# An Adaptive Underwater Image Enhancement Framework via Multi-Domain Fusion and Color Compensation

arXiv: https://arxiv.org/abs/2503.03640 · HTML: https://mlsanity.com/paper/2503.03640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 69 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [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)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [Deep Underwater Image Enhancement](https://mlsanity.com/paper/1807.03528/) (2018)
- [UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement](https://mlsanity.com/paper/2404.14542/) (2024)
- [A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image Enhancement](https://mlsanity.com/paper/2308.05447/) (2023)
- [An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging](https://mlsanity.com/paper/1907.03246/) (2019)
- [Underwater Optical Image Processing: A Comprehensive Review](https://mlsanity.com/paper/1702.03600/) (2017)
- [DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods](https://mlsanity.com/paper/2303.04025/) (2023)
- [SINET: Sparsity-driven Interpretable Neural Network for Underwater Image Enhancement](https://mlsanity.com/paper/2409.01022/) (2024)

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

- [UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding](https://mlsanity.com/paper/2510.18262/) (2025)

## Abstract (arXiv)

Underwater optical imaging is severely degraded by light absorption, scattering, and color distortion, hindering visibility and accurate image analysis. This paper presents an adaptive enhancement framework integrating illumination compensation, multi-domain filtering, and dynamic color correction. A hybrid illumination compensation strategy combining CLAHE, Gamma correction, and Retinex enhances visibility. A two-stage filtering process, including spatial-domain (Gaussian, Bilateral, Guided) and frequency-domain (Fourier, Wavelet) methods, effectively reduces noise while preserving details. To correct color distortion, an adaptive color compensation (ACC) model estimates spectral attenuation and water type to combine RCP, DCP, and MUDCP dynamically. Finally, a perceptually guided color balance mechanism ensures natural color restoration. Experimental results on benchmark datasets demonstrate superior performance over state-of-the-art methods in contrast enhancement, color correction, and structural preservation, making the framework robust for underwater imaging applications.