {2023}
}
Suggestions and opinions on this work (both positive and negative) are greatly welcomed. Please contact the authors by sending an email to
0417oxp at gmail.com
or cs.chan at um.edu.my
.
The project is open source under BSD-3 license (see the LICENSE
file).
©2023 Universiti Malaya.