Modern spectral estimation techniques (superresolution in technical jargon) have been applied to many fields of signal processing since many years[1][2]. Application to radar imaging, mainlyto ISAR (Inverse Synthetic ...Modern spectral estimation techniques (superresolution in technical jargon) have been applied to many fields of signal processing since many years[1][2]. Application to radar imaging, mainlyto ISAR (Inverse Synthetic Aperture Radar) is documented in some recent papers[3] to [6]. Applications have been attempted also to SAR (Synthetic Aperture Radar)[7][8]. In these fields the benefit ofspectral estimation reveals in a resolution beyond the Rayleigh limits set by compressed pulse andsynthetic aperture lengths. Furthermore very low sidelobes of point scatterer response are obtained.In this paper superresolution has been applied both to simulated stepped-frequency ISAR dataand to real ERS-1 SAR data; the achieved results are encouraging and suggest a more extensivepractical application of the technique. The paper is organized in two parts. In the first we have applied the autoregressive (AR) and the minimum variance (MV)-Capon methods to improve therange resolution of simulated ISAR data. In the second part we have conceived an upgraded versionof spectral analysis (SPECAN) processing to obtain a SAR image of better quality. The method hasbeen tested on recorded live data of ERS-1 mission.展开更多
An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated b...An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.展开更多
Structured illumination microscopy(SIM)is an established optical superresolution imaging technique.However,conventional SIM based on wide-field image acquisition is generally limited to visualizing thin cellular sampl...Structured illumination microscopy(SIM)is an established optical superresolution imaging technique.However,conventional SIM based on wide-field image acquisition is generally limited to visualizing thin cellular samples.We propose combining one-dimensional image rescan and structured illumination in the orthogonal direction to achieve superresolution without the need to rotate the illumination pattern.The image acquisition speed is consequently improved threefold,which is also beneficial for minimizing photobleaching and phototoxicity.Optical sectioning in thick biological tissue is enhanced by including a confocal slit in the system to significantly suppress the out-of-focus background and the associated noise.With all the technical improvements,our method captures threedimensional superresolved image stacks of neuronal structures in mouse brain tissue samples for a depth range of more than 200μm.展开更多
This paper investigates the influences of phase shift on superresolution performances of annular filters. Firstly, it investigates the influence of phase shift on axial superresolution. It proves theoretically that ax...This paper investigates the influences of phase shift on superresolution performances of annular filters. Firstly, it investigates the influence of phase shift on axial superresolution. It proves theoretically that axial superresolution can not be obtained by two-zone phase filter with phase shift n, and it gets the phase shift with which axial superresolution can be brought by two-zone phase filter. Secondly, it studies the influence of phase shift on transverse superresolution. It finds that the three-zone phase filter with arbitrary phase shift has an almost equal optimal transverse gain to that of commonly used three-zone phase filter, but can produce a much higher axial superresolution gain. Thirdly, it investigates the influence of phase shift on three-dimensional superresolution. Three-dimensional superresolution capability and design margin of three-zone complex filter with arbitrary phase shift are obtained, which presents the theoretical basis for three-dimensional superresolution design. Finally, it investigates the influence of phase shift on focal shift. To obtain desired focal shifts, it designs a series of three-zone phase filters with different phase shifts. A spatial light modulator （SLM） is used to implement the designed filters. By regulating the voltage imposed on the SLM, an accurate focal shift control is obtained,展开更多
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
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
In this work,we achieve a fourfold enhancement in thermo-optic coefficient measurement resolution for KTiOPO_(4) crystal using a self-stabilized birefringence interferometer integrated with cascaded second-harmonic ge...In this work,we achieve a fourfold enhancement in thermo-optic coefficient measurement resolution for KTiOPO_(4) crystal using a self-stabilized birefringence interferometer integrated with cascaded second-harmonic generation.We observe the tunable interference beating phenomenon by rotating a birefringent crystal versus the temperature of the crystal.Furthermore,the fourth-harmonic interference fringes beat 4 times faster than the fundamental wave interference fringes.This beating effect is used to determine the thermo-optic coefficients of the two principal refractive axes with a single measurement.This work provides a feasible,real-time,and robust method for superresolution measurements based on birefringence interferometry.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
Acquiring high-resolution light fields(LFs)is expensive.LF angular superresolution aims to synthesize the required number of views from a given sparse set of spatially high-resolution images.Existing methods struggle ...Acquiring high-resolution light fields(LFs)is expensive.LF angular superresolution aims to synthesize the required number of views from a given sparse set of spatially high-resolution images.Existing methods struggle with sparsely sampled LFs captured with large baselines.Some methods rely on depth estimation and view reprojection,and are sensitive to textureless and occluded regions.Other non-depth based methods suffer from aliasing or blurring effects due to the large disparity.In addition,most methods require specific models for different interpolation rates,which reduces their flexibility in practice.In this paper,we propose a learning framework that overcomes these challenges by exploiting the global and local structures of LFs.Our framework includes aggregation across both the angular and spatial dimensions to fully exploit the input data and a novel bilateral upsampling module that upsamples each epipolar plane image while better preserving its local parallax structure.Furthermore,our method predicts the weights of the interpolation filters based on both subpixel offset and range difference,allowing angular superresolution at different rates with a single model.We show that our non-depth based method outperforms the state-of-the-art methods in terms of handling large disparities and flexibility on both real-world and synthetic LF images.展开更多
Because of the fingerprint-like specificity of its characteristic spectrogram, Raman spectral imaging has been applied widely in various research areas. Using a combination of structured illumination with the surface-...Because of the fingerprint-like specificity of its characteristic spectrogram, Raman spectral imaging has been applied widely in various research areas. Using a combination of structured illumination with the surface- enhanced Raman scattering （SERS） technique, wide-field Raman imaging is developed with a significant improve- ment in spatial resolution. As a result of the relatively narrow Raman characteristic peaks, optically encoded SERS nanoparticles can be used to perform multiplexed imaging. The results show excellent superresolution wide-fidd multiplexed imaging performance. The developed technique has extraordinary potential for applications in biological imaging and other related fields.展开更多
Using a strong nonlinear saturation absorption effect is one technique for breaking through the diffraction limit. In this technique, formation of a dynamic and reversible optical pinhole channel and transient superre...Using a strong nonlinear saturation absorption effect is one technique for breaking through the diffraction limit. In this technique, formation of a dynamic and reversible optical pinhole channel and transient superresolution is critical. In this work, a pump–probe transient detection and observation–experimental setup is constructed to explore the formation process directly. A Ge2Sb2Te5 thin film with strong nonlinear saturation absorption is investigated. The dynamic evolution of the optical pinhole channel is detected and imaged, and the transient superresolution spot is directly captured experimentally. Results verify that the superresolution effect originates from the generation of an optical pinhole channel and that the formation of the optical pinhole channel is dynamic and reversible. A good method is provided for direct detection and observation of the transient process of the superresolution effect of nonlinear thin films.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
Visualization of the spatiotemporal organization of chromatin is highly desirable in the study of genome function regulations.Clustered regularly interspa-ced short palindromic repeats(CRISPR)/CRISPR-associated endonu...Visualization of the spatiotemporal organization of chromatin is highly desirable in the study of genome function regulations.Clustered regularly interspa-ced short palindromic repeats(CRISPR)/CRISPR-associated endonuclease system has shown great promise for application in real-time chromatin imag-ing due to its DNA targeting ability in living cells.Previous studies typically used fluorescent proteins to generate fluorescent signals which,however,have trade-offs among signal intensity,multiplexibility,and simplicity.展开更多
Several pupil filtering techniques have been developed in the last few years to obtain transverse superresolution （a narrower point spread function core）. Such a core decrease entails two relevant limitations： a de...Several pupil filtering techniques have been developed in the last few years to obtain transverse superresolution （a narrower point spread function core）. Such a core decrease entails two relevant limitations： a decrease of the peak intensity and an increase of the sidelobe intensity. Here, we calculate the Strehl ratio as a function of the core size for the most used binary phase filters. Furthermore, we show that this relation approaches the fundamental limit of the attainable Strehl ratio at the focal plane for any filter. Finally, we show the calculation of the peak-to-sidelobe ratio in order to check the system viability in every application.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
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
To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on...To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.展开更多
The superresolution(SR)method based on generative adversarial networks(GANs)cannot adequately capture enough diversity from training data,resulting in misalignment between input low resolution(LR)images and output hig...The superresolution(SR)method based on generative adversarial networks(GANs)cannot adequately capture enough diversity from training data,resulting in misalignment between input low resolution(LR)images and output high resolution(HR)images.GAN training has difficulty converging.Based on this,an advanced GAN-based image SR reconstructionmethod is presented.First,the dense connection residual block and attention mechanism are integrated into the GAN generator to improve high-frequency feature extraction.Meanwhile,an added discriminator is added into the GAN discriminant network,which forms a dual discriminator to ensure that the process of training is stable.Second,the more robust Charbonnier loss is used instead of the mean square error(MSE)loss to compare similarities between the obtained image and actual image,and the total variation(TV)loss is employed to smooth the training results.Finally,the experimental results indicate that global structures can be better reconstructed using the method of this paper and texture details of images compared with other SOTA methods.The peak signal-to-noise ratio(PSNR)values by the method of this paper are improved by an average of 2.24 dB,and the structural similarity index measure(SSIM)values are improved by an average of 0.07.展开更多
In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,w...In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.展开更多
文摘Modern spectral estimation techniques (superresolution in technical jargon) have been applied to many fields of signal processing since many years[1][2]. Application to radar imaging, mainlyto ISAR (Inverse Synthetic Aperture Radar) is documented in some recent papers[3] to [6]. Applications have been attempted also to SAR (Synthetic Aperture Radar)[7][8]. In these fields the benefit ofspectral estimation reveals in a resolution beyond the Rayleigh limits set by compressed pulse andsynthetic aperture lengths. Furthermore very low sidelobes of point scatterer response are obtained.In this paper superresolution has been applied both to simulated stepped-frequency ISAR dataand to real ERS-1 SAR data; the achieved results are encouraging and suggest a more extensivepractical application of the technique. The paper is organized in two parts. In the first we have applied the autoregressive (AR) and the minimum variance (MV)-Capon methods to improve therange resolution of simulated ISAR data. In the second part we have conceived an upgraded versionof spectral analysis (SPECAN) processing to obtain a SAR image of better quality. The method hasbeen tested on recorded live data of ERS-1 mission.
基金supported by the National Natural Science Foundation of China(6130127161331007)+2 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(2011018512000820120185130001)the Fundamental Research Funds for Central Universities(ZYGX2012J043)
文摘An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.
基金supported by the Ministry of Education-Singapore(Grant Nos.MOE2019-T2-2-094 and MOE Tier I R-397-000-327-114)Shenzhen Science and Technology Program(Grant No.GJHZ20210705141805015).
文摘Structured illumination microscopy(SIM)is an established optical superresolution imaging technique.However,conventional SIM based on wide-field image acquisition is generally limited to visualizing thin cellular samples.We propose combining one-dimensional image rescan and structured illumination in the orthogonal direction to achieve superresolution without the need to rotate the illumination pattern.The image acquisition speed is consequently improved threefold,which is also beneficial for minimizing photobleaching and phototoxicity.Optical sectioning in thick biological tissue is enhanced by including a confocal slit in the system to significantly suppress the out-of-focus background and the associated noise.With all the technical improvements,our method captures threedimensional superresolved image stacks of neuronal structures in mouse brain tissue samples for a depth range of more than 200μm.
文摘This paper investigates the influences of phase shift on superresolution performances of annular filters. Firstly, it investigates the influence of phase shift on axial superresolution. It proves theoretically that axial superresolution can not be obtained by two-zone phase filter with phase shift n, and it gets the phase shift with which axial superresolution can be brought by two-zone phase filter. Secondly, it studies the influence of phase shift on transverse superresolution. It finds that the three-zone phase filter with arbitrary phase shift has an almost equal optimal transverse gain to that of commonly used three-zone phase filter, but can produce a much higher axial superresolution gain. Thirdly, it investigates the influence of phase shift on three-dimensional superresolution. Three-dimensional superresolution capability and design margin of three-zone complex filter with arbitrary phase shift are obtained, which presents the theoretical basis for three-dimensional superresolution design. Finally, it investigates the influence of phase shift on focal shift. To obtain desired focal shifts, it designs a series of three-zone phase filters with different phase shifts. A spatial light modulator （SLM） is used to implement the designed filters. By regulating the voltage imposed on the SLM, an accurate focal shift control is obtained,
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.
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 the National Key Research and Development Program of China(Nos.2022YFB3903102 and 2022YFB3607700)the National Natural Science Foundation of China(No.62435018)+2 种基金the Innovation Program for Quantum Science and Technology(No.2021ZD0301100)the USTC Research Funds of the Double First-Class Initiative(No.YD2030002023)the Research Cooperation Fund of SAST,CASC(No.SAST2022-075)。
文摘In this work,we achieve a fourfold enhancement in thermo-optic coefficient measurement resolution for KTiOPO_(4) crystal using a self-stabilized birefringence interferometer integrated with cascaded second-harmonic generation.We observe the tunable interference beating phenomenon by rotating a birefringent crystal versus the temperature of the crystal.Furthermore,the fourth-harmonic interference fringes beat 4 times faster than the fundamental wave interference fringes.This beating effect is used to determine the thermo-optic coefficients of the two principal refractive axes with a single measurement.This work provides a feasible,real-time,and robust method for superresolution measurements based on birefringence interferometry.
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(No.62001432)the Fundamental Research Funds for the Central Universities(No.CUC18LG024).
文摘Acquiring high-resolution light fields(LFs)is expensive.LF angular superresolution aims to synthesize the required number of views from a given sparse set of spatially high-resolution images.Existing methods struggle with sparsely sampled LFs captured with large baselines.Some methods rely on depth estimation and view reprojection,and are sensitive to textureless and occluded regions.Other non-depth based methods suffer from aliasing or blurring effects due to the large disparity.In addition,most methods require specific models for different interpolation rates,which reduces their flexibility in practice.In this paper,we propose a learning framework that overcomes these challenges by exploiting the global and local structures of LFs.Our framework includes aggregation across both the angular and spatial dimensions to fully exploit the input data and a novel bilateral upsampling module that upsamples each epipolar plane image while better preserving its local parallax structure.Furthermore,our method predicts the weights of the interpolation filters based on both subpixel offset and range difference,allowing angular superresolution at different rates with a single model.We show that our non-depth based method outperforms the state-of-the-art methods in terms of handling large disparities and flexibility on both real-world and synthetic LF images.
基金National Natural Science Foundation of China(NSFC)(61490712,61427819,91750205,61605117)National Key Basic Research Program of China(973)(2015CB352004)+4 种基金Leading Talents of Guangdong Province Program(00201505)Natural Science Foundation of Guangdong Province(2016A030312010,2016A030310063,2017A030313351)National Key Research and Development Program of China(2016YFC0102401)Science and Technology Innovation Commission of Shenzhen(KQTD2017033011044403,KQTD2015071016560101,ZDSYS201703031605029)Excellent Young Teacher Program of Guangdong Province(YQ2014151)
文摘Because of the fingerprint-like specificity of its characteristic spectrogram, Raman spectral imaging has been applied widely in various research areas. Using a combination of structured illumination with the surface- enhanced Raman scattering （SERS） technique, wide-field Raman imaging is developed with a significant improve- ment in spatial resolution. As a result of the relatively narrow Raman characteristic peaks, optically encoded SERS nanoparticles can be used to perform multiplexed imaging. The results show excellent superresolution wide-fidd multiplexed imaging performance. The developed technique has extraordinary potential for applications in biological imaging and other related fields.
基金partially supported by National Natural Science Foundation of China (Nos. 51172253 and 61137002)
文摘Using a strong nonlinear saturation absorption effect is one technique for breaking through the diffraction limit. In this technique, formation of a dynamic and reversible optical pinhole channel and transient superresolution is critical. In this work, a pump–probe transient detection and observation–experimental setup is constructed to explore the formation process directly. A Ge2Sb2Te5 thin film with strong nonlinear saturation absorption is investigated. The dynamic evolution of the optical pinhole channel is detected and imaged, and the transient superresolution spot is directly captured experimentally. Results verify that the superresolution effect originates from the generation of an optical pinhole channel and that the formation of the optical pinhole channel is dynamic and reversible. A good method is provided for direct detection and observation of the transient process of the superresolution effect of nonlinear thin films.
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This work was supported by the National Key R&D Program of China(2016YFA0400900)National Natural Science Foundation of China(21775157,21390414,61475181,and U1632125)Key Research Program of Frontier Sciences,Chinese Academy of Sciences(QYZDJ-SSW-SLH031).
文摘Visualization of the spatiotemporal organization of chromatin is highly desirable in the study of genome function regulations.Clustered regularly interspa-ced short palindromic repeats(CRISPR)/CRISPR-associated endonuclease system has shown great promise for application in real-time chromatin imag-ing due to its DNA targeting ability in living cells.Previous studies typically used fluorescent proteins to generate fluorescent signals which,however,have trade-offs among signal intensity,multiplexibility,and simplicity.
基金supported by the by the Ministerio de Economía y Competitividad under project FIS2012-31079
文摘Several pupil filtering techniques have been developed in the last few years to obtain transverse superresolution （a narrower point spread function core）. Such a core decrease entails two relevant limitations： a decrease of the peak intensity and an increase of the sidelobe intensity. Here, we calculate the Strehl ratio as a function of the core size for the most used binary phase filters. Furthermore, we show that this relation approaches the fundamental limit of the attainable Strehl ratio at the focal plane for any filter. Finally, we show the calculation of the peak-to-sidelobe ratio in order to check the system viability in every application.
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.
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This work was supported in part by the Basic Scientific Research Project of Liaoning Provincial Department of Education under Grant Nos.LJKQZ2021152 and LJ2020JCL007in part by the National Science Foundation of China(NSFC)under Grant No.61602226in part by the PhD Startup Foundation of Liaoning Technical University of China under Grant Nos.18-1021.
文摘To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.
基金supported in part by the Basic Scientific Research Project of Liaoning Provincial Department of Education under Grant No.LJKQZ2021152in part by the National Science Foundation of China (NSFC)under Grant No.61602226in part by the PhD Startup Foundation of Liaoning Technical University of China under Grant No.18-1021.
文摘The superresolution(SR)method based on generative adversarial networks(GANs)cannot adequately capture enough diversity from training data,resulting in misalignment between input low resolution(LR)images and output high resolution(HR)images.GAN training has difficulty converging.Based on this,an advanced GAN-based image SR reconstructionmethod is presented.First,the dense connection residual block and attention mechanism are integrated into the GAN generator to improve high-frequency feature extraction.Meanwhile,an added discriminator is added into the GAN discriminant network,which forms a dual discriminator to ensure that the process of training is stable.Second,the more robust Charbonnier loss is used instead of the mean square error(MSE)loss to compare similarities between the obtained image and actual image,and the total variation(TV)loss is employed to smooth the training results.Finally,the experimental results indicate that global structures can be better reconstructed using the method of this paper and texture details of images compared with other SOTA methods.The peak signal-to-noise ratio(PSNR)values by the method of this paper are improved by an average of 2.24 dB,and the structural similarity index measure(SSIM)values are improved by an average of 0.07.
基金support from the National Science Foundation (Grant CMMI-1934300)Defense Advanced Research Projects Agency (DARPA) under the Physics of Artificial Intelligence (PAI) program (Grant HR00111890034)partial funding support by graduate fellowship from China Scholarship Council (CSC) in this effort
文摘In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.
