Underwater imaging faces challenges due to complex optical properties in water. Our purpose is to explore the application of polarimetric imaging in image recovery under turbid water based on deep learning. A polarization camera is used to capture the polarization images of objects under water as datasets. The method used in our study aims to explore a structure and loss function that is suitable for the model. In terms of the model structure, four pairs of models consisting of polarized version and gray version based on the idea of dense U-Net and information flow were proposed. In the aspect of loss function, the method of combining weighted mean squared error with perceptual loss was proposed and a proper set of loss weights was selected through comparison experiments. Comparing the model outputs, it is found that adding polarized information along with the light intensity information to the model at the very front of the model structure brings about better recovering image. The model structure proposed can be used for image recovery in turbid water or other scattering environments. Since the polarization characteristics are considered, the recovered image has more detailed features than that where only intensity is considered. The results of comparison with other methods show the effectiveness of the proposed method.

Ran Zhang

Xinyuan Gui

Haoyuan Cheng

Jinkui Chu

Applied Optics

在光学、光子学、成像和传感领域提供以应用为中心的研究的光学领域的每个人都必须阅读一本备受推崇的优质书籍。与该期刊密切相关的主题包括光学技术、激光、光子学、环境光学和信息处理。

Applied Optics is a prestigious journal that provides application-oriented research in the fields of optics, photonics, imaging, and sensing, making it a must-read for everyone in the optics community. Related topics include optical technologies, lasers, photonics, environmental optics, and information processing.

《Applied Optics》是一本在光学、光子学、成像和传感领域提供应用研究的权威期刊，深受光学领域研究者的推崇。期刊涵盖的相关主题包括光学技术、激光、光子学、环境光学和信息处理等。

Underwater image recovery utilizing polarimetric imaging based on neural networks

It is found that adding polarized information along with the light intensity information to the model at the very front of the model structure brings about better recovering image.

Category	Quartile
工程技术	4
工程技术, 光学	4

Underwater image recovery utilizing polarimetric imaging based on neural networks

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