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

TL;DR

This paper introduces a physical model-guided framework that jointly trains a deep degradation model and an enhancement network to improve underwater image quality and estimate scene depth accurately.

Contribution

It proposes a novel framework that combines physical modeling with neural networks for improved underwater image enhancement and depth estimation, compatible with any UIE model.

Findings

UIEConv achieves superior enhancement results across diverse scenes.

The depth estimation sub-network provides accurate underwater scene depth.

Framework effectively models physical constraints for better enhancement.

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.