Awesome

USLN

USLN: A statistically guided lightweight network for underwater image enhancement via dual-statistic white balance and multi-color space stretch

Overview

<div align=center><img src="imgs/overview.png"></div> Our model consists of dual-statistic white balance module, multi-color space stretch module and residual-enhancement modules. The input of USLN is three-dimensional underwater image in which the pixel values is between 0 and 1. ‘convolutional layer’ has the kernel of size 3 × 3 and stride 1, which is used to merge enhanced images together.

Performance

Extensive experiments show that USLN significantly reduces the required network capacity (over 98%) and achieves state-of-the-art performance.

Requirement

python 3.9, pytorch 1.10.1

Train and Test

if you want to train the model:
1, put your datasets into corresponding folders ("images_train", "labels_train", "images_val", "labels_val")
2, run train.py
3, the checkpoints will be saved in "logs"

if you want to test the model:
1, put your datasets into "images_test"
2, run test.py (load model checkpoints from "logs" first)
3, the result will be saved in "pred"

Bibtex

@misc{USLN,
  doi = {10.48550/ARXIV.2209.02221},
  url = {https://arxiv.org/abs/2209.02221}, 
  author = {Xiao, Ziyuan and Han, Yina and Rahardja, Susanto and Ma, Yuanliang},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS:   Computer and information sciences},
  title = {USLN: A statistically guided lightweight network for underwater image enhancement via dual-statistic white balance and multi-color space stretch},
  publisher = {arXiv},
  year = {2022}, 
  copyright = {arXiv.org perpetual, non-exclusive license}
}

License

The code is made available for academic research purpose only. This project is open sourced under MIT license.

Contact

If you have any questions, please contact Ziyuan Xiao at xiaoziyuan@mail.nwpu.edu.cn.