LANet

Our work proposes an adaptive learning attention network (LANet) to solve the problem of color casts and low illumination in underwater images.

Dependencies

The code runs with Python=3.6 and requires Pytorch of version 1.7 or higher. Please pip install the following packages:

numpy=1.20.2
torchvision=0.8.0
matplotlib=3.4.2
opencv-python=4.5.2.54
scipy=1.7.0

Training

1. Download the code
2. run Python train.py --input_images-path ./data/trainA/ --label_images_path ./data/trainB/ 
3. Find checkpoint in the "./checkpoints/" folder
The training data includes input data and label data. input data are in the "./data/trainA" folder, label data are in the "./data/trainB" folder

Testing

1. pre-trained models in the "./checkpoints/" folder
2. Put your testing images in the "./data/test/" folder 
3. run Python test.py --test_pth ./data/test/ --snapshot_pth ./checkpoints/model_epoch_40.pk
4. Find the result in "./results" folder

Contact

If you have any questions, please contact Shiben Liu at [email protected].

Underwater image enhancement

Related tags

Overview

LANet

Dependencies

Training

Testing

Contact

Owner

LiuShiBen

IEEE Winter Conference on Applications of Computer Vision 2022 Accepted

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