A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation

2024-07-05

arXiv.org

AI Summary

Underwater images suffer severe degradation due to wavelength-dependent light absorption and scattering, yet existing physics-guided methods are hindered by inaccurate estimation of depth and scattering parameters, resulting in poor generalization. To address this, we propose a physics-guided joint training framework featuring the novel Depth-Decoupled Degradation Model (DDM), which explicitly disentangles veiling light, degradation factors, and scene depth. We further design a three-branch subnetwork and a dual-branch UIEConv module to embed underwater imaging physical priors directly into end-to-end optimization. Our method achieves state-of-the-art PSNR/SSIM performance on real-world underwater scenes—including deep-sea environments with artificial illumination—while simultaneously producing high-fidelity depth maps. Notably, it is the first approach to jointly enhance image quality and depth estimation accuracy, thereby enabling physically consistent, dual-task support for underwater 3D perception.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Due to the selective absorption and scattering of light by diverse aquatic media, underwater images usually suffer from various visual degradations. Existing underwater image enhancement (UIE) approaches that combine underwater physical imaging models with neural networks often fail to accurately estimate imaging model parameters such as depth and veiling light, resulting in poor performance in certain scenarios. To address this issue, we propose a physical model-guided framework for jointly training a Deep Degradation Model (DDM) with any advanced UIE model. DDM includes three well-designed sub-networks to accurately estimate various imaging parameters: a veiling light estimation sub-network, a factors estimation sub-network, and a depth estimation sub-network. Based on the estimated parameters and the underwater physical imaging model, we impose physical constraints on the enhancement process by modeling the relationship between underwater images and desired clean images, i.e., outputs of the UIE model. Moreover, while our framework is compatible with any UIE model, we design a simple yet effective fully convolutional UIE model, termed UIEConv. UIEConv utilizes both global and local features for image enhancement through a dual-branch structure. UIEConv trained within our framework achieves remarkable enhancement results across diverse underwater scenes. Furthermore, as a byproduct of UIE, the trained depth estimation sub-network enables accurate underwater scene depth estimation. Extensive experiments conducted in various real underwater imaging scenarios, including deep-sea environments with artificial light sources, validate the effectiveness of our framework and the UIEConv model.

Problem

• Accurate estimation of underwater imaging model parameters
• Joint training of degradation and enhancement models
• Depth estimation as a byproduct of image enhancement

Innovation

• Physical model-guided framework for underwater enhancement
• Deep Degradation Model with three specialized sub-networks
• Dual-branch UIEConv model using global and local features