# SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception

arXiv: https://arxiv.org/abs/2201.02832 · HTML: https://mlsanity.com/paper/2201.02832/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 66 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 27 of 27)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences](https://mlsanity.com/paper/1912.05524/) (2019)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis](https://mlsanity.com/paper/1801.05091/) (2018)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [Coherent Semantic Attention for Image Inpainting](https://mlsanity.com/paper/1905.12384/) (2019)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [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)
- [Visual Representations for Semantic Target Driven Navigation](https://mlsanity.com/paper/1805.06066/) (2018)
- [Universal Correspondence Network](https://mlsanity.com/paper/1606.03558/) (2016)
- [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation](https://mlsanity.com/paper/1912.12215/) (2019)
- [Densely Semantically Aligned Person Re-Identification](https://mlsanity.com/paper/1812.08967/) (2018)
- [Bayesian Relational Memory for Semantic Visual Navigation](https://mlsanity.com/paper/1909.04306/) (2019)
- [Semantically Contrastive Learning for Low-light Image Enhancement](https://mlsanity.com/paper/2112.06451/) (2021)
- [Diving Deeper into Underwater Image Enhancement: A Survey](https://mlsanity.com/paper/1907.07863/) (2019)
- [An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging](https://mlsanity.com/paper/1907.03246/) (2019)
- [MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction](https://mlsanity.com/paper/2002.05333/) (2020)
- [Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising](https://mlsanity.com/paper/2104.01073/) (2021)

## Built on by (most cited first, 30 of 33)

- [Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancement](https://mlsanity.com/paper/2305.08824/) (2023)
- [NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2504.14582/) (2025)
- [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)
- [MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility](https://mlsanity.com/paper/2312.15633/) (2023)
- [Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier](https://mlsanity.com/paper/2405.16214/) (2024)
- [Underwater Image Enhancement with Cascaded Contrastive Learning](https://mlsanity.com/paper/2411.10682/) (2024)
- [UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics](https://mlsanity.com/paper/2204.08813/) (2022)
- [Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design](https://mlsanity.com/paper/2603.01767/) (2026)
- [UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement](https://mlsanity.com/paper/2306.08344/) (2023)
- [WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2406.13344/) (2024)
- [RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method](https://mlsanity.com/paper/2311.00246/) (2023)
- [UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors](https://mlsanity.com/paper/2411.05886/) (2024)
- [DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement](https://mlsanity.com/paper/2503.12470/) (2025)
- [WWE-UIE: A Wavelet & White Balance Efficient Network for Underwater Image Enhancement](https://mlsanity.com/paper/2511.16321/) (2025)
- [SLLEN: Semantic-aware Low-light Image Enhancement Network](https://mlsanity.com/paper/2211.11571/) (2022)
- [A Semi-supervised Physics-Aware Triple-Stream Underwater Image Enhancement Network](https://mlsanity.com/paper/2307.11470/) (2023)
- [StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging](https://mlsanity.com/paper/2404.09158/) (2024)
- [Depth-Assisted Network for Indiscernible Marine Object Counting with Adaptive Motion-Differentiated Feature Encoding](https://mlsanity.com/paper/2503.08152/) (2025)
- [Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.16056/) (2025)
- [FUSION: Frequency-guided Underwater Spatial Image recOnstructioN](https://mlsanity.com/paper/2504.01243/) (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)
- [Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition](https://mlsanity.com/paper/2305.12661/) (2023)
- [IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater](https://mlsanity.com/paper/2312.06955/) (2023)
- [Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement](https://mlsanity.com/paper/2508.04123/) (2025)
- [TIDE: Two-Stage Inverse Degradation Estimation with Guided Prior Disentanglement for Underwater Image Restoration](https://mlsanity.com/paper/2512.07171/) (2025)
- [Enhancing Underwater Images via Adaptive Semantic-aware Codebook Learning](https://mlsanity.com/paper/2602.10586/) (2026)
- [Retinex Meets Language: A Physics-Semantics-Guided Underwater Image Enhancement Network](https://mlsanity.com/paper/2603.07076/) (2026)
- [Beyond Fidelity: Semantic Similarity Assessment in Low-Level Image Processing](https://mlsanity.com/paper/2604.25408/) (2026)

## Abstract (arXiv)

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions. Accordingly, we propose semantic region-wise enhancement module to perceive the degradation of different semantic regions from multiple scales and feed it back to the global attention features extracted from its original scale. This strategy helps to achieve robust and visually pleasant enhancements to different semantic objects, which should thanks to the guidance of semantic information for differentiated enhancement. More importantly, for those degradation types that are not common in the training sample distribution, the guidance connects them with the already well-learned types according to their semantic relevance. Extensive experiments on the publicly available datasets and our proposed dataset demonstrated the impressive performance of SGUIE-Net. The code and proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.html