# Impact of Underwater Image Enhancement on Feature Matching

arXiv: https://arxiv.org/abs/2507.21715 · HTML: https://mlsanity.com/paper/2507.21715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 75 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators](https://mlsanity.com/paper/2306.08918/) (2023)
- [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021)
- [Domain Adaptation for Underwater Image Enhancement](https://mlsanity.com/paper/2108.09650/) (2021)
- [SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception](https://mlsanity.com/paper/2201.02832/) (2022)
- [Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map](https://mlsanity.com/paper/1906.08673/) (2019)
- [Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration](https://mlsanity.com/paper/2106.07910/) (2021)
- [Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain](https://mlsanity.com/paper/1904.02215/) (2019)
- [Fast ORB-SLAM without Keypoint Descriptors](https://mlsanity.com/paper/2008.09870/) (2020)
- [MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction](https://mlsanity.com/paper/2002.05333/) (2020)
- [LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement](https://mlsanity.com/paper/2105.01299/) (2021)
- [HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration](https://mlsanity.com/paper/2107.02660/) (2021)
- [SurroundNet: Towards Effective Low-Light Image Enhancement](https://mlsanity.com/paper/2110.05098/) (2021)
- [End-To-End Underwater Video Enhancement: Dataset and Model](https://mlsanity.com/paper/2403.11506/) (2024)

## Built on by (most cited first, 1 of 1)

- [BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illumination](https://mlsanity.com/paper/2604.19133/) (2026)

## Abstract (arXiv)

We introduce local matching stability and furthest matchable frame as quantitative measures for evaluating the success of underwater image enhancement. This enhancement process addresses visual degradation caused by light absorption, scattering, marine growth, and debris. Enhanced imagery plays a critical role in downstream tasks such as path detection and autonomous navigation for underwater vehicles, relying on robust feature extraction and frame matching. To assess the impact of enhancement techniques on frame-matching performance, we propose a novel evaluation framework tailored to underwater environments. Through metric-based analysis, we identify strengths and limitations of existing approaches and pinpoint gaps in their assessment of real-world applicability. By incorporating a practical matching strategy, our framework offers a robust, context-aware benchmark for comparing enhancement methods. Finally, we demonstrate how visual improvements affect the performance of a complete real-world algorithm -- Simultaneous Localization and Mapping (SLAM) -- reinforcing the framework's relevance to operational underwater scenarios.