A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
<|reference_start|>A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation: 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.<|reference_end|>
@article{du2024a,
    title={A Physical Model-Guided Framework for Underwater Image Enhancement and
  Depth Estimation},
    author={Dazhao Du, Enhan Li, Lingyu Si, Fanjiang Xu, Jianwei Niu, Fuchun Sun},
    journal={arXiv preprint arXiv:2407.04230},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04230},
    primaryClass={cs.CV}
}