Appl Opt. 2021 Sep 20;60(27):8419-8425. doi: 10.1364/AO.431299.
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.
水下成像由于水中复杂的光学特性而面临挑战。我们的目的是探索基于深度学习的混浊水下偏振成像在图像恢复中的应用。偏振相机用于捕获水下物体的偏振图像作为数据集。我们研究中使用的方法旨在探索适合模型的结构和损失函数。在模型结构方面，提出了四对基于密集 U-Net 和信息流思想的偏振版本和灰度版本的模型。在损失函数方面，提出了结合加权均方误差和感知损失的方法，并通过对比实验选择了合适的损失权重集。通过比较模型输出，发现将偏振信息与光强信息一起添加到模型结构的前端，对模型的恢复图像有更好的效果。所提出的模型结构可用于混浊水或其他散射环境中的图像恢复。由于考虑了偏振特性，因此恢复的图像比仅考虑强度的图像具有更多的细节特征。与其他方法的比较结果表明了所提出方法的有效性。
