A dynamic data updating algorithm for image superesolution is proposed. On the basis of Delaunay triangulation and its local updating property, this algorithm can update the changed region directly under the circumsta...A dynamic data updating algorithm for image superesolution is proposed. On the basis of Delaunay triangulation and its local updating property, this algorithm can update the changed region directly under the circumstances that only a part of the source images has been changed. For its high efficiency and adaptability, this algorithm can serve as a fast algorithm for image superesolution reconstruction.展开更多
Superresolution is an image processing technique that estimates an original high-resolutionimage from its low-resolution and degraded observations.In superresolution tasks,there have beenproblems regarding the computa...Superresolution is an image processing technique that estimates an original high-resolutionimage from its low-resolution and degraded observations.In superresolution tasks,there have beenproblems regarding the computational cost for the estimation of high-dimensional variables.Theseproblems are now being overcome by the recent development of fast computers and the developmentof powerful computational techniques such as variational Bayesian approximation.This paper reviewsa Bayesian treatment of the superresolution problem and presents its extensions based on hierarchicalmodeling by employing hidden variables.展开更多
Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on e...Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band.However,they fail to adaptively correct the nonuniform noise in IRLF images,resulting in over-smoothness or artifacts in their results.This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity.The main idea is to decompose the structure and nonuniform noise into high-and low-frequency components and then learn the frequency correlations to help correct the nonuniformity.To learn the frequency correlation,intra-and inter-frequency units are designed.The former learns the correlation of neighboring pixels within each component,aiming to reconstruct the structure and coarsely remove nonuniform noise.The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise.Both units are equipped with our designed triple-attention mechanism,which can jointly exploit spatial,angular,and frequency information.Moreover,we collected two real-world IRLF-image datasets with significant nonuniformity,which can be used as a common base in the field.Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts.The code is available at https://github.com/DuYou2023/IRLF-FSR.展开更多
Terahertz(THz)technology offers novel opportunities in biology and medicine,thanks to the unique features of THzwave interactions with tissues and cells.Among them,we particularly notice strong sensitivity of THz wave...Terahertz(THz)technology offers novel opportunities in biology and medicine,thanks to the unique features of THzwave interactions with tissues and cells.Among them,we particularly notice strong sensitivity of THz waves to the tissue water,as a medium for biochemical reactions and a main endogenous marker for THz spectroscopy and imaging.Tissues of the brain have an exceptionally high content of water.This factor,along with the features of the structural organization and biochemistry of neuronal and glial tissues,makes the brain an exciting subject to study in the THz range.In this paper,progress and prospects of THz technology in neurodiagnostics are overviewed,including diagnosis of neurodegenerative disease,myelin deficit,tumors of the central nervous system(with an emphasis on brain gliomas),and traumatic brain injuries.Fundamental and applied challenges in study of the THz-wave–brain tissue interactions and development of the THz biomedical tools and systems for neurodiagnostics are discussed.展开更多
Structured illumination microscopy(SIM)has been widely applied in the superresolution imaging of subcellular dynamics in live cells.Higher spatial resolution is expected for the observation of finer structures.However...Structured illumination microscopy(SIM)has been widely applied in the superresolution imaging of subcellular dynamics in live cells.Higher spatial resolution is expected for the observation of finer structures.However,further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging.Here,we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers(ADMM)framework,i.e.,ADMM-DRE-SIM.By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM,ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets.Moreover,an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity.Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of∼1.6 compared to conventional SIM,evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.展开更多
Single-molecule localization microscopy(SMLM)provides nanoscale imaging,but pixel integration of acquired SMLM images limited the choice of sampling rate,which restricts the information content conveyed within each im...Single-molecule localization microscopy(SMLM)provides nanoscale imaging,but pixel integration of acquired SMLM images limited the choice of sampling rate,which restricts the information content conveyed within each image.We propose an upsampled point spread function(PSF)inverse modeling method for large-pixel singlemolecule localization,enabling precise three-dimensional superresolution imaging with a sparse sampling rate.展开更多
Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultan...Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features.This challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model sampling.To address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images.We then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising distribution.Doing so boosts the generative modeling capabilities within a minimal number of sampling steps.Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.展开更多
DNA tetrahedral nanostructures are considered to be uew nanocarriers because they can be precisely controlled and hold excellent penetration ability to the cellular membrane. Although the DNA tetrahedral nanostructure...DNA tetrahedral nanostructures are considered to be uew nanocarriers because they can be precisely controlled and hold excellent penetration ability to the cellular membrane. Although the DNA tetrahedral nanostructure is extensively studied in biology and medicine, its behavior in the cells with nanoscale resolution is not understood clearly. In this letter, we demonstrate superrcsolution fluorescence imaging of the distribution of DNA tetrahedral nanostructures in the cell with a simulated emission depletion （STED） microscope, which is built based on a conventional eonfocal microscope and can t）rovide a resolution of 70 nm.展开更多
Optical superoscillation enables far-field superresolution imaging beyond diffraction limits.However,existing superoscillatory lenses for spatial superresolution imaging systems still confront critical performance lim...Optical superoscillation enables far-field superresolution imaging beyond diffraction limits.However,existing superoscillatory lenses for spatial superresolution imaging systems still confront critical performance limitations due to the lack of advanced design methods and limited design degree of freedom.Here,we propose an optical superoscillatory diffractive neural network(SODNN)that achieves spatial superresolution for imaging beyond the diffraction limit with superior optical performance.SODNN is constructed by utilizing diffractive layers for optical interconnections and imaging samples or biological sensors for nonlinearity.This modulates the incident optical field to create optical superoscillation effects in three-dimensional(3D)space and generate the superresolved focal spots.By optimizing diffractive layers with 3D optical field constraints under an incident wavelength size ofλ,we achieved a superoscillatory optical spot and needle with a full width at half-maximum of 0.407λat the far-field distance over 400λwithout sidelobes over the field of view and with a long depth of field over 10λ.Furthermore,the SODNN implements a multiwavelength and multifocus spot array that effectively avoids chromatic aberrations,achieving comprehensive performance improvement that surpasses the trade-off among performance indicators of conventional superoscillatory lens design methods.Our research work will inspire the development of intelligent optical instruments to facilitate the applications of imaging,sensing,perception,etc.展开更多
The significant role of telomeres in cells has attracted much attention since they were discovered.Fluorescence imaging is an effective method to study subcellular structures like telomeres.However,the diffraction lim...The significant role of telomeres in cells has attracted much attention since they were discovered.Fluorescence imaging is an effective method to study subcellular structures like telomeres.However,the diffraction limit of traditional optical microscope hampers further investigation on them.Recent progress on superresolution fluorescence microscopy has broken this limit.In this work,we used stimulated emission depletion(STED) microscope to observe fluorescence-labeled telomeres in interphase cell nuclei.The results showed that the size of fluorescent puncta representing telomeres under the STED microscope was much smaller than that under the confocal microscope.Two adjacent telomeres were clearly separated via STED imaging,which could hardly be discriminated by confocal microscopy due to the diffraction limit.We conclude that STED microscope is a more powerful tool that enable us to obtain detailed information about telomeres.展开更多
Hypomyelination leukodystrophies constitute a group of heritable white matter disorders exhibiting defective myelin development.Initially identified as a lysosomal protein,the TMEM106B D252N mutant has recently been a...Hypomyelination leukodystrophies constitute a group of heritable white matter disorders exhibiting defective myelin development.Initially identified as a lysosomal protein,the TMEM106B D252N mutant has recently been associated with hypomyelination.However,how lysosomal TMEM106B facilitates myelination and how the D252N mutation disrupts that process are poorly understood.We used superresolution Hessian structured illumination microscopy(Hessian-SIM)and spinning discconfocal structured illumination microscopy(SD-SIM)to find that the wild-type TMEM106B protein is targeted to the plasma membrane,filopodia,and lysosomes in human oligodendrocytes.The D252N mutation reduces the size of lysosomes in oligodendrocytes and compromises lysosome changes upon starvation stress.Most importantly,we detected reductions in the length and number of filopodia in cells expressing the D252N mutant.PLP1 is the most abundant myelin protein that almost entirely colocalizes with TMEM106B,and coexpressing PLP1 with the D252N mutant readily rescues the lysosome and filopodia phenotypes of cells.Therefore,interactions between TMEM106B and PLP1 on the plasma membrane are essential for filopodia formation and myelination in oligodendrocytes,which may be sustained by the delivery of these proteins from lysosomes via exocytosis.展开更多
Minimal photon fluxes(MINFLUX)nanoscopy has emerged as a transformative advancement in superresolution imaging,enabling unprecedented nanoscale observations across diverse biological scenarios.In this work,we propose,...Minimal photon fluxes(MINFLUX)nanoscopy has emerged as a transformative advancement in superresolution imaging,enabling unprecedented nanoscale observations across diverse biological scenarios.In this work,we propose,for the first time,that employing high-order vortex beams can significantly enhance the performance of MINFLUX,surpassing the limitations of the conventional MINFLUX using the first-order vortex beam.Our theoretical analysis indicates that,for standard MINFLUX,high-order vortex beams can improve the maximum localization precision by a factor corresponding to their order,which can approach a sub-nanometer scale under optimal conditions,and for raster scan MINFLUX,high-order vortex beams allow for a wider field of view while maintaining enhanced precision.These findings underscore the potential of high-order vortex beams to elevate the performance of MINFLUX,paving the way towards ultra-high resolution imaging for a broad range of applications.展开更多
The laser-induced damage detection images used in high-power laser facilities have a dark background,few textures with sparse and small-sized damage sites,and slight degradation caused by slight defocus and optical di...The laser-induced damage detection images used in high-power laser facilities have a dark background,few textures with sparse and small-sized damage sites,and slight degradation caused by slight defocus and optical diffraction,which make the image superresolution(SR)reconstruction challenging.We propose a non-blind SR reconstruction method by using an exquisite mixing of high-,intermediate-,and low-frequency information at each stage of pixel reconstruction based on UNet.We simplify the channel attention mechanism and activation function to focus on the useful channels and keep the global information in the features.We pay more attention on the damage area in the loss function of our end-toend deep neural network.For constructing a high-low resolution image pairs data set,we precisely measure the point spread function(PSF)of a low-resolution imaging system by using a Bernoulli calibration pattern;the influence of different distance and lateral position on PSFs is also considered.A high-resolution camera is used to acquire the ground-truth images,which is used to create a low-resolution image pairs data set by convolving with the measured PSFs.Trained on the data set,our network has achieved better results,which proves the effectiveness of our method.展开更多
文摘A dynamic data updating algorithm for image superesolution is proposed. On the basis of Delaunay triangulation and its local updating property, this algorithm can update the changed region directly under the circumstances that only a part of the source images has been changed. For its high efficiency and adaptability, this algorithm can serve as a fast algorithm for image superesolution reconstruction.
文摘Superresolution is an image processing technique that estimates an original high-resolutionimage from its low-resolution and degraded observations.In superresolution tasks,there have beenproblems regarding the computational cost for the estimation of high-dimensional variables.Theseproblems are now being overcome by the recent development of fast computers and the developmentof powerful computational techniques such as variational Bayesian approximation.This paper reviewsa Bayesian treatment of the superresolution problem and presents its extensions based on hierarchicalmodeling by employing hidden variables.
基金supported by the National Natural Science Foundation of China(62475199,62075169,U23B2050)the Industry University-Research Cooperation Program of Zhuhai(2220004002828).
文摘Super-resolution(SR)for the camera array-based infrared light field(IRLF)images aims to reconstruct high-resolution sub-aperture images(SAIs)from their low-resolution counterparts.Existing SR methods mainly focus on exploiting the spatial and angular information of SAIs and have achieved promising results in the visible band.However,they fail to adaptively correct the nonuniform noise in IRLF images,resulting in over-smoothness or artifacts in their results.This study proposes a novel method that reconstructs high-resolution IRLF images while correcting the nonuniformity.The main idea is to decompose the structure and nonuniform noise into high-and low-frequency components and then learn the frequency correlations to help correct the nonuniformity.To learn the frequency correlation,intra-and inter-frequency units are designed.The former learns the correlation of neighboring pixels within each component,aiming to reconstruct the structure and coarsely remove nonuniform noise.The latter models the correlation of contents between different components to reconstruct fine-grained structures and reduce residual noise.Both units are equipped with our designed triple-attention mechanism,which can jointly exploit spatial,angular,and frequency information.Moreover,we collected two real-world IRLF-image datasets with significant nonuniformity,which can be used as a common base in the field.Qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art approaches with a clearer structure and fewer artifacts.The code is available at https://github.com/DuYou2023/IRLF-FSR.
基金The work was supported by the Russian Science Foundation,Project#22-22-00596.
文摘Terahertz(THz)technology offers novel opportunities in biology and medicine,thanks to the unique features of THzwave interactions with tissues and cells.Among them,we particularly notice strong sensitivity of THz waves to the tissue water,as a medium for biochemical reactions and a main endogenous marker for THz spectroscopy and imaging.Tissues of the brain have an exceptionally high content of water.This factor,along with the features of the structural organization and biochemistry of neuronal and glial tissues,makes the brain an exciting subject to study in the THz range.In this paper,progress and prospects of THz technology in neurodiagnostics are overviewed,including diagnosis of neurodegenerative disease,myelin deficit,tumors of the central nervous system(with an emphasis on brain gliomas),and traumatic brain injuries.Fundamental and applied challenges in study of the THz-wave–brain tissue interactions and development of the THz biomedical tools and systems for neurodiagnostics are discussed.
基金supported by the National Natural Science Foundation of China(Grant Nos.12274129,12274139,12074121,92150301,62105101,62175066,and 12034008)the Science and Technology Commission of Shanghai Municipality(Grant Nos.21XD1400900,20ZR1417100,and 21JM0010700).
文摘Structured illumination microscopy(SIM)has been widely applied in the superresolution imaging of subcellular dynamics in live cells.Higher spatial resolution is expected for the observation of finer structures.However,further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging.Here,we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers(ADMM)framework,i.e.,ADMM-DRE-SIM.By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM,ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets.Moreover,an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity.Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of∼1.6 compared to conventional SIM,evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.
基金National Key Research and Development Program of China(2024YFF0726003)Shenzhen Medical Research Fund(B2302038)+8 种基金National Natural Science Foundationof China(62375116)Key,Technology Research and Development Program of Shandong Province(2021CXGC010212)Shenzhen Science and Technology Innovation Program JCYJ20220818100416036,KQTD20200820113012029)BasicandApplied Basic Research Foundation of Guangdong Province(2024A1515011565)Postdoctoral Fellowship Program of CPSF(GZC20240651)GuangdongProvincial Key Laboratory of Advanced Biomaterials(2022B1212010003)Startup Grant from Southern University of Science and TechnologyCenter for Computational Science and Engineering of Southern University of Science and Technology。
文摘Single-molecule localization microscopy(SMLM)provides nanoscale imaging,but pixel integration of acquired SMLM images limited the choice of sampling rate,which restricts the information content conveyed within each image.We propose an upsampled point spread function(PSF)inverse modeling method for large-pixel singlemolecule localization,enabling precise three-dimensional superresolution imaging with a sparse sampling rate.
基金supported by General Project of Guangxi Science and Technology Major Project(AA19254016)Beihai City Science and Technology Planning Project(202082033)+1 种基金Beihai City Science and Technology Planning Project(202082023)Guangxi Graduate Student Innovation Project(YCSW2021174)。
文摘Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features.This challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model sampling.To address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images.We then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising distribution.Doing so boosts the generative modeling capabilities within a minimal number of sampling steps.Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.
基金supported by the National Natural Science Foundation of China under Grand Nos.61008056,21227804,61078016,and 61378062)
文摘DNA tetrahedral nanostructures are considered to be uew nanocarriers because they can be precisely controlled and hold excellent penetration ability to the cellular membrane. Although the DNA tetrahedral nanostructure is extensively studied in biology and medicine, its behavior in the cells with nanoscale resolution is not understood clearly. In this letter, we demonstrate superrcsolution fluorescence imaging of the distribution of DNA tetrahedral nanostructures in the cell with a simulated emission depletion （STED） microscope, which is built based on a conventional eonfocal microscope and can t）rovide a resolution of 70 nm.
基金supported by the National Key Research and Development Program of China(Grant No.2021ZD0109902)the National Natural Science Foundation of China(Grant No.62275139)the China Postdoctoral Science Foundation(Grant No.2023M741889).
文摘Optical superoscillation enables far-field superresolution imaging beyond diffraction limits.However,existing superoscillatory lenses for spatial superresolution imaging systems still confront critical performance limitations due to the lack of advanced design methods and limited design degree of freedom.Here,we propose an optical superoscillatory diffractive neural network(SODNN)that achieves spatial superresolution for imaging beyond the diffraction limit with superior optical performance.SODNN is constructed by utilizing diffractive layers for optical interconnections and imaging samples or biological sensors for nonlinearity.This modulates the incident optical field to create optical superoscillation effects in three-dimensional(3D)space and generate the superresolved focal spots.By optimizing diffractive layers with 3D optical field constraints under an incident wavelength size ofλ,we achieved a superoscillatory optical spot and needle with a full width at half-maximum of 0.407λat the far-field distance over 400λwithout sidelobes over the field of view and with a long depth of field over 10λ.Furthermore,the SODNN implements a multiwavelength and multifocus spot array that effectively avoids chromatic aberrations,achieving comprehensive performance improvement that surpasses the trade-off among performance indicators of conventional superoscillatory lens design methods.Our research work will inspire the development of intelligent optical instruments to facilitate the applications of imaging,sensing,perception,etc.
基金supported by the National Natural Science Foundation of China(61378062,21227804,21390414,61475181)the National Basic ResearchProgram of China(2012CB825805)the Shanghai Municipal Commission for Science and Technology(14ZR1448000)
文摘The significant role of telomeres in cells has attracted much attention since they were discovered.Fluorescence imaging is an effective method to study subcellular structures like telomeres.However,the diffraction limit of traditional optical microscope hampers further investigation on them.Recent progress on superresolution fluorescence microscopy has broken this limit.In this work,we used stimulated emission depletion(STED) microscope to observe fluorescence-labeled telomeres in interphase cell nuclei.The results showed that the size of fluorescent puncta representing telomeres under the STED microscope was much smaller than that under the confocal microscope.Two adjacent telomeres were clearly separated via STED imaging,which could hardly be discriminated by confocal microscopy due to the diffraction limit.We conclude that STED microscope is a more powerful tool that enable us to obtain detailed information about telomeres.
基金supported by the National Natural Science Foundation of China(81925022,61827825,32227802,92054301)the Fundamental Research Center Project of the National Natural Science Foundation of China(T2288102)+4 种基金the National Science and Technology Major Project Program(2022YFC3400600)Beijing Natural Science Foundation Key Research Topics(Z20J00059)UMHS-PUHSC Joint Institute for Translational and Clinical Research(BMU2019JI009)Beijing Key Laboratory of Molecular Diagnosis and Study on Pediatric Genetic Diseases(BZ0317)China Postdoctoral Science Foundation(2021M690465)。
文摘Hypomyelination leukodystrophies constitute a group of heritable white matter disorders exhibiting defective myelin development.Initially identified as a lysosomal protein,the TMEM106B D252N mutant has recently been associated with hypomyelination.However,how lysosomal TMEM106B facilitates myelination and how the D252N mutation disrupts that process are poorly understood.We used superresolution Hessian structured illumination microscopy(Hessian-SIM)and spinning discconfocal structured illumination microscopy(SD-SIM)to find that the wild-type TMEM106B protein is targeted to the plasma membrane,filopodia,and lysosomes in human oligodendrocytes.The D252N mutation reduces the size of lysosomes in oligodendrocytes and compromises lysosome changes upon starvation stress.Most importantly,we detected reductions in the length and number of filopodia in cells expressing the D252N mutant.PLP1 is the most abundant myelin protein that almost entirely colocalizes with TMEM106B,and coexpressing PLP1 with the D252N mutant readily rescues the lysosome and filopodia phenotypes of cells.Therefore,interactions between TMEM106B and PLP1 on the plasma membrane are essential for filopodia formation and myelination in oligodendrocytes,which may be sustained by the delivery of these proteins from lysosomes via exocytosis.
基金supported in part by the Academic Research Fund(AcRF)-Tier 2(A-8000117-01-00)and Tier 1(A-8003279-00-00)from the Ministry of Education(MOE)of Singapore,Science and Technology Project of Jiangsu Province(BZ2022056),NUS(Suzhou)Research Institute/Biomedical and Health Technology Platform,2024 Tsinghua-NUS Joint Research Initiative Fund(A-8002557-00-00)the National Medical Research Council(NMRC)(A-0009502-01-00,and A-8001143-00-00),Singapore.
文摘Minimal photon fluxes(MINFLUX)nanoscopy has emerged as a transformative advancement in superresolution imaging,enabling unprecedented nanoscale observations across diverse biological scenarios.In this work,we propose,for the first time,that employing high-order vortex beams can significantly enhance the performance of MINFLUX,surpassing the limitations of the conventional MINFLUX using the first-order vortex beam.Our theoretical analysis indicates that,for standard MINFLUX,high-order vortex beams can improve the maximum localization precision by a factor corresponding to their order,which can approach a sub-nanometer scale under optimal conditions,and for raster scan MINFLUX,high-order vortex beams allow for a wider field of view while maintaining enhanced precision.These findings underscore the potential of high-order vortex beams to elevate the performance of MINFLUX,paving the way towards ultra-high resolution imaging for a broad range of applications.
文摘The laser-induced damage detection images used in high-power laser facilities have a dark background,few textures with sparse and small-sized damage sites,and slight degradation caused by slight defocus and optical diffraction,which make the image superresolution(SR)reconstruction challenging.We propose a non-blind SR reconstruction method by using an exquisite mixing of high-,intermediate-,and low-frequency information at each stage of pixel reconstruction based on UNet.We simplify the channel attention mechanism and activation function to focus on the useful channels and keep the global information in the features.We pay more attention on the damage area in the loss function of our end-toend deep neural network.For constructing a high-low resolution image pairs data set,we precisely measure the point spread function(PSF)of a low-resolution imaging system by using a Bernoulli calibration pattern;the influence of different distance and lateral position on PSFs is also considered.A high-resolution camera is used to acquire the ground-truth images,which is used to create a low-resolution image pairs data set by convolving with the measured PSFs.Trained on the data set,our network has achieved better results,which proves the effectiveness of our method.
