# DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement

arXiv: https://arxiv.org/abs/2503.12470 · HTML: https://mlsanity.com/paper/2503.12470/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 23 of 23)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [U-shape Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2111.11843/) (2021)
- [Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2211.13202/) (2022)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Underwater Ranker: Learn Which Is Better and How to Be Better](https://mlsanity.com/paper/2208.06857/) (2022)
- [PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators](https://mlsanity.com/paper/2306.08918/) (2023)
- [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)
- [Diving Deeper into Underwater Image Enhancement: A Survey](https://mlsanity.com/paper/1907.07863/) (2019)
- [Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement](https://mlsanity.com/paper/2309.04089/) (2023)
- [Deep Underwater Image Enhancement](https://mlsanity.com/paper/1807.03528/) (2018)
- [Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map](https://mlsanity.com/paper/1906.08673/) (2019)
- [SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images](https://mlsanity.com/paper/2302.08269/) (2023)
- [FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets](https://mlsanity.com/paper/2302.12772/) (2023)
- [A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image Enhancement](https://mlsanity.com/paper/2308.05447/) (2023)
- [A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation](https://mlsanity.com/paper/2407.04230/) (2024)
- [Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier](https://mlsanity.com/paper/2405.16214/) (2024)
- [UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection](https://mlsanity.com/paper/2309.04708/) (2023)
- [Adaptive deep learning framework for robust unsupervised underwater image enhancement](https://mlsanity.com/paper/2212.08983/) (2022)
- [Single Underwater Image Enhancement Using an Analysis-Synthesis Network](https://mlsanity.com/paper/2108.09023/) (2021)
- [Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement](https://mlsanity.com/paper/2404.17883/) (2024)

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

- [RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision](https://mlsanity.com/paper/2606.06176/) (2026)
- [Fusing Transferred Priors and Physics-based Decomposition for Underwater Image Enhancement](https://mlsanity.com/paper/2606.15648/) (2026)
- [BenthicFlow: Generating Extensible Underwater Environments via Flow Matching](https://mlsanity.com/paper/2608.23173/) (2026)

## Abstract (arXiv)

Due to the complex interplay of light absorption and scattering in the underwater environment, underwater images experience significant degradation. This research presents a two-stage underwater image enhancement network called the Data-Driven and Physical Parameters Fusion Network (DPF-Net), which harnesses the robustness of physical imaging models alongside the generality and efficiency of data-driven methods. We first train a physical parameter estimate module using synthetic datasets to guarantee the trustworthiness of the physical parameters, rather than solely learning the fitting relationship between raw and reference images by the application of the imaging equation, as is common in prior studies. This module is subsequently trained in conjunction with an enhancement network, where the estimated physical parameters are integrated into a data-driven model within the embedding space. To maintain the uniformity of the restoration process amid underwater imaging degradation, we propose a physics-based degradation consistency loss. Additionally, we suggest an innovative weak reference loss term utilizing the entire dataset, which alleviates our model's reliance on the quality of individual reference images. Our proposed DPF-Net demonstrates superior performance compared to other benchmark methods across multiple test sets, achieving state-of-the-art results. The source code and pre-trained models are available on the project home page: https://github.com/OUCVisionGroup/DPF-Net.