Cover story: Yang K, Liu F, Liang SY et al. Data-driven polarimetric imaging: a review. Opto-Electron Sci 3, 230042 (2024).

Data-driven polarimetric imaging is a novel approach aimed at compensating for the defects and difficulties of a single-information interpretation model. Combining polarimetric imaging and deep learning can helps extract valuable information from polarization data to address various challenges.

Data-driven polarization imaging technologies have gradually found applications in polarization information reconstruction and enhancement, target detection, biomedical imaging, pathological diagnosis, semantic segmentation, diffusive scattering media, 3D reconstruction, reflection removal, and other fields. Due to the high-order non-linear representation capabilities of convolutional neural networks, they can extract higher-dimensional information from complex imaging media and scenes, significantly improving the interpretation and reconstruction of physical properties in challenging environments. This optimization enhances imaging results in low signal-to-noise ratio environments, such as natural settings, scattering media, noise, ambient light interference, low dynamics, and biological tissues. Additionally, the introduction of polarization information provides additional supplementary information, expanding the application scope of existing intensity-based deep learning algorithms, and further extending to new application domains like 3D reconstruction and physical information transformation. This development provides powerful tools and methods for improving imaging quality, enhancing target interpretation accuracy, and driving the progress of emerging application areas.