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 techni...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.展开更多
The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware.These limitations pose significant challenges in acquiring higher-dimensional information a...The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware.These limitations pose significant challenges in acquiring higher-dimensional information and reconstructing accurate images,particularly in applications such as scattering imaging,superresolution,and complex scene reconstruction.However,the rapid development and widespread adoption of deep learning are reshaping the field of optical imaging through computational imaging technology.Datadriven computational imaging has ushered in a paradigm shift by leveraging the nonlinear expression and feature learning capabilities of neural networks.This approach transcends the limitations of conventional physical models,enabling the adaptive extraction of critical features directly from data.As a result,computational imaging overcomes the traditional“what you see is what you get”paradigm,paving the way for more compact optical system designs,broader information acquisition,and improved image reconstruction accuracy.These advancements have significantly enhanced the interpretation of highdimensional light-field information and the processing of complex images.This review presents a comprehensive analysis of the integration of deep learning and computational imaging,emphasizing its transformative potential in three core areas:computational optical system design,high-dimensional information interpretation,and image enhancement and processing.Additionally,this review addresses the challenges and future directions of this cutting-edge technology,providing novel insights into interdisciplinary imaging research.展开更多
Polarization underwater imaging is of great potential to target detection in turbid water. Typical methods are challenged by the requirement on degrees of polarization(Do Ps) of both target light and backscattering. A...Polarization underwater imaging is of great potential to target detection in turbid water. Typical methods are challenged by the requirement on degrees of polarization(Do Ps) of both target light and backscattering. A polarization descattering imaging method was developed using the Mueller matrix, which in turn derived a depolarization(Dep) index from the Mueller matrix to characterize scattering media by estimating the transmittance map by combining a developed optimal function.By quantifying light attenuation with the transmittance map, a clear vision of targets can be recovered. Only using the information of scattering media, the underwater vision under diverse water turbidity was enhanced by the results of experimental data.展开更多
This paper presents a polarization descattering imaging method for underwater detection in which the targets have nonuniform polarization characteristics. The core of this method takes the nonuniform distribution of t...This paper presents a polarization descattering imaging method for underwater detection in which the targets have nonuniform polarization characteristics. The core of this method takes the nonuniform distribution of the polarization information of the target-reflected light into account and expands the application field of underwater polarization imaging.Independent component analysis was used to separate the target light and backscattered light. Theoretical analysis and proof-of-concept experiments were employed to demonstrate the effectiveness of the proposed method in estimating target information. The proposed method showed superiority in accurately estimating the target information compared with other polarization imaging methods.展开更多
基金support from the National Natural Science Foundation of China(Nos.62205259,62075175,61975254,62375212,62005203 and 62105254)the Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology(No.B022420004)the Fundamental Research Funds for the Central Universities(No.ZYTS23125).
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.62405231,62205259,62075175,62105254,and 62375212)the National Key Laboratory of Infrared Detection Technologies(No.IRDT-23-06)+1 种基金the Fundamental Research Funds for the Central Universities(Nos.XJSJ24028 and XJS222202)the Open Research Fund of Beijing Key Laboratory of Advanced Optical Remote Sensing Technology(No.AORS202405).
文摘The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware.These limitations pose significant challenges in acquiring higher-dimensional information and reconstructing accurate images,particularly in applications such as scattering imaging,superresolution,and complex scene reconstruction.However,the rapid development and widespread adoption of deep learning are reshaping the field of optical imaging through computational imaging technology.Datadriven computational imaging has ushered in a paradigm shift by leveraging the nonlinear expression and feature learning capabilities of neural networks.This approach transcends the limitations of conventional physical models,enabling the adaptive extraction of critical features directly from data.As a result,computational imaging overcomes the traditional“what you see is what you get”paradigm,paving the way for more compact optical system designs,broader information acquisition,and improved image reconstruction accuracy.These advancements have significantly enhanced the interpretation of highdimensional light-field information and the processing of complex images.This review presents a comprehensive analysis of the integration of deep learning and computational imaging,emphasizing its transformative potential in three core areas:computational optical system design,high-dimensional information interpretation,and image enhancement and processing.Additionally,this review addresses the challenges and future directions of this cutting-edge technology,providing novel insights into interdisciplinary imaging research.
基金This work was supported by the National Natural Science Foundation of China(NSFC)(Nos.62075175 and 62005203)the Key Laboratory of Optical Engineering,Chinese Academy of Sciences。
文摘Polarization underwater imaging is of great potential to target detection in turbid water. Typical methods are challenged by the requirement on degrees of polarization(Do Ps) of both target light and backscattering. A polarization descattering imaging method was developed using the Mueller matrix, which in turn derived a depolarization(Dep) index from the Mueller matrix to characterize scattering media by estimating the transmittance map by combining a developed optimal function.By quantifying light attenuation with the transmittance map, a clear vision of targets can be recovered. Only using the information of scattering media, the underwater vision under diverse water turbidity was enhanced by the results of experimental data.
基金This work was supported by the Key Laboratory of Optical Engineering,Chinese Academy of Sciences(No.QC20191097)the National Natural Science Foundation of China(NSFC)(Nos.62075175 and 62005203).
文摘This paper presents a polarization descattering imaging method for underwater detection in which the targets have nonuniform polarization characteristics. The core of this method takes the nonuniform distribution of the polarization information of the target-reflected light into account and expands the application field of underwater polarization imaging.Independent component analysis was used to separate the target light and backscattered light. Theoretical analysis and proof-of-concept experiments were employed to demonstrate the effectiveness of the proposed method in estimating target information. The proposed method showed superiority in accurately estimating the target information compared with other polarization imaging methods.
