# Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map

arXiv: https://arxiv.org/abs/1906.08673 · HTML: https://mlsanity.com/paper/1906.08673/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Domain Adaptation for Underwater Image Enhancement](https://mlsanity.com/paper/2108.09650/) (2021)
- [Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancement](https://mlsanity.com/paper/2305.08824/) (2023)
- [Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis](https://mlsanity.com/paper/2501.02701/) (2025)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [Underwater Image Enhancement with Cascaded Contrastive Learning](https://mlsanity.com/paper/2411.10682/) (2024)
- [Learning a self-supervised tone mapping operator via feature contrast masking loss](https://mlsanity.com/paper/2110.09866/) (2021)
- [Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement](https://mlsanity.com/paper/2102.00670/) (2021)
- [UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection](https://mlsanity.com/paper/2309.04708/) (2023)
- [MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs](https://mlsanity.com/paper/2404.13884/) (2024)
- [Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions](https://mlsanity.com/paper/2208.03319/) (2022)
- [RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement](https://mlsanity.com/paper/2302.12186/) (2023)
- [Mamba-UIE: Enhancing Underwater Images with Physical Model Constraint](https://mlsanity.com/paper/2407.19248/) (2024)
- [DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement](https://mlsanity.com/paper/2503.12470/) (2025)
- [Dual High-Order Total Variation Model for Underwater Image Restoration](https://mlsanity.com/paper/2407.14868/) (2024)
- [PDCFNet: Enhancing Underwater Images through Pixel Difference Convolution](https://mlsanity.com/paper/2409.19269/) (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)
- [Impact of Underwater Image Enhancement on Feature Matching](https://mlsanity.com/paper/2507.21715/) (2025)
- [DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement](https://mlsanity.com/paper/2312.06999/) (2023)
- [MCMS: Multi-Category Information and Multi-Scale Stripe Attention for Blind Motion Deblurring](https://mlsanity.com/paper/2405.01083/) (2024)
- [Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation](https://mlsanity.com/paper/2507.07687/) (2025)
- [AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement](https://mlsanity.com/paper/2512.05960/) (2025)
- [Enhancing Underwater Images via Adaptive Semantic-aware Codebook Learning](https://mlsanity.com/paper/2602.10586/) (2026)
- [A Dual-Branch Collaborative Framework for Joint Optimization of Underwater Image Enhancement and Object Detection](https://mlsanity.com/paper/2606.15857/) (2026)

## Abstract (arXiv)

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light and the transmission map. Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior via the statistic of clear and high resolution underwater images, then a scene depth map based on the underwater light attenuation prior and an adjusted reversed saturation map are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R channel and G-B channels. Finally, to improve the color and contrast of the restored image with a natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, less computation time, more superior performance, and more valuable information retention.