TSMPN-PSI: High-performance polarization scattering imaging based on three-stage multi-pipeline networks
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影响因子：3.8
DOI码：10.1364/OE.501269
发表刊物：Optics Express
摘要：Polarization imaging, which provides multidimensional information beyond traditional intensity imaging, has prominent advantages for complex imaging tasks, particularly in scattering environments. By introducing deep learning (DL) into computational imaging and sensing, polarization scattering imaging (PSI) has obtained impressive progresses, however, it remains a challenging but long-standing puzzle due to the fact that scattering medium can result in significant degradation of the object information. Herein, we explore the relationship between multiple polarization feature learning strategy and the PSI performances, and propose a new multi-polarization driven multi-pipeline (MPDMP) framework to extract rich hierarchical representations from multiple independent polarization feature maps. Based on the MPDMP framework, we introduce a well-designed three-stage multi-pipeline networks (TSMPN) architecture to achieve the PSI, named TSMPN-PSI. The proposed TSMPN-PSI comprises three stages: pre-processing polarization image for de-speckling, multiple polarization feature learning, and target information reconstruction. Furthermore, we establish a real-world polarization scattering imaging system under active light illumination to acquire a dataset of real-life scenarios for training the model. Both qualitative and quantitative experimental results show that the proposed TSMPN-PSI achieves higher generalization performance than other methods on three testing data sets refer to imaging distances, target structures, and target materials and their background materials. We believe that our work presents a new framework for the PSI and paves the way to its pragmatic applications.
合写作者：Bing Lin,Kai Guo,Bingyi Liu
第一作者：Xueqiang Fan
论文类型：期刊论文
通讯作者：Zhongyi Guo
文献类型：J
卷号：31
期号：23
页面范围：38097-38113
是否译文：否
发表时间：2023-10-26
收录刊物：SCI
发布期刊链接：https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-23-38097&id=541190