The code is built on DnCNN (PyTorch) and tested on Ubuntu 16.04/18.04 environment (Python3.6, PyTorch_1.3.0, CUDA10.1, cuDNN7.6) with Titan RTX/2080Ti GPUs.
python3
pytorch >= 1.3
torchvision >= 0.4.1.
tensorboardX
hdf5
numpy
opencv
$ python train.py --preprocess True
NOTE
- If you've already built the training and validation dataset (i.e. train.h5 & val.h5 files), set preprocess to be False.
$ python test.py --test_data Set12 --output_size 10 --test_noiseL 25
NOTE
- test_data can be Set12 or Set68 or Urban100.
- output_size can be any number from 1. Please refer to the paper on this part.
- test_noiseL can be 15 or 25 or 50
- pretrained weights for each noise level are provided in logs/
@article{lee2020locally,
title={Locally Adaptive Channel Attention-Based Network for Denoising Images},
author={Lee, Haeyun and Cho, Sunghyun},
journal={IEEE Access},
volume={8},
pages={34686--34695},
year={2020},
publisher={IEEE}
}