PubCard - Data-driven polarimetric imaging: a review

(Peer-Reviewed) Data-driven polarimetric imaging: a review

Kui Yang 杨奎 ¹, Fei Liu 刘飞 ¹, Shiyang Liang 梁诗阳 ³, Meng Xiang 相萌 ¹, Pingli Han 韩平丽 ¹, Jinpeng Liu 刘金鹏 ¹, Xue Dong 董雪 ¹, Yi Wei 卫毅 ⁴, Bingjian Wang 王炳健 ², Koichi Shimizu ³, Xiaopeng Shao 邵晓鹏 ¹ ⁵

¹ School of Optoelectronic Engineering, Xidian University, Xi'an 710071, China
中国西安西安电子科技大学光电工程学院
² School of Physics, Xidian University, Xi'an 710071, China
中国西安西安电子科技大学技术物理学院
³ Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan
⁴ Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
⁵ Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China
中国杭州西安电子科技大学杭州研究院

Opto-Electronic Science, 2024-02-24

https://doi.org/10.29026/oes.2024.230042

https://www.oejournal.org/article/doi/10.29026/oes.2024.230042

Abstract

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications. The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.

Polarization information is increasingly incorporated into convolutional neural networks (CNN) as a supplemental feature of objects to improve performance in computer vision task applications. Polarimetric imaging and deep learning can extract abundant information to address various challenges. Therefore, this article briefly reviews recent developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection, and biomedical imaging.

Furthermore, we synthetically analyze the input, datasets, and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages. We also highlight the significance of data-driven polarimetric imaging in future research and development.