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

arXiv: https://arxiv.org/abs/2209.02221 · HTML: https://mlsanity.com/paper/2209.02221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception](https://mlsanity.com/paper/2002.01155/) (2020)
- [Shallow-UWnet : Compressed Model for Underwater Image Enhancement](https://mlsanity.com/paper/2101.02073/) (2021)

## Built on by (most cited first, 1 of 1)

- [Adaptive deep learning framework for robust unsupervised underwater image enhancement](https://mlsanity.com/paper/2212.08983/) (2022)

## Abstract (arXiv)

Underwater images are inevitably affected by color distortion and reduced contrast. Traditional statistic-based methods such as white balance and histogram stretching attempted to adjust the imbalance of color channels and narrow distribution of intensities a priori thus with limited performance. Recently, deep-learning-based methods have achieved encouraging results. However, the involved complicate architecture and high computational costs may hinder their deployment in practical constrained platforms. Inspired by above works, we propose a statistically guided lightweight underwater image enhancement network (USLN). Concretely, we first develop a dual-statistic white balance module which can learn to use both average and maximum of images to compensate the color distortion for each specific pixel. Then this is followed by a multi-color space stretch module to adjust the histogram distribution in RGB, HSI, and Lab color spaces adaptively. Extensive experiments show that, with the guidance of statistics, USLN significantly reduces the required network capacity (over98%) and achieves state-of-the-art performance. The code and relevant resources are available at https://github.com/deepxzy/USLN.