Cascade deep polarization network for precise image semantic segmentation.

Applied optics Pub Date : 2025-09-20 DOI:10.1364/AO.561465
Jinyu Zhang, Xu Ma, Weili Chen, Hantang Chen, Gonzalo R Arce
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引用次数: 0

Abstract

Optical polarization imaging technology provides multi-dimensional light field information, encompassing spatial details and polarization data, which can be exploited for image semantic segmentation for target scene analysis. Most recent works focus on the development of neural networks with separate simple preprocessing steps to deal with the raw polarization images, which limit the accuracy of semantic segmentation. This paper proposes a novel, to the best of our knowledge, method, dubbed cascade deep polarization network (CDPN), to improve the performance of semantic segmentation by integrating preprocessing modules directly into the end-to-end deep learning work. The raw input data include the angle of linear polarization, degree of linear polarization, and a set of Stokes parameters. The multi-dimensional feature maps are extracted from the raw data through the image denoising, fusion, and enhancement modules, which are then concatenated with a backbone network to obtain the segmentation results. By collaboratively training the preprocessing modules and backbone network with self-supervised loss functions, we strive to find out the optimal segmentation solution. Experimental results show that the proposed method can effectively improve the segmentation accuracy, while maintaining fast computation speed.

用于图像精确语义分割的级联深度极化网络。
光偏振成像技术提供了包含空间细节和偏振数据的多维光场信息,可用于图像语义分割,用于目标场景分析。近年来的研究主要集中在开发具有单独的简单预处理步骤的神经网络来处理原始极化图像,这限制了语义分割的准确性。本文提出了一种新的方法,据我们所知,被称为级联深度极化网络(CDPN),通过将预处理模块直接集成到端到端深度学习工作中来提高语义分割的性能。原始输入数据包括线极化角、线极化度和一组Stokes参数。通过图像去噪、融合和增强模块对原始数据进行多维特征映射提取,然后与骨干网进行拼接,得到分割结果。通过协同训练预处理模块和具有自监督损失函数的骨干网,努力找出最优分割方案。实验结果表明,该方法能有效提高分割精度,同时保持较快的计算速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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