# Underwater Image Enhancement via Learning Water Type Desensitized Representations

arXiv: https://arxiv.org/abs/2102.00676 · HTML: https://mlsanity.com/paper/2102.00676/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [Switchable Whitening for Deep Representation Learning](https://mlsanity.com/paper/1904.09739/) (2019)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer](https://mlsanity.com/paper/1710.07084/) (2017)
- [All-In-One Underwater Image Enhancement using Domain-Adversarial Learning](https://mlsanity.com/paper/1905.13342/) (2019)
- [Positional Normalization](https://mlsanity.com/paper/1907.04312/) (2019)
- [Deep Underwater Image Enhancement](https://mlsanity.com/paper/1807.03528/) (2018)

## Built on by (most cited first, 9 of 9)

- [Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration](https://mlsanity.com/paper/2311.16845/) (2023)
- [Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement](https://mlsanity.com/paper/2309.04089/) (2023)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration](https://mlsanity.com/paper/2210.01069/) (2022)
- [Global Modeling Matters: A Fast, Lightweight and Effective Baseline for Efficient Image Restoration](https://mlsanity.com/paper/2507.13663/) (2025)
- [Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement](https://mlsanity.com/paper/2404.17883/) (2024)
- [SINET: Sparsity-driven Interpretable Neural Network for Underwater Image Enhancement](https://mlsanity.com/paper/2409.01022/) (2024)
- [A Correction-Based Dynamic Enhancement Framework towards Underwater Detection](https://mlsanity.com/paper/2302.02553/) (2023)
- [Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models](https://mlsanity.com/paper/2409.06420/) (2024)

## Abstract (arXiv)

We present a novel underwater image enhancement method termed SCNet to improve the image quality meanwhile cope with the degradation diversity caused by the water. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Specifically, we apply whitening to de-correlate activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such water type irrelevant encodings, the decoder can easily reconstruct the clean signal and be unaffected by the distortion types. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.