README.md

Laplacian encoder-decoder network for raindrop removal

Official implementation of the paper "Laplacian encoder-decoder network for raindrop removal".

Written in Python 3.8.10 and Pytorch v1.9.1

Getting Started

Requirements

Requirements are stored in the requirements.txt file.

- matplotlib==3.3.3
- numpy==1.19.4
- opencv-python==4.4.0.46
- Pillow==8.0.1
- scikit-image==0.18.0
- scipy==1.5.4
- torch==1.9.1
- torchvision==0.10.1

Installation using python3-pip

pip3 install -r requirements.txt

Training

Running the reaining

To train the model, run the main.py inside the source directory, passing the training options.

E.g.

savedir='./trained_models/LAPNN_exp1'
images_dir='./dataset/'

python3 ./source/main.py  \
--threads 8 \
--batch_size 16 \
--lr 2e-5 \
--save_dir $savedir \
--images_dir $images_dir \
--augm \
--device cuda:0

To see the available options run:

python3 ./source/main.py  --h

Training dataset configurarion

The dataloader expects a dataset structure as

Dataset
├── train
|   ├── data
|   |   ├──img01.png
|   |   ...
|   |   └──img#N.png
|   └── gt
|       ├──img01.png
|       ...
|       └──img#N.png
└── validation
    ├── data
    |   ├──img01.png
    |   ...
    |   └──img#N.png
    └── gt
        ├──img01.png
        ...
        └──img#N.png

where the data folder contains the images to be processed and the gt folder contains the target for loss evaluation.

Inference

Testset configuration

Pretrained model weights and optimizer weight are stored in the ./weights/ folder.

To evaluate a trained model use the test.py script, which loads a set of images and performs inference and evaluation with PSNR and SSIM indexes. Please note that this mode expects to have a dataset with input and ground truth.

E.g.

model='./weights/netLAP_v4_1.pth'

python3 ./source/test.py \
--input_dir ./test/data/ \
--gt_dir ./test/gt/ \
--output_dir ./tests/LAPNN_v4_1/ \
--model $model \
--device cuda:0

Running the code

To run the code in inference, use enhance_dir.py script to run it on a directory containing multiple images.

E.g.

model='./weights/netLAP_v4_1_BASELINE.pth'

python3 ./source/enhance_dir.py \
--input_dir ./data/ \
--output_dir ./out/ \
--model $model \
--device cuda:0

Cite

Please, if you use the code provided in this repository, cite our original work:

@article{zini2022laplacian,
  title={Laplacian encoder-decoder network for raindrop removal},
  author={Zini, Simone and Buzzelli, Marco},
  journal={Pattern Recognition Letters},
  volume={158},
  pages={24--33},
  year={2022},
  publisher={Elsevier}
}