mlsanityData to 14 Oct 2026

Impact of Underwater Image Enhancement on Feature Matching

arXiv 2507.21715 ↗First posted 29 Jul 2025cs.CVcited by 1

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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.

Key facts. Cited by 1 paper 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

22 of 75 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
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  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  5. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,443
  6. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,320
  7. 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 253
  8. 2019Fast Underwater Image Enhancement for Improved Visual Perceptioncited by 183
  9. 2017DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networkscited by 156
  10. 2023PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminatorscited by 49
  11. 2021UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Spacecited by 35
  12. 2021Domain Adaptation for Underwater Image Enhancementcited by 35
10 more
  1. 2022SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perceptioncited by 33
  2. 2019Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Mapcited by 23
  3. 2021Wavelength-based Attributed Deep Neural Network for Underwater Image Restorationcited by 23
  4. 2019Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domaincited by 16
  5. 2020Fast ORB-SLAM without Keypoint Descriptorscited by 9
  6. 2020MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correctioncited by 8
  7. 2021LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancementcited by 8
  8. 2021HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restorationcited by 7
  9. 2021SurroundNet: Towards Effective Low-Light Image Enhancementcited by 7
  10. 2024End-To-End Underwater Video Enhancement: Dataset and Modelcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illuminationcited by 0