Framing photography provides a high temporal resolution and minimizes crosstalk between adjacent frames,making it an indispensable tool for recording ultrafast phenomena.To date,various ultrafast framing photography t...Framing photography provides a high temporal resolution and minimizes crosstalk between adjacent frames,making it an indispensable tool for recording ultrafast phenomena.To date,various ultrafast framing photography techniques have been developed.However,simultaneously achieving large sequence depth,high image quality,ultrashort exposure time,and flexible frame interval remains a significant challenge.Herein,we present a spatiotemporal shearing-based ultrafast framing photography,termed STS-UFP,designed to address this challenge.STS-UFP employs an adjustable ultrashort laser pulse train with a spectrum shuttle to illuminate the dynamic scenes for extracting the transient information and records discrete frames using a streak camera via spatiotemporal shearing.Based on its unique design,STS-UFP achieves high-quality ultrafast imaging with a sequence depth of up to 16 frames and frame intervals ranging from hundreds of picoseconds to nanoseconds,while maintaining an extremely short(picosecond)exposure time.The exceptional performance of STS-UFP is demonstrated through experimental observations of femtosecond laser-induced plasma and shockwave in water,femtosecond laser ablation in biological tissue,and femtosecond laser-induced shockwave on a silicon surface.Given its remarkable imaging capabilities,STS-UFP serves as a powerful tool for precisely observing ultrafast dynamics and holds significant potential for advancing studies of ultrafast phenomena.展开更多
Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facil...Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research,high-signal-to-noise ratio(SNR)images are indispensable.However,the inevitable noise presents a significant barrier to imaging qualities.Here,we propose SelfMirror,a self-supervised deep-learning denoising method for volumetric image reconstruction.SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth;when the sampling interval in volumetric imaging is sufficiently small,the similarity of neighboring slices in terms of the spatial structure becomes apparent.Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately.The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions.We demonstrate the broad applicability of SelfMirror on multiple imaging modalities,including two-photon microscopy,confocal microscopy,expansion microscopy,computed tomography,and 3D electron microscopy.This versatility extends from single neuron cells to tissues and organs,highlighting SelfMirror's potential for integration into diverse imaging and analysis pipelines.展开更多
基金National Natural Science Foundation of China(12325408,12274129,12274139,12374274,62175066,92150102,12474404,12304338,12471368,62475070)Fundamental Research Funds for the Central Universities。
文摘Framing photography provides a high temporal resolution and minimizes crosstalk between adjacent frames,making it an indispensable tool for recording ultrafast phenomena.To date,various ultrafast framing photography techniques have been developed.However,simultaneously achieving large sequence depth,high image quality,ultrashort exposure time,and flexible frame interval remains a significant challenge.Herein,we present a spatiotemporal shearing-based ultrafast framing photography,termed STS-UFP,designed to address this challenge.STS-UFP employs an adjustable ultrashort laser pulse train with a spectrum shuttle to illuminate the dynamic scenes for extracting the transient information and records discrete frames using a streak camera via spatiotemporal shearing.Based on its unique design,STS-UFP achieves high-quality ultrafast imaging with a sequence depth of up to 16 frames and frame intervals ranging from hundreds of picoseconds to nanoseconds,while maintaining an extremely short(picosecond)exposure time.The exceptional performance of STS-UFP is demonstrated through experimental observations of femtosecond laser-induced plasma and shockwave in water,femtosecond laser ablation in biological tissue,and femtosecond laser-induced shockwave on a silicon surface.Given its remarkable imaging capabilities,STS-UFP serves as a powerful tool for precisely observing ultrafast dynamics and holds significant potential for advancing studies of ultrafast phenomena.
基金National Natural Science Foundation of China(62027812,62333012)。
文摘Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research,high-signal-to-noise ratio(SNR)images are indispensable.However,the inevitable noise presents a significant barrier to imaging qualities.Here,we propose SelfMirror,a self-supervised deep-learning denoising method for volumetric image reconstruction.SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth;when the sampling interval in volumetric imaging is sufficiently small,the similarity of neighboring slices in terms of the spatial structure becomes apparent.Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately.The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions.We demonstrate the broad applicability of SelfMirror on multiple imaging modalities,including two-photon microscopy,confocal microscopy,expansion microscopy,computed tomography,and 3D electron microscopy.This versatility extends from single neuron cells to tissues and organs,highlighting SelfMirror's potential for integration into diverse imaging and analysis pipelines.
