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
Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects.Despite developments of optical imaging technologies,still the difficulties about deep skin vascu...Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects.Despite developments of optical imaging technologies,still the difficulties about deep skin vascular imaging have been continued.On the other hand,since hemoglobin con-centration of human blood has key role in the veins imaging by optical manner,the used wavelength in vascular imaging,must be chosen considering absorption of hemoglobin.In this research,we constructed a near infrared(NIR)light source because of lower absorption of hemoglobin in this optical region.To obtain vascular image,reflectance geometry was used.Next,from recorded images,vascular network analysis,such as calculation of width of vascular of interest and complexity of selected region were implemented.By comparing with other modalities,we observed that proposed imaging system has great advantages including nonionized radiation,moderate penetration depth of 0.5-3 mm and diameter of 1 mm,cost-effective and algorit hmic simplicity for analysis.展开更多
In recent years,the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms.Traditional methods encounter significant challenges when reconstructing high-dimensi...In recent years,the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms.Traditional methods encounter significant challenges when reconstructing high-dimensional information in complex scenarios[1].By leveraging the powerful nonlinear modeling and advanced feature extraction capabilities of deep learning,these barriers have been effectively overcome,enabling end-to-end optimization—from optical system design to image reconstruction[2].This shift transforms optoelectronic imaging from a conventional“what you see is what you get”model toward a more adaptive“what you see is what you need”approach,catalyzing breakthroughs across diverse applications including optical imaging,medical diagnostics,remote sensing,and beyond.展开更多
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Scientic and Technological Research Council of Turkey(TUBITAK),under grand,No:113E771.
文摘Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects.Despite developments of optical imaging technologies,still the difficulties about deep skin vascular imaging have been continued.On the other hand,since hemoglobin con-centration of human blood has key role in the veins imaging by optical manner,the used wavelength in vascular imaging,must be chosen considering absorption of hemoglobin.In this research,we constructed a near infrared(NIR)light source because of lower absorption of hemoglobin in this optical region.To obtain vascular image,reflectance geometry was used.Next,from recorded images,vascular network analysis,such as calculation of width of vascular of interest and complexity of selected region were implemented.By comparing with other modalities,we observed that proposed imaging system has great advantages including nonionized radiation,moderate penetration depth of 0.5-3 mm and diameter of 1 mm,cost-effective and algorit hmic simplicity for analysis.
文摘In recent years,the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms.Traditional methods encounter significant challenges when reconstructing high-dimensional information in complex scenarios[1].By leveraging the powerful nonlinear modeling and advanced feature extraction capabilities of deep learning,these barriers have been effectively overcome,enabling end-to-end optimization—from optical system design to image reconstruction[2].This shift transforms optoelectronic imaging from a conventional“what you see is what you get”model toward a more adaptive“what you see is what you need”approach,catalyzing breakthroughs across diverse applications including optical imaging,medical diagnostics,remote sensing,and beyond.
