# An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

arXiv: https://arxiv.org/abs/1907.03246 · HTML: https://mlsanity.com/paper/1907.03246/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception](https://mlsanity.com/paper/2201.02832/) (2022)
- [Domain Adaptation for Underwater Image Enhancement via Content and Style Separation](https://mlsanity.com/paper/2202.08537/) (2022)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions](https://mlsanity.com/paper/2208.03319/) (2022)
- [Robustly Removing Deep Sea Lighting Effects for Visual Mapping of Abyssal Plains](https://mlsanity.com/paper/2110.00480/) (2021)
- [A Survey of Fish Tracking Techniques Based on Computer Vision](https://mlsanity.com/paper/2110.02551/) (2021)
- [A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior](https://mlsanity.com/paper/2405.16197/) (2024)
- [Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution](https://mlsanity.com/paper/2403.05808/) (2024)
- [An Adaptive Underwater Image Enhancement Framework via Multi-Domain Fusion and Color Compensation](https://mlsanity.com/paper/2503.03640/) (2025)
- [A deep neural network for multi-species fish detection using multiple acoustic cameras](https://mlsanity.com/paper/2109.10664/) (2021)
- [Empowering Semantic-Sensitive Underwater Image Enhancement with VLM](https://mlsanity.com/paper/2603.12773/) (2026)

## Abstract (arXiv)

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.