The Experts below are selected from a list of 101169 Experts worldwide ranked by ideXlab platform
Xiaowei Zhuang - One of the best experts on this subject based on the ideXlab platform.
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isotropic three dimensional super resolution imaging with a self bending point Spread Function
Nature Photonics, 2014Co-Authors: Joshua C Vaughan, Xiaowei ZhuangAbstract:By exploiting a self-bending point Spread Function based on Airy beams, a three-dimensional super-resolution fluorescence imaging is realized. A three-dimensional localization precision in the range 10–15 nm was obtained at an imaging depth of 3 µm from ∼2,000 photons per localization.
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isotropic three dimensional super resolution imaging with a self bending point Spread Function
Nature Photonics, 2014Co-Authors: Joshua C Vaughan, Xiaowei Zhuang, Shu JiaAbstract:Airy beams maintain their intensity profiles over a large propagation distance without substantial diffraction and exhibit lateral bending during propagation1,2,3,4,5. This unique property has been exploited for the micromanipulation of particles6, the generation of plasma channels7 and the guidance of plasmonic waves8, but has not been explored for high-resolution optical microscopy. Here, we introduce a self-bending point Spread Function (SB-PSF) based on Airy beams for three-dimensional super-resolution fluorescence imaging. We designed a side-lobe-free SB-PSF and implemented a two-channel detection scheme to enable unambiguous three-dimensional localization of fluorescent molecules. The lack of diffraction and the propagation-dependent lateral bending make the SB-PSF well suited for precise three-dimensional localization of molecules over a large imaging depth. Using this method, we obtained super-resolution imaging with isotropic three-dimensional localization precision of 10–15 nm over a 3 µm imaging depth from ∼2,000 photons per localization. By exploiting a self-bending point Spread Function based on Airy beams, a three-dimensional super-resolution fluorescence imaging is realized. A three-dimensional localization precision in the range 10–15 nm was obtained at an imaging depth of 3 µm from ∼2,000 photons per localization.
Joshua C Vaughan - One of the best experts on this subject based on the ideXlab platform.
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isotropic three dimensional super resolution imaging with a self bending point Spread Function
Nature Photonics, 2014Co-Authors: Joshua C Vaughan, Xiaowei ZhuangAbstract:By exploiting a self-bending point Spread Function based on Airy beams, a three-dimensional super-resolution fluorescence imaging is realized. A three-dimensional localization precision in the range 10–15 nm was obtained at an imaging depth of 3 µm from ∼2,000 photons per localization.
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isotropic three dimensional super resolution imaging with a self bending point Spread Function
Nature Photonics, 2014Co-Authors: Joshua C Vaughan, Xiaowei Zhuang, Shu JiaAbstract:Airy beams maintain their intensity profiles over a large propagation distance without substantial diffraction and exhibit lateral bending during propagation1,2,3,4,5. This unique property has been exploited for the micromanipulation of particles6, the generation of plasma channels7 and the guidance of plasmonic waves8, but has not been explored for high-resolution optical microscopy. Here, we introduce a self-bending point Spread Function (SB-PSF) based on Airy beams for three-dimensional super-resolution fluorescence imaging. We designed a side-lobe-free SB-PSF and implemented a two-channel detection scheme to enable unambiguous three-dimensional localization of fluorescent molecules. The lack of diffraction and the propagation-dependent lateral bending make the SB-PSF well suited for precise three-dimensional localization of molecules over a large imaging depth. Using this method, we obtained super-resolution imaging with isotropic three-dimensional localization precision of 10–15 nm over a 3 µm imaging depth from ∼2,000 photons per localization. By exploiting a self-bending point Spread Function based on Airy beams, a three-dimensional super-resolution fluorescence imaging is realized. A three-dimensional localization precision in the range 10–15 nm was obtained at an imaging depth of 3 µm from ∼2,000 photons per localization.
Dongmei Cai - One of the best experts on this subject based on the ideXlab platform.
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psf net a nonparametric point Spread Function model for ground based optical telescopes
The Astronomical Journal, 2020Co-Authors: Peng Jia, Bojun Cai, Dongmei CaiAbstract:Ground-based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instrument design and image restoration method development. Because the point-Spread Function can reflect performance of the whole optic system, it is appropriate to use the point-Spread Function to describe atmospheric turbulence induced aberrations. Assuming point-Spread Functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a nonparametric point-Spread Function—PSF–NET. The PSF–NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF–NET with simulated and real observation data, we find that a well trained PSF–NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF–NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF–NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF–NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would benefit the development of new observation methods for ground-based optical telescopes.
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Point Spread Function modelling for wide-field small-aperture telescopes with a denoising autoencoder.
Monthly Notices of the Royal Astronomical Society, 2020Co-Authors: Peng Jia, Weinan Wang, Dongmei CaiAbstract:The point Spread Function reflects the state of an optical telescope and it is important for the design of data post-processing methods. For wide-field small-aperture telescopes, the point Spread Function is hard to model because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose the use of a denoising autoencoder, a type of deep neural network, to model the point Spread Function of wide-field small-aperture telescopes. The denoising autoencoder is a point Spread Function modelling method, based on pure data, which uses calibration data from real observations or numerical simulated results as point Spread Function templates. According to real observation conditions, different levels of random noise or aberrations are added to point Spread Function templates, making them realizations of the point Spread Function (i.e. simulated star images). Then we train the denoising autoencoder with realizations and templates of the point Spread Function. After training, the denoising autoencoder learns the manifold space of the point Spread Function and it can map any star images obtained by wide-field small-aperture telescopes directly to its point Spread Function. This could be used to design data post-processing or optical system alignment methods.
Peng Jia - One of the best experts on this subject based on the ideXlab platform.
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psf net a nonparametric point Spread Function model for ground based optical telescopes
The Astronomical Journal, 2020Co-Authors: Peng Jia, Bojun Cai, Dongmei CaiAbstract:Ground-based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instrument design and image restoration method development. Because the point-Spread Function can reflect performance of the whole optic system, it is appropriate to use the point-Spread Function to describe atmospheric turbulence induced aberrations. Assuming point-Spread Functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a nonparametric point-Spread Function—PSF–NET. The PSF–NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF–NET with simulated and real observation data, we find that a well trained PSF–NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF–NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF–NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF–NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would benefit the development of new observation methods for ground-based optical telescopes.
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Point Spread Function modelling for wide-field small-aperture telescopes with a denoising autoencoder.
Monthly Notices of the Royal Astronomical Society, 2020Co-Authors: Peng Jia, Weinan Wang, Dongmei CaiAbstract:The point Spread Function reflects the state of an optical telescope and it is important for the design of data post-processing methods. For wide-field small-aperture telescopes, the point Spread Function is hard to model because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose the use of a denoising autoencoder, a type of deep neural network, to model the point Spread Function of wide-field small-aperture telescopes. The denoising autoencoder is a point Spread Function modelling method, based on pure data, which uses calibration data from real observations or numerical simulated results as point Spread Function templates. According to real observation conditions, different levels of random noise or aberrations are added to point Spread Function templates, making them realizations of the point Spread Function (i.e. simulated star images). Then we train the denoising autoencoder with realizations and templates of the point Spread Function. After training, the denoising autoencoder learns the manifold space of the point Spread Function and it can map any star images obtained by wide-field small-aperture telescopes directly to its point Spread Function. This could be used to design data post-processing or optical system alignment methods.
Shu Jia - One of the best experts on this subject based on the ideXlab platform.
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isotropic three dimensional super resolution imaging with a self bending point Spread Function
Nature Photonics, 2014Co-Authors: Joshua C Vaughan, Xiaowei Zhuang, Shu JiaAbstract:Airy beams maintain their intensity profiles over a large propagation distance without substantial diffraction and exhibit lateral bending during propagation1,2,3,4,5. This unique property has been exploited for the micromanipulation of particles6, the generation of plasma channels7 and the guidance of plasmonic waves8, but has not been explored for high-resolution optical microscopy. Here, we introduce a self-bending point Spread Function (SB-PSF) based on Airy beams for three-dimensional super-resolution fluorescence imaging. We designed a side-lobe-free SB-PSF and implemented a two-channel detection scheme to enable unambiguous three-dimensional localization of fluorescent molecules. The lack of diffraction and the propagation-dependent lateral bending make the SB-PSF well suited for precise three-dimensional localization of molecules over a large imaging depth. Using this method, we obtained super-resolution imaging with isotropic three-dimensional localization precision of 10–15 nm over a 3 µm imaging depth from ∼2,000 photons per localization. By exploiting a self-bending point Spread Function based on Airy beams, a three-dimensional super-resolution fluorescence imaging is realized. A three-dimensional localization precision in the range 10–15 nm was obtained at an imaging depth of 3 µm from ∼2,000 photons per localization.
