A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data a...A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.展开更多
Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provi...Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.展开更多
Optical microscopy promises researchers to soe most tiny substances directly.However,the resolution of conventional microscopy is resticted by the diffraction limit.This makes it a challenge to observe subcellular pro...Optical microscopy promises researchers to soe most tiny substances directly.However,the resolution of conventional microscopy is resticted by the diffraction limit.This makes it a challenge to observe subcellular processes happened in nanoscale.The development of super-resolution microscopy provides a solution to this challenge.Here,we briefly review several commonly used super-resolution techniques,explicating their basic principles and applications in biological science,especially in neuroscience.In addition,characteristics and limitations of each techrique are compared to provide a guidance for biologists to choose the most suitable tool.展开更多
Image scanning microscopy based on pixel reassignment can improve the confocal resolution limit without losing the image signal-to-noise ratio(SNR)greatly[C.J.R.Sheppard,"Super resolution in confocal imaging,&quo...Image scanning microscopy based on pixel reassignment can improve the confocal resolution limit without losing the image signal-to-noise ratio(SNR)greatly[C.J.R.Sheppard,"Super resolution in confocal imaging,"Optik 80(2)53-54(1988).C.B.Miller,E.Jorg,"Image scanning microscopy,"Phys.Reu.Lett.104(19)198101(2010).C.J.R.Sheppard,s.B.Mehta,R Heintzmann,"Superresolution by image scanning microscopy using pixel reassignment,"Opt.Lett.38(15)28892892(2013)].Here,we use a tailor-made optical fiber and 19 avalanche pho-todiodes(APDs)as parallel detectors to upgrade our existing confocal microscopy,termed as parallel-detection super resolution(PDSR)microscopy.In order to obtain the correct shift value,we use the normalized 2D cross correlation to calculate the shifting value of each image.We characterized our system performance by imaging fuorescence beads and applied this system to observing the 3D structure of biological specimen.展开更多
Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and...Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration.展开更多
Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such ...Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical...Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical components can depolarize the laser beams hence degenerating the modulation depth.Here,we first presented a direct measurement method designed to estimate the modulation depth more precisely by shifting illumination patterns with equal phase steps.This measurement method greatly reduces the dependence of modulation depths on the samples,and then developed a polarization optimization method to achieve high modulation depth at all orientations by actively and quantitatively compensating for the additional phase difference using a combination of waveplate and a liquid crystal variable retarder(LCVR).Experimental results demonstrate that our method can achieve illumination patterns with modulation depth higher than 0.94 at three orientations with only one LCVR voltage,which enables isotropic resolution improvement.展开更多
We developed an imaging technique combining two-photon computed super-resolution microscopy and suction-based stabilization to achieve the resolution of the single-cell level and organelles in vivo.To accomplish this,...We developed an imaging technique combining two-photon computed super-resolution microscopy and suction-based stabilization to achieve the resolution of the single-cell level and organelles in vivo.To accomplish this,a conventional two-photon microscope was equipped with a 3D-printed holders,which stabilize the tissue surface within the focal plane of immersion objectives.Further computational image stabilization and noise reduction were applied,followed by superresolution radial fluctuations(SRRF)analysis,doubling image resolution,and enhancing signal-to-noise ratios for in vivo subcellular process investigation.Stabilization of<1μm was obtained by suction,and<25 nm were achieved by subsequent algorithmic image stabilization.A Mito-Dendra2 mouse model,expressing green fluorescent protein(GFP)in mitochondria,demonstrated the potential of long-term intravital subcellular imaging.In vivo mitochondrial fission and fusion,mitochondrial status migration,and the effects of alcohol consumption(modeled as an alcoholic liver disease)and berberine treatment on hepatocyte mitochondrial dynamics are directly observed intravitally.Suction-based stabilization in two-photon intravital imaging,coupled with computational super-resolution holds promise for advancing in vivo subcellular imaging studies.展开更多
The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great imp...The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.展开更多
Resolution is undoubtedly the most important parameter in optical microscopy by providing an estimation on the maximum resolving power of a certain optical microscope. For centuries, the resolution of an optical micro...Resolution is undoubtedly the most important parameter in optical microscopy by providing an estimation on the maximum resolving power of a certain optical microscope. For centuries, the resolution of an optical microscope is generally considered to be limited only by the numerical aperture of the optical system and the wavelength of light. However, since the invention and popularity of various advanced fluorescence microscopy techniques, especially super-resolution fluorescence microscopy, many new methods have been proposed for estimating the resolution, leading to confusions for researchers who need to quantify the resolution of their fluorescence microscopes. In this paper, we firstly summarize the early concepts and criteria for predicting the resolution limit of an ideal optical system. Then, we discuss some important influence factors that deteriorate the resolution of a certain fluorescence microscope. Finally, we provide methods and examples on how to measure the resolution of a fluorescence microscope from captured fluorescence images. This paper aims to answer as best as possible the theoretical and practical issues regarding the resolution estimation in fluorescence microscopy.展开更多
Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens.However,the super-resolution capability is often achieved at the expense of reducing imaging speed ...Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens.However,the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation.The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures.To overcome this contradiction,here we propose and demonstrate a temporal compressive super-resolution microscopy(TCSRM)technique.This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction,where the enhanced temporal compressive microscopy is utilized to improve the imaging speed,and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement.The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second(fps)and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip.Given the outstanding imaging performance with high-speed super-resolution,TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures,especially in the biomedical field.展开更多
Photon avalanche occurring in lanthanide-doped materials exhibits a giant optical nonlinear response of the emission intensity to the excitation intensity,which holds great potential in the applications of optical sen...Photon avalanche occurring in lanthanide-doped materials exhibits a giant optical nonlinear response of the emission intensity to the excitation intensity,which holds great potential in the applications of optical sensing,super-resolution imaging,quantum detection,and other techniques.However,strategies for developing photon avalanches in nanoparticles are limited,and many widely used lanthanide ions have not yet been able to generate high-efficiency avalanching emissions.A general strategy named cascade migrating photon avalanche was proposed to achieve efficient avalanching emissions with huge optical nonlinearities from a large number of emitters at the nanoscale and at room temperature.Specifically,the optical nonlinearity order of bright avalanched Tm^(3+)-emission was achieved at 63rd order by utilizing the Yb^(3+)∕Pr^(3+)-codoped nano-engine.By further incorporating a Gd^(3+)sublattice migrating network,its avalanching energy can propagate over a long distance to arouse avalanching emission with extreme optical nonlinearities up to 45th order among various emitters(Tb^(3+),Eu^(3+),Dy^(3+),Sm^(3+))in multilayered nanostructures.By achieving abundant avalanching full-spectrum emissions,it would be highly conducive to applications in various fields.For instance,our strategy demonstrated its applicability in multi-color super-resolution microscopic imaging with single-nanoparticle sensitivity and resolution up to 48 nm,utilizing a single low-power 852 nm excitation beam.展开更多
The transmembrane protein CD47,an innate immune checkpoint protein,plays a pivotal role in preventing healthy erythrocytes from immune clearance.Our study utilized stochastic optical reconstruction microscopy(STORM)an...The transmembrane protein CD47,an innate immune checkpoint protein,plays a pivotal role in preventing healthy erythrocytes from immune clearance.Our study utilized stochastic optical reconstruction microscopy(STORM)and single-molecule analysis to investigate the distribution of CD47 on the human erythrocyte membrane.Contrary to previous findings in mouse erythrocytes,we discovered that CD47 exists in randomly distributed monomers rather than in clusters across the human erythrocyte membrane.Using secondary antibody-induced crosslinking,we found that CD47 aggregates into stable clusters within minutes.By comparing these STORM results with those of the fully mobile protein CD59 and the cytoskeleton-bound membrane protein glycophorin C under similar conditions,as well as devising two-color STORM co-labeling and co-clustering experiments,we further quantitatively revealed an intermediate,self-limiting clustering behavior of CD47,elucidating its fractional(∼14%)attachment to the cytoskele-ton.Moreover,we report reductions in both the amount of CD47 and its clustering capability in aged erythrocytes,providing new insight into erythrocyte senescence.Together,the combination of STORM and secondary antibody-based crosslinking unveils the unique self-limiting clustering behavior of CD47 due to its fractional cytoskeleton attachment.展开更多
With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,the research team led by Prof.Li Junbai(李峻柏)at the CAS Key Lab of Colloid,Interface and Thermodynamics,Instit...With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,the research team led by Prof.Li Junbai(李峻柏)at the CAS Key Lab of Colloid,Interface and Thermodynamics,Institute of Chemistry,Chinese Academy of Sciences,revealed the distribution of proteins in the transformation of inorganic/protein hybrid crystals by super-resolution microscopy,which展开更多
The introduction of super-resolution microscopy(SRM)has significantly advanced our understanding of cellular and molecular dynamics,offering a detailed view previously beyond our reach.Implementing SRM in biophysical ...The introduction of super-resolution microscopy(SRM)has significantly advanced our understanding of cellular and molecular dynamics,offering a detailed view previously beyond our reach.Implementing SRM in biophysical research,however,presents numerous challenges.This review addresses the crucial aspects of utilizing SRM effectively,from selecting appropriate fluorophores and preparing samples to analyzing complex data sets.We explore recent technological advancements and methodological improvements that enhance the capabilities of SRM.Emphasizing the integration of SRM with other analytical methods,we aim to overcome inherent limitations and expand the scope of biological insights achievable.By providing a comprehensive guide for choosing the most suitable SRM methods based on specific research objectives,we aim to empower researchers to explore complex biological processes with enhanced precision and clarity,thereby advancing the frontiers of biophysical research.展开更多
The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved sta...The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.展开更多
The resolution of conventional optical microscopy is only -200 nm, which is becoming less and less sufficient for a variety of applications. In order to surpass the diffraction limited resolution, super-resolution mic...The resolution of conventional optical microscopy is only -200 nm, which is becoming less and less sufficient for a variety of applications. In order to surpass the diffraction limited resolution, super-resolution microscopy （SRM） has been developed to achieve a high resolution of one to tens of nanometers. The techniques involved in SRM can be assigned into two broad categories, namely ＂true＂ super-resolution techniques and ＂functional＂ super-resolution techniques. In ＂functional＂ super-resolution techniques, stochastic super-resolution microscopy （SSRM） is widely used due to its low expense, simple operation, and high resolution. The principle process in SSRM is to accumulate the coordinates of many diffraction-limited emitters （e.g., single fluorescent molecules） on the object by localizing the centroids of the point spread functions （PSF）, and then reconstruct the image of the object using these coordinates. When the diffraction-limited emitters take part in a catalytic reaction, the activity distribution and kinetic information about the catalysis by nanoparticles can be obtained by SSRM. SSRM has been applied and exhibited outstanding advantages in several fields of catalysis, such as metal nanoparticle catalysis, molecular sieve catalysis, and photocatalysis. Since SSRM is able to resolve the catalytic activity within one nanoparticle, it promises to accelerate the development and discovery of new and better catalysts. This review will present a brief introduction to SRM, and a detailed description of SSRM and its applications in nano-catalysis.展开更多
Hematologic malignancies are one of the most common malignant tumors caused by the clonal proliferation and differentiation of hematopoietic and lymphoid stem cells.The examination of bone marrow cells combined with i...Hematologic malignancies are one of the most common malignant tumors caused by the clonal proliferation and differentiation of hematopoietic and lymphoid stem cells.The examination of bone marrow cells combined with immunodeficiency typing is of great significance to the diagnostic type,treatment and prognosis of hematologic malignancies.Super-resolution fluorescence microscopy(SRM)is a special kind of optical microscopy technology,which breaks the resolution limit and was awarded the Nobel Prize in Chemistry in 2014.With the development of SRM,many related technologies have been applied to the diagnosis and treatment of clinical diseases.It was reported that a major type of SRM technique,single molecule localization microscopy(SMLM),is more sensitive than flow cytometry(FC)in detecting cell membrane antigens'expression,thus enabling better chances in detecting antigens on hematopoietic cells than traditional analytic tools.Furthermore,SRM may be applied to clinical pathology and may guide precision medicine and personalized medicine for clone hematopoietic cell diseases.In this paper,we mainly discuss the application of SRM in clone hematological malignancies.展开更多
基金supported by the National Natural Science Foundation of China(62175034,62175036,32271510)the National Key R&D Program of China(2021YFF0502900)+2 种基金the Science and Technology Research Program of Shanghai(Grant No.19DZ2282100)the Shanghai Key Laboratory of Metasurfaces for Light Manipulation(23dz2260100)the Shanghai Engineering Technology Research Center of Hair Medicine(19DZ2250500).
文摘A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.
基金supported by the following grants:National Key R&D Program of China(Grant no.2022YFC3401100)National Natural Science Foundation of China(Grant nos.32271428,92054110,32201132 and 31600692).
文摘Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.
基金support from National Basic Research Program of China (973 Program) (2015CB352005)National Natural Science Foundation of China (No.6142780065,31571110,81527901)+1 种基金Natural Science Foundation of Zhejiang Province of China (No.Y16F050002)the Fundamental Research Funds for the Central Universities.
文摘Optical microscopy promises researchers to soe most tiny substances directly.However,the resolution of conventional microscopy is resticted by the diffraction limit.This makes it a challenge to observe subcellular processes happened in nanoscale.The development of super-resolution microscopy provides a solution to this challenge.Here,we briefly review several commonly used super-resolution techniques,explicating their basic principles and applications in biological science,especially in neuroscience.In addition,characteristics and limitations of each techrique are compared to provide a guidance for biologists to choose the most suitable tool.
基金sponsored by National Natural Science Foundation of China(61827825 and 61735017)Fundamental Research Funds for the Central Universities(2019XZZX003-06)+1 种基金Natural Science Foundation of Zhejiang province(LR16F050001)Zhejiang Lab(2018EB0ZX01).
文摘Image scanning microscopy based on pixel reassignment can improve the confocal resolution limit without losing the image signal-to-noise ratio(SNR)greatly[C.J.R.Sheppard,"Super resolution in confocal imaging,"Optik 80(2)53-54(1988).C.B.Miller,E.Jorg,"Image scanning microscopy,"Phys.Reu.Lett.104(19)198101(2010).C.J.R.Sheppard,s.B.Mehta,R Heintzmann,"Superresolution by image scanning microscopy using pixel reassignment,"Opt.Lett.38(15)28892892(2013)].Here,we use a tailor-made optical fiber and 19 avalanche pho-todiodes(APDs)as parallel detectors to upgrade our existing confocal microscopy,termed as parallel-detection super resolution(PDSR)microscopy.In order to obtain the correct shift value,we use the normalized 2D cross correlation to calculate the shifting value of each image.We characterized our system performance by imaging fuorescence beads and applied this system to observing the 3D structure of biological specimen.
基金supported by the National Natural Science Foundation of China(Nos.12205044 and 12265003)2024 Jiangxi Province Civil-Military Integration Research Institute‘BeiDou+’Project Subtopic(No.2024JXRH0Y06).
文摘Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration.
基金funded by Project of Sichuan Provincial Department of Science and Technology under 2025JDKP0150the Fundamental Research Funds for the Central Universities under 25CAFUC03093.
文摘Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金supported by the National Natural Science Foundation of China[Grant Nos.62205367 and 62141506]the Suzhou Basic Research Pilot Project[Grant Nos.SSD2023006 and SJC2021013]the National Key Research and Development Program of China[Grant No.2023YFF1205700].
文摘Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical components can depolarize the laser beams hence degenerating the modulation depth.Here,we first presented a direct measurement method designed to estimate the modulation depth more precisely by shifting illumination patterns with equal phase steps.This measurement method greatly reduces the dependence of modulation depths on the samples,and then developed a polarization optimization method to achieve high modulation depth at all orientations by actively and quantitatively compensating for the additional phase difference using a combination of waveplate and a liquid crystal variable retarder(LCVR).Experimental results demonstrate that our method can achieve illumination patterns with modulation depth higher than 0.94 at three orientations with only one LCVR voltage,which enables isotropic resolution improvement.
基金supported by the Ministry of Science,ICT and Future Planning(MSIP)through the National Research Foundation of Korea(NRF)(RS-2024-00450201)supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health and Welfare,Republic of Korea(HI22C1374).
文摘We developed an imaging technique combining two-photon computed super-resolution microscopy and suction-based stabilization to achieve the resolution of the single-cell level and organelles in vivo.To accomplish this,a conventional two-photon microscope was equipped with a 3D-printed holders,which stabilize the tissue surface within the focal plane of immersion objectives.Further computational image stabilization and noise reduction were applied,followed by superresolution radial fluctuations(SRRF)analysis,doubling image resolution,and enhancing signal-to-noise ratios for in vivo subcellular process investigation.Stabilization of<1μm was obtained by suction,and<25 nm were achieved by subsequent algorithmic image stabilization.A Mito-Dendra2 mouse model,expressing green fluorescent protein(GFP)in mitochondria,demonstrated the potential of long-term intravital subcellular imaging.In vivo mitochondrial fission and fusion,mitochondrial status migration,and the effects of alcohol consumption(modeled as an alcoholic liver disease)and berberine treatment on hepatocyte mitochondrial dynamics are directly observed intravitally.Suction-based stabilization in two-photon intravital imaging,coupled with computational super-resolution holds promise for advancing in vivo subcellular imaging studies.
基金Project supported by the National Key Foundation for Exploring Scientific Instrument （No. 2013YQ03065102）, the National Basic Research Program （973） of China （No. 2012CB316503）, and the National Natural Science Foundation of China （Nos. 31327901, 61475010, 31361163004, and 61428501）
文摘The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.
基金supported by the National Natural Science Foundation of China (81427801, 81827901)National Basic Research Program of China (2015CB352003)+2 种基金Science Fund for Creative Research Groups (61721092)Fundamental Research Funds for the Central Universities (2018KFYXKJC039)Director Fund of WNLO。
文摘Resolution is undoubtedly the most important parameter in optical microscopy by providing an estimation on the maximum resolving power of a certain optical microscope. For centuries, the resolution of an optical microscope is generally considered to be limited only by the numerical aperture of the optical system and the wavelength of light. However, since the invention and popularity of various advanced fluorescence microscopy techniques, especially super-resolution fluorescence microscopy, many new methods have been proposed for estimating the resolution, leading to confusions for researchers who need to quantify the resolution of their fluorescence microscopes. In this paper, we firstly summarize the early concepts and criteria for predicting the resolution limit of an ideal optical system. Then, we discuss some important influence factors that deteriorate the resolution of a certain fluorescence microscope. Finally, we provide methods and examples on how to measure the resolution of a fluorescence microscope from captured fluorescence images. This paper aims to answer as best as possible the theoretical and practical issues regarding the resolution estimation in fluorescence microscopy.
基金the National Natural Science Foundation of China(91850202,92150301,12074121,62105101,62175066,11727810,12034008,12274129,12274139)Science and Technology Commission of Shanghai Municipality(21XD1400900,20ZR1417100,21JM0010700).
文摘Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens.However,the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation.The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures.To overcome this contradiction,here we propose and demonstrate a temporal compressive super-resolution microscopy(TCSRM)technique.This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction,where the enhanced temporal compressive microscopy is utilized to improve the imaging speed,and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement.The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second(fps)and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip.Given the outstanding imaging performance with high-speed super-resolution,TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures,especially in the biomedical field.
基金supported by the National Natural Science Foundation of China(Grant Nos.62122028 and 62335008)Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023B1515040018 and 2018B030306015)+1 种基金National Key Research and Development Program of China(Grant No.2023YFF0722600)Scientific Research Cultivation Fund for Young Faculty of South China Normal University(Grant Nos.23KJ01 and 22KJ30).
文摘Photon avalanche occurring in lanthanide-doped materials exhibits a giant optical nonlinear response of the emission intensity to the excitation intensity,which holds great potential in the applications of optical sensing,super-resolution imaging,quantum detection,and other techniques.However,strategies for developing photon avalanches in nanoparticles are limited,and many widely used lanthanide ions have not yet been able to generate high-efficiency avalanching emissions.A general strategy named cascade migrating photon avalanche was proposed to achieve efficient avalanching emissions with huge optical nonlinearities from a large number of emitters at the nanoscale and at room temperature.Specifically,the optical nonlinearity order of bright avalanched Tm^(3+)-emission was achieved at 63rd order by utilizing the Yb^(3+)∕Pr^(3+)-codoped nano-engine.By further incorporating a Gd^(3+)sublattice migrating network,its avalanching energy can propagate over a long distance to arouse avalanching emission with extreme optical nonlinearities up to 45th order among various emitters(Tb^(3+),Eu^(3+),Dy^(3+),Sm^(3+))in multilayered nanostructures.By achieving abundant avalanching full-spectrum emissions,it would be highly conducive to applications in various fields.For instance,our strategy demonstrated its applicability in multi-color super-resolution microscopic imaging with single-nanoparticle sensitivity and resolution up to 48 nm,utilizing a single low-power 852 nm excitation beam.
基金supported by the National Key Research and Development Program of China(2022YFC3400600)the National Natural Science Foundation of China(11874231,32227802,and 12174208)+3 种基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030009)China Postdoctoral Science Foundation(2020M680032)the Fundamental Research Funds for the Central Universities(2122021337 and 2122021405)the Pew Charitable Trusts.
文摘The transmembrane protein CD47,an innate immune checkpoint protein,plays a pivotal role in preventing healthy erythrocytes from immune clearance.Our study utilized stochastic optical reconstruction microscopy(STORM)and single-molecule analysis to investigate the distribution of CD47 on the human erythrocyte membrane.Contrary to previous findings in mouse erythrocytes,we discovered that CD47 exists in randomly distributed monomers rather than in clusters across the human erythrocyte membrane.Using secondary antibody-induced crosslinking,we found that CD47 aggregates into stable clusters within minutes.By comparing these STORM results with those of the fully mobile protein CD59 and the cytoskeleton-bound membrane protein glycophorin C under similar conditions,as well as devising two-color STORM co-labeling and co-clustering experiments,we further quantitatively revealed an intermediate,self-limiting clustering behavior of CD47,elucidating its fractional(∼14%)attachment to the cytoskele-ton.Moreover,we report reductions in both the amount of CD47 and its clustering capability in aged erythrocytes,providing new insight into erythrocyte senescence.Together,the combination of STORM and secondary antibody-based crosslinking unveils the unique self-limiting clustering behavior of CD47 due to its fractional cytoskeleton attachment.
文摘With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,the research team led by Prof.Li Junbai(李峻柏)at the CAS Key Lab of Colloid,Interface and Thermodynamics,Institute of Chemistry,Chinese Academy of Sciences,revealed the distribution of proteins in the transformation of inorganic/protein hybrid crystals by super-resolution microscopy,which
基金support from the National Institutes of Health(Grant R35GM133505)the National Science Foundation(Grant no.2237129)the University of Houston.
文摘The introduction of super-resolution microscopy(SRM)has significantly advanced our understanding of cellular and molecular dynamics,offering a detailed view previously beyond our reach.Implementing SRM in biophysical research,however,presents numerous challenges.This review addresses the crucial aspects of utilizing SRM effectively,from selecting appropriate fluorophores and preparing samples to analyzing complex data sets.We explore recent technological advancements and methodological improvements that enhance the capabilities of SRM.Emphasizing the integration of SRM with other analytical methods,we aim to overcome inherent limitations and expand the scope of biological insights achievable.By providing a comprehensive guide for choosing the most suitable SRM methods based on specific research objectives,we aim to empower researchers to explore complex biological processes with enhanced precision and clarity,thereby advancing the frontiers of biophysical research.
基金supported by the National Key R&D Program of China(2021YFF0502900)the National Natural Science Foundation of China(61835009/62127819).
文摘The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.
文摘The resolution of conventional optical microscopy is only -200 nm, which is becoming less and less sufficient for a variety of applications. In order to surpass the diffraction limited resolution, super-resolution microscopy （SRM） has been developed to achieve a high resolution of one to tens of nanometers. The techniques involved in SRM can be assigned into two broad categories, namely ＂true＂ super-resolution techniques and ＂functional＂ super-resolution techniques. In ＂functional＂ super-resolution techniques, stochastic super-resolution microscopy （SSRM） is widely used due to its low expense, simple operation, and high resolution. The principle process in SSRM is to accumulate the coordinates of many diffraction-limited emitters （e.g., single fluorescent molecules） on the object by localizing the centroids of the point spread functions （PSF）, and then reconstruct the image of the object using these coordinates. When the diffraction-limited emitters take part in a catalytic reaction, the activity distribution and kinetic information about the catalysis by nanoparticles can be obtained by SSRM. SSRM has been applied and exhibited outstanding advantages in several fields of catalysis, such as metal nanoparticle catalysis, molecular sieve catalysis, and photocatalysis. Since SSRM is able to resolve the catalytic activity within one nanoparticle, it promises to accelerate the development and discovery of new and better catalysts. This review will present a brief introduction to SRM, and a detailed description of SSRM and its applications in nano-catalysis.
基金This work was supported by the Innovation Fund of WNLO(2018WNLOKF023)the Start-up Fund of Hainan University(KYQD(ZR)-20077).
文摘Hematologic malignancies are one of the most common malignant tumors caused by the clonal proliferation and differentiation of hematopoietic and lymphoid stem cells.The examination of bone marrow cells combined with immunodeficiency typing is of great significance to the diagnostic type,treatment and prognosis of hematologic malignancies.Super-resolution fluorescence microscopy(SRM)is a special kind of optical microscopy technology,which breaks the resolution limit and was awarded the Nobel Prize in Chemistry in 2014.With the development of SRM,many related technologies have been applied to the diagnosis and treatment of clinical diseases.It was reported that a major type of SRM technique,single molecule localization microscopy(SMLM),is more sensitive than flow cytometry(FC)in detecting cell membrane antigens'expression,thus enabling better chances in detecting antigens on hematopoietic cells than traditional analytic tools.Furthermore,SRM may be applied to clinical pathology and may guide precision medicine and personalized medicine for clone hematopoietic cell diseases.In this paper,we mainly discuss the application of SRM in clone hematological malignancies.
