# Underwater Image Enhancement based on Deep Learning and Image Formation Model

arXiv: https://arxiv.org/abs/2101.00991 · HTML: https://mlsanity.com/paper/2101.00991/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2021.

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration](https://mlsanity.com/paper/2107.02660/) (2021)
- [HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning](https://mlsanity.com/paper/2411.18296/) (2024)
- [WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond](https://mlsanity.com/paper/2308.00931/) (2023)
- [Adaptive deep learning framework for robust unsupervised underwater image enhancement](https://mlsanity.com/paper/2212.08983/) (2022)
- [OceanSplat: Object-aware Gaussian Splatting with Trinocular View Consistency for Underwater Scene Reconstruction](https://mlsanity.com/paper/2601.04984/) (2026)
- [Virtual Underwater Datasets for Autonomous Inspections](https://mlsanity.com/paper/2209.06013/) (2022)
- [Underwater Image Enhancement with Physical-based Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2409.18476/) (2024)
- [Underwater Image Reconstruction Using a Swin Transformer-Based Generator and PatchGAN Discriminator](https://mlsanity.com/paper/2512.05866/) (2025)
- [Reinforced Swin-Convs Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2205.00434/) (2022)
- [CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception](https://mlsanity.com/paper/2309.01102/) (2023)
- [Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrain](https://mlsanity.com/paper/2407.13159/) (2024)
- [Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models](https://mlsanity.com/paper/2409.06420/) (2024)
- [Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation](https://mlsanity.com/paper/2409.19685/) (2024)
- [VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement](https://mlsanity.com/paper/2505.01224/) (2025)
- [Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement](https://mlsanity.com/paper/2508.04123/) (2025)

## Abstract (arXiv)

Underwater robots play an important role in oceanic geological exploration, resource exploitation, ecological research, and other fields. However, the visual perception of underwater robots is affected by various environmental factors. The main challenge now is that images captured by underwater robots are color-distorted. The hue of underwater images tends to be close to green and blue. In addition, the contrast is low and the details are fuzzy. In this paper, a new underwater image enhancement algorithm based on deep learning and image formation model is proposed. Experimental results show that the advantages of the proposed method are that it eliminates the influence of underwater environmental factors, enriches the color, enhances details, achieves higher scores in PSNR and SSIM metrics, and helps feature key-point point matching get better results. Another significant advantage is that its computation speed is much faster than other methods.