Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction...Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.展开更多
Ultrafast imaging is key for the real-time visualization of many transient events in physics,chemistry,and biology.The past decade has witnessed the blossom of new theories and technologies that have substantially pro...Ultrafast imaging is key for the real-time visualization of many transient events in physics,chemistry,and biology.The past decade has witnessed the blossom of new theories and technologies that have substantially propelled ultrafast imaging.The newly developed ultrafast imaging systems,in turn,have enabled unprecedented applications in both fundamental and applied sciences that unveil many new scientific discoveries ranging from carrier dynamics to brain functions.To date,ultrafast imaging marks an active frontier in both research and innovation.展开更多
基金supported by National Natural Science Foundation of China(12402336,82201637,U20A2070,and 12025202)National High-Level Talent Project(YQR23069)+6 种基金Natural Science Foundation of Jiangsu Province(BK20230876)the Young Elite Scientist Sponsorship Program by CAST(YESS20210238)Forwardlooking layout projects(1002-ILB24009)Zhejang Provincial Medical and Health Technology Project(Grant No.2024KY246,2025KY180)Scientific Research Foundation of Hangzhou City University(No.J-202402)Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence,Zhejiang University(Grant No.BMI2400025)Hangzhou Science and Technology Bureau.
文摘Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.
文摘Ultrafast imaging is key for the real-time visualization of many transient events in physics,chemistry,and biology.The past decade has witnessed the blossom of new theories and technologies that have substantially propelled ultrafast imaging.The newly developed ultrafast imaging systems,in turn,have enabled unprecedented applications in both fundamental and applied sciences that unveil many new scientific discoveries ranging from carrier dynamics to brain functions.To date,ultrafast imaging marks an active frontier in both research and innovation.
