# UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images

arXiv: https://arxiv.org/abs/2501.16211 · HTML: https://mlsanity.com/paper/2501.16211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration](https://mlsanity.com/paper/2311.16845/) (2023)
- [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021)
- [Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy](https://mlsanity.com/paper/2309.03445/) (2023)
- [CLE Diffusion: Controllable Light Enhancement Diffusion Model](https://mlsanity.com/paper/2308.06725/) (2023)
- [UIEDP:Underwater Image Enhancement with Diffusion Prior](https://mlsanity.com/paper/2312.06240/) (2023)
- [Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions](https://mlsanity.com/paper/2208.03319/) (2022)

## Built on by (most cited first, 3 of 3)

- [Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applications](https://mlsanity.com/paper/2510.03353/) (2025)
- [AquaFeat: A Features-Based Image Enhancement Model for Underwater Object Detection](https://mlsanity.com/paper/2508.12343/) (2025)
- [AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking](https://mlsanity.com/paper/2601.09652/) (2026)

## Abstract (arXiv)

Activities in underwater environments are paramount in several scenarios, which drives the continuous development of underwater image enhancement techniques. A major challenge in this domain is the depth at which images are captured, with increasing depth resulting in a darker environment. Most existing methods for underwater image enhancement focus on noise removal and color adjustment, with few works dedicated to brightness enhancement. This work introduces a novel unsupervised learning approach to underwater image enhancement using a diffusion model. Our method, called UDBE, is based on conditional diffusion to maintain the brightness details of the unpaired input images. The input image is combined with a color map and a Signal-Noise Relation map (SNR) to ensure stable training and prevent color distortion in the output images. The results demonstrate that our approach achieves an impressive accuracy rate in the datasets UIEB, SUIM and RUIE, well-established underwater image benchmarks. Additionally, the experiments validate the robustness of our approach, regarding the image quality metrics PSNR, SSIM, UIQM, and UISM, indicating the good performance of the brightness enhancement process. The source code is available here: https://github.com/gusanagy/UDBE.