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DPF-Net: Physical imaging model embedded data-driven underwater image enhancement

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Bibliographic Details
Title: DPF-Net: Physical imaging model embedded data-driven underwater image enhancement
Authors: Han Mei, Kunqian Li, Shuaixin Liu, Chengzhi Ma, Qianli Jiang
Source: ISPRS Journal of Photogrammetry and Remote Sensing. 228:679-693
Publication Status: Preprint
Publisher Information: Elsevier BV, 2025.
Publication Year: 2025
Subject Terms: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing
Description: 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.
Document Type: Article
Language: English
ISSN: 0924-2716
DOI: 10.1016/j.isprsjprs.2025.07.031
DOI: 10.48550/arxiv.2503.12470
Access URL: http://arxiv.org/abs/2503.12470
Rights: Elsevier TDM
arXiv Non-Exclusive Distribution
Accession Number: edsair.doi.dedup.....c755e47bda57cf1f950d9a8d3c8992d4
Database: OpenAIRE
Description
Abstract: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.
ISSN:09242716
DOI:10.1016/j.isprsjprs.2025.07.031