# UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space

arXiv: https://arxiv.org/abs/2103.07138 · HTML: https://mlsanity.com/paper/2103.07138/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 59 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [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)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [Feedback Network for Image Super-Resolution](https://mlsanity.com/paper/1903.09814/) (2019)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [DeepISP: Towards Learning an End-to-End Image Processing Pipeline](https://mlsanity.com/paper/1801.06724/) (2018)
- [Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/1906.08462/) (2019)
- [RGB-D Salient Object Detection with Cross-Modality Modulation and Selection](https://mlsanity.com/paper/2007.07051/) (2020)
- [Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer](https://mlsanity.com/paper/1710.07084/) (2017)
- [Diving Deeper into Underwater Image Enhancement: A Survey](https://mlsanity.com/paper/1907.07863/) (2019)
- [All-In-One Underwater Image Enhancement using Domain-Adversarial Learning](https://mlsanity.com/paper/1905.13342/) (2019)
- [Deep Underwater Image Enhancement](https://mlsanity.com/paper/1807.03528/) (2018)
- [UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing](https://mlsanity.com/paper/1912.10269/) (2019)
- [NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal](https://mlsanity.com/paper/2008.02984/) (2020)
- [CURL: Neural Curve Layers for Global Image Enhancement](https://mlsanity.com/paper/1911.13175/) (2019)

## Built on by (most cited first, 30 of 35)

- [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)
- [Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2403.01497/) (2024)
- [Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancement](https://mlsanity.com/paper/2305.08824/) (2023)
- [Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement](https://mlsanity.com/paper/2308.11932/) (2023)
- [Is Underwater Image Enhancement All Object Detectors Need?](https://mlsanity.com/paper/2311.18814/) (2023)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation](https://mlsanity.com/paper/2407.04230/) (2024)
- [Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier](https://mlsanity.com/paper/2405.16214/) (2024)
- [UIEDP:Underwater Image Enhancement with Diffusion Prior](https://mlsanity.com/paper/2312.06240/) (2023)
- [PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement](https://mlsanity.com/paper/2406.08444/) (2024)
- [End-To-End Underwater Video Enhancement: Dataset and Model](https://mlsanity.com/paper/2403.11506/) (2024)
- [MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs](https://mlsanity.com/paper/2404.13884/) (2024)
- [Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions](https://mlsanity.com/paper/2208.03319/) (2022)
- [RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement](https://mlsanity.com/paper/2302.12186/) (2023)
- [ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions](https://mlsanity.com/paper/2409.18932/) (2024)
- [UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images](https://mlsanity.com/paper/2501.16211/) (2025)
- [MetaUE: Model-based Meta-learning for Underwater Image Enhancement](https://mlsanity.com/paper/2303.06543/) (2023)
- [Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement](https://mlsanity.com/paper/2404.17883/) (2024)
- [A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior](https://mlsanity.com/paper/2405.16197/) (2024)
- [Image-Conditional Diffusion Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2407.05389/) (2024)
- [Transmission and Color-guided Network for Underwater Image Enhancement](https://mlsanity.com/paper/2308.04892/) (2023)
- [LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement](https://mlsanity.com/paper/2406.14973/) (2024)
- [Underwater Image Enhancement using Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/2501.06273/) (2025)
- [Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement](https://mlsanity.com/paper/2505.19895/) (2025)
- [Impact of Underwater Image Enhancement on Feature Matching](https://mlsanity.com/paper/2507.21715/) (2025)
- [Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement](https://mlsanity.com/paper/2604.12257/) (2026)
- [Reinforced Swin-Convs Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2205.00434/) (2022)
- [Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?](https://mlsanity.com/paper/2308.00408/) (2023)
- [DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement](https://mlsanity.com/paper/2312.06999/) (2023)

## Abstract (arXiv)

Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the last few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first to use HSV color space for underwater image enhancement based on deep learning. UIEC^2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show the good performance of our proposed method in both subjective comparisons and objective metrics. The code are available at https://github.com/BIGWangYuDong/UWEnhancement.