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Data-driven polarimetric approaches fuel computational imaging expansion
数据驱动的极化方法推动了计算成像的扩展
データ駆動の偏光方法は計算イメージングの拡張を推進する
데이터 제어 극화 방법으로 컴퓨팅 이미지 확장
El método de polarización impulsado por datos impulsa la expansión de la imagen computacional
Une approche de polarisation axée sur les données alimente l'expansion de l'imagerie computationnelle
Метод поляризации, основанный на данных, способствует расширению вычислительной визуализации
Sylvain Gigan
Laboratoire Kastler Brossel, École Normale Supérieure/PSL Research University, Paris 75005, France
Opto-Electronic Advances, 25 September 2024
Abstract

Incorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). A recent review in Opto-Electronic Science reports on the developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection and biomedical imaging. The review carefully analyzes these new trends with their advantages and disadvantages, and provides a general insight for future research and development.
Opto-Electronic Advances_1
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