# Diving Deeper into Underwater Image Enhancement: A Survey

arXiv: https://arxiv.org/abs/1907.07863 · HTML: https://mlsanity.com/paper/1907.07863/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

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

- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [SeaThru-NeRF: Neural Radiance Fields in Scattering Media](https://mlsanity.com/paper/2304.07743/) (2023)
- [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021)
- [SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception](https://mlsanity.com/paper/2201.02832/) (2022)
- [UniUIR: Considering Underwater Image Restoration as An All-in-One Learner](https://mlsanity.com/paper/2501.12981/) (2025)
- [Underwater Image Restoration via Contrastive Learning and a Real-world Dataset](https://mlsanity.com/paper/2106.10718/) (2021)
- [Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement](https://mlsanity.com/paper/2308.15816/) (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)
- [O-Mamba: O-shape State-Space Model for Underwater Image Enhancement](https://mlsanity.com/paper/2408.12816/) (2024)
- [MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility](https://mlsanity.com/paper/2312.15633/) (2023)
- [PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images](https://mlsanity.com/paper/2404.03998/) (2024)
- [Marine Snow Removal Benchmarking Dataset](https://mlsanity.com/paper/2103.14249/) (2021)
- [UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics](https://mlsanity.com/paper/2204.08813/) (2022)
- [3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling](https://mlsanity.com/paper/2505.21238/) (2025)
- [DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement](https://mlsanity.com/paper/2503.12470/) (2025)
- [A Survey of Fish Tracking Techniques Based on Computer Vision](https://mlsanity.com/paper/2110.02551/) (2021)
- [A deep learning approach for marine snow synthesis and removal](https://mlsanity.com/paper/2311.15584/) (2023)
- [Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network](https://mlsanity.com/paper/2408.03564/) (2024)
- [Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement](https://mlsanity.com/paper/2507.06234/) (2025)
- [On the limits of perceptual quality measures for enhanced underwater images](https://mlsanity.com/paper/2207.05470/) (2022)
- [Transmission and Color-guided Network for Underwater Image Enhancement](https://mlsanity.com/paper/2308.04892/) (2023)
- [Single image deep defocus estimation and its applications](https://mlsanity.com/paper/2107.14443/) (2021)
- [Enhancing Underwater Images Using Deep Learning with Subjective Image Quality Integration](https://mlsanity.com/paper/2507.05393/) (2025)
- [Automated Marine Biofouling Assessment: Benchmarking Computer Vision and Multimodal LLMs on the Level of Fouling Scale](https://mlsanity.com/paper/2601.20196/) (2026)
- [UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation](https://mlsanity.com/paper/2603.27012/) (2026)
- [CoRe-UIE: Rethinking Coexisting and Region-wise Degradation for Underwater Image Enhancement](https://mlsanity.com/paper/2608.08965/) (2026)
- [Robust Cross-Modal Foundation Model Perception for Underwater Robots under Degraded Visual Conditions](https://mlsanity.com/paper/2608.19710/) (2026)
- [PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2610.17936/) (2026)
- [Physics-Shaped, Not Physical: Auditing Physics-Guided Underwater Image Restoration](https://mlsanity.com/paper/2610.19763/) (2026)

## Abstract (arXiv)

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time, and hence; a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before. To be specific, we first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented including network architecture, network parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.