This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering a...This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.展开更多
Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging f...Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging from organic dyes to genetically encoded proteins and nanomaterials, provide unparalleled specificity, sensitivity, and multiplexing capabilities. However, challenges such as brightness, photobleaching, biocompatibility, and emission range continue to drive innovation in probe design and application. This special issue, comprising four review papers and seven original research studies, highlights cutting-edge advancements in fluorescent probe technologies and their transformative roles in super-resolution imaging, in vivo diagnostics, and cancer therapeutics.展开更多
Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam...Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam probe, Atto565-Tet, is rationally constructed for spontaneously blinking after biorthogonal labelling and successfully applied to super resolution imaging of mitochondria and lysosomes.展开更多
BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and H...BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.展开更多
Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely exp...Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.展开更多
Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal resolution.Recently,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS...Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal resolution.Recently,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image guidance.Previous approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted priors.Recently,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing data.In this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image guidance.Specif-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image guidance.Then,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes.The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial constraint.It has a good general-isation capability,especially for blind HS image super-resolution.Comprehensive experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.展开更多
As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note tha...As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.展开更多
In many cases,the Digital Surface Models(DSMs)and Digital Elevation Models(DEMs)are obtained with Light Detection and Ranging(LiDAR)or stereo matching.As an active method,LiDAR is very accurate but expensive,thus ofte...In many cases,the Digital Surface Models(DSMs)and Digital Elevation Models(DEMs)are obtained with Light Detection and Ranging(LiDAR)or stereo matching.As an active method,LiDAR is very accurate but expensive,thus often limiting its use in small-scale acquisition.Stereo matching is suitable for large-scale acquisition of terrain information as the increase of satellite stereo sensors.However,underperformance of stereo matching easily occurs in textureless areas.Accordingly,this study proposed a Shading Aware DSM GEneration Method(SADGE)with high resolution multi-view satellite images.Considering the complementarity of stereo matching and Shape from Shading(SfS),SADGE combines the advantage of stereo matching and SfS technique.First,an improved Semi-Global Matching(SGM)technique is used to generate an initial surface expressed by a DSM;then,it is refined by optimizing the objective function which modeled the imaging process with the illumination,surface albedo,and normal object surface.Different from the existing shading-based DEM refinement or generation method,no information about the illumination or the viewing angle is needed while concave/convex ambiguity can be avoided as multi-view images are utilized.Experiments with ZiYuan-3 and GaoFen-7 images show that the proposed method can generate higher accuracy DSM(12.5-56.3%improvement)with sound overall shape and temporarily detailed surface compared with a software solution(SURE)for multi-view stereo.展开更多
We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact ...We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.展开更多
Leaf area index(LAI)is a key measure of forest stand physiology and biomass production,and is essential within ecosystem modeling.There are two common approaches to obtaining LAI:(i)terrestrial forest inventory-based...Leaf area index(LAI)is a key measure of forest stand physiology and biomass production,and is essential within ecosystem modeling.There are two common approaches to obtaining LAI:(i)terrestrial forest inventory-based“bottom-up”,and(ii)satellite-based“top-down”techniques.The purpose of this study is to compare terrestrial LAI from allometric functions applied to more than 30,000 trees of the Austrian National Forest Inventory(NFI)vs.satellite-based LAI estimates obtained from moderate resolution imaging spectroradiometer(MODIS)and Sentinel(Sentinel-3 TOC reflectance and PROBA-V)data across Austrian forests.We analyzed a satellite pixelto-plot aggregation and obtained the full inventory data set for the LAI comparison.The results suggest that terrestrial vs.satellite(MODIS and Sentinel)driven LAI estimates are consistent,but(i)the variation of the terrestrial forest inventory LAI is larger vs.the pixel average LAI from satellite data,and(ii)any satellite LAI estimation needs a forest stand density correction if the crown competition factor(CCF),a measure for stand density,is<250 to avoid an overestimation in LAI.展开更多
Mesoscopy refers to imaging methodologies that provide a field of view(FOV)ranging from several millimeters to centimeters while achieving cellular or even subcellular resolution(Figure 1).This technological framework...Mesoscopy refers to imaging methodologies that provide a field of view(FOV)ranging from several millimeters to centimeters while achieving cellular or even subcellular resolution(Figure 1).This technological framework employs specially designed large-scale objective lenses to correct aberrations across extended FOVs,synchronized with light-field acquisition modalities through either scanning point detection or large-format array detection.Conventional microscopes,constrained by the limitations of objective lenses,exhibit a trade-off between the FOV and resolution.To achieve both high resolution and a large FOV,common approaches such as FOV stitching and Fourier ptychography were employed.However,these methods were extremely slow and imposed numerous constraints on samples.In 2016,a mesoscopic objective lens was introduced to address these challenges,achieving a 6 mm FOV and 0.7 mm resolution,thereby increasing the imaging throughput of conventional objective lenses by orders of magnitude.1 In the same year,this technology was recognized as one of the top ten physics breakthroughs worldwide by Physics World.Since then,mesoscopic imaging technology has gradually gained momentum and has been applied in various fields.展开更多
Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads ...Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads to limited depth penetration and compromised imaging fidelity.To overcome these challenges,we propose a novel imaging system,confocal2 spinning-disk image scanning microscopy(C^(2)SD-ISM).It integrates a spinning-disk(SD)confocal microscope,which physically eliminates out-of-focus signals,forming the first confocal level.A digital micromirror device(DMD)is employed for sparse multifocal illumination,combined with a dynamic pinhole array pixel reassignment(DPA-PR)algorithm for ISM super-resolution reconstruction,forming the second confocal level.The dual confocal configuration enhances system resolution,while effectively mitigating scattering background interference.Compared to computational out-of-focus signal removal,SD preserves the original intensity distribution as the penetration depth increases,achieving an imaging depth of up to 180μm.Additionally,the DPA-PR algorithm effectively corrects Stokes shifts,optical aberrations,and other non-ideal conditions,achieving a lateral resolution of 144 nm and an axial resolution of 351 nm,and a linear correlation of up to 92%between the original confocal and the reconstructed image,thereby enabling high-fidelity super-resolution imaging.Moreover,the system's programmable illumination via the DMD allows for seamless realization with structured illumination microscopy modality,offering excellent scalability and ease of use.Altogether,these capabilities make the C^(2)SD-ISM system a versatile tool,advancing cellular imaging and tissue-scale exploration for modern bioimaging needs.展开更多
In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can...In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.展开更多
Fires are one of the most destructive natural disasters and have serious long-term effects on the environment,economy,and human health.In Inner Mongolia Autonomous Region,China,frequent fire disturbance occurs due to ...Fires are one of the most destructive natural disasters and have serious long-term effects on the environment,economy,and human health.In Inner Mongolia Autonomous Region,China,frequent fire disturbance occurs due to the intensification of climate change and human activities.It is crucial to understand the fire regime and estimate the probability of regional fire occurrence and reducing fire losses.However,most studies have primarily focused on the dynamic changes,probability of occurrence,and driving mechanisms of wildfires in the grassland and forest land ecosystems in Inner Mongolia,while insufficient research has been conducted on the spatiotemporal variations in active fires and their impact on the wildfire risk in forest land and grassland.Therefore,in this study,we analyzed the active fire regime based on Moderate Resolution Imaging Spectroradiometer(MODIS)thermal anomalies and burned area products from 2000 to 2022.Combined with climate,topographic,landscape,anthropogenic,and vegetation datasets,logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models were chosen to estimate the probability of active fire occurrence at the seasonal timescale.The results revealed that:(1)a total of 100,343 active fires occurred in Inner Mongolia and the burned area reached 6.59×104 km².The number of ignition point exhibited a significant increasing trend,while the burned area exhibited a nonsignificant decreasing trend;(2)four active fire belts were detected,namely,the Hetao-Tumochuan Plain fire belt,Xiliao River Plain fire belt,Songnen Plain fire belt,and Hailar River Eroded Plain fire belt.The centroid of the active fires has shifted 456.4 km toward the southwest;(3)RF model achieved the highest accuracy in estimating the probability of active fire occurrence,followed by CNN,and LR and SVM models had lower accuracies;and(4)the distribution of the high and extremely high fire risk areas largely aligned with the four fire belts.The probability of active fire occurrence was the highest in spring,followed by that in autumn,and it gradually decreased in summer and winter.Our results revealed active fires migrated to the southwest and ignition sources increased,despite reduction of the burned area was not significant.The RF model outperformed the other models in predicting the probability of active fire occurrence.These findings contribute to future fire prevention and prediction in Inner Mongolia.展开更多
Introduction of the stepwise phase dispersion compensation layer allowed broadband achromatic metalens to have a high numerical aperture,which enabled high-resolution metalens imaging.
Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climat...Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.展开更多
The global shortage of fossil fuels,along with the widespread pollution caused by their use,urgently calls for the development of reliable clean energy resources.Among various fundamental strategies,the production of ...The global shortage of fossil fuels,along with the widespread pollution caused by their use,urgently calls for the development of reliable clean energy resources.Among various fundamental strategies,the production of green hydrogen through photochemical or electrochemical water splitting has been extensively studied.展开更多
Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction...Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.展开更多
Spectral imaging—a suite of techniques combining image acquisition with extremely high color resolution—plays an ever-increasing role in many fields,such as biomedicine,agriculture,geology,archeology,and environment...Spectral imaging—a suite of techniques combining image acquisition with extremely high color resolution—plays an ever-increasing role in many fields,such as biomedicine,agriculture,geology,archeology,and environmental control.^(1-3)The capability of visualizing,in real time,a tissue or terrain with spatially resolved chemical sensitivity can literally mean the difference between life and death.To appreciate the significance,consider how in vivo spectroscopic sensing of malignant tissue empowers the surgeon to minimize collateral damage during tumor removal while keeping the risk of cancer recurrence low.展开更多
A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, s...A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.展开更多
文摘This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.
文摘Fluorescent probes have revolutionized optical imaging and biosensing by enabling real-time visualization, quantification, and tracking of biological processes at molecular and cellular levels. These probes, ranging from organic dyes to genetically encoded proteins and nanomaterials, provide unparalleled specificity, sensitivity, and multiplexing capabilities. However, challenges such as brightness, photobleaching, biocompatibility, and emission range continue to drive innovation in probe design and application. This special issue, comprising four review papers and seven original research studies, highlights cutting-edge advancements in fluorescent probe technologies and their transformative roles in super-resolution imaging, in vivo diagnostics, and cancer therapeutics.
基金supported by the National Natural Science Foundation of China (Nos. 21421005, 21576040, 21776037, 22004011)China Postdoctoral Science Foundation (Nos. BX20200073 and 2020M670754)Dalian Science and Technology Innovation Fund (No. 2020JJ25CY014)。
文摘Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam probe, Atto565-Tet, is rationally constructed for spontaneously blinking after biorthogonal labelling and successfully applied to super resolution imaging of mitochondria and lysosomes.
基金Supported by the Key Research and Development Plan of Shaanxi Province,No.2021SF-298.
文摘BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.
基金the TCL Science and Technology Innovation Fundthe Youth Science and Technology Talent Promotion Project of Jiangsu Association for Science and Technology,Grant/Award Number:JSTJ‐2023‐017+4 种基金Shenzhen Municipal Science and Technology Innovation Council,Grant/Award Number:JSGG20220831105002004National Natural Science Foundation of China,Grant/Award Number:62201468Postdoctoral Research Foundation of China,Grant/Award Number:2022M722599the Fundamental Research Funds for the Central Universities,Grant/Award Number:D5000210966the Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2021A1515110079。
文摘Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.
基金National Key R&D Program of China,Grant/Award Number:2022YFC3300704National Natural Science Foundation of China,Grant/Award Numbers:62171038,62088101,62006023。
文摘Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal resolution.Recently,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image guidance.Previous approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted priors.Recently,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing data.In this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image guidance.Specif-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image guidance.Then,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes.The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial constraint.It has a good general-isation capability,especially for blind HS image super-resolution.Comprehensive experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.
基金Supported by the National Natural Science Foundation of China(No.61340034)the Research Program of Application Foundation and Advanced Technology of Tianjin(No.13JCYBJC15600)
文摘As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.
基金supported by the National Natural Science Foundation of China[grant number 41801390]the National Key R&D Program of China[grant number 2018YFD1100405].
文摘In many cases,the Digital Surface Models(DSMs)and Digital Elevation Models(DEMs)are obtained with Light Detection and Ranging(LiDAR)or stereo matching.As an active method,LiDAR is very accurate but expensive,thus often limiting its use in small-scale acquisition.Stereo matching is suitable for large-scale acquisition of terrain information as the increase of satellite stereo sensors.However,underperformance of stereo matching easily occurs in textureless areas.Accordingly,this study proposed a Shading Aware DSM GEneration Method(SADGE)with high resolution multi-view satellite images.Considering the complementarity of stereo matching and Shape from Shading(SfS),SADGE combines the advantage of stereo matching and SfS technique.First,an improved Semi-Global Matching(SGM)technique is used to generate an initial surface expressed by a DSM;then,it is refined by optimizing the objective function which modeled the imaging process with the illumination,surface albedo,and normal object surface.Different from the existing shading-based DEM refinement or generation method,no information about the illumination or the viewing angle is needed while concave/convex ambiguity can be avoided as multi-view images are utilized.Experiments with ZiYuan-3 and GaoFen-7 images show that the proposed method can generate higher accuracy DSM(12.5-56.3%improvement)with sound overall shape and temporarily detailed surface compared with a software solution(SURE)for multi-view stereo.
文摘We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.
基金part of the Areas of Forest Innovation Climate Smart Forestry(Project No.101726),Work Package Modeling,funded by the Austrian Ministry of Agriculture,Forestry,Regions,and Water Management.
文摘Leaf area index(LAI)is a key measure of forest stand physiology and biomass production,and is essential within ecosystem modeling.There are two common approaches to obtaining LAI:(i)terrestrial forest inventory-based“bottom-up”,and(ii)satellite-based“top-down”techniques.The purpose of this study is to compare terrestrial LAI from allometric functions applied to more than 30,000 trees of the Austrian National Forest Inventory(NFI)vs.satellite-based LAI estimates obtained from moderate resolution imaging spectroradiometer(MODIS)and Sentinel(Sentinel-3 TOC reflectance and PROBA-V)data across Austrian forests.We analyzed a satellite pixelto-plot aggregation and obtained the full inventory data set for the LAI comparison.The results suggest that terrestrial vs.satellite(MODIS and Sentinel)driven LAI estimates are consistent,but(i)the variation of the terrestrial forest inventory LAI is larger vs.the pixel average LAI from satellite data,and(ii)any satellite LAI estimation needs a forest stand density correction if the crown competition factor(CCF),a measure for stand density,is<250 to avoid an overestimation in LAI.
基金supported by the Chinese Academy of Sciences Project for Young Scientists in Basic Research(YSBR067)the Natural Science Foundation of Jiangsu Province(BK20240024)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Y2023087)。
文摘Mesoscopy refers to imaging methodologies that provide a field of view(FOV)ranging from several millimeters to centimeters while achieving cellular or even subcellular resolution(Figure 1).This technological framework employs specially designed large-scale objective lenses to correct aberrations across extended FOVs,synchronized with light-field acquisition modalities through either scanning point detection or large-format array detection.Conventional microscopes,constrained by the limitations of objective lenses,exhibit a trade-off between the FOV and resolution.To achieve both high resolution and a large FOV,common approaches such as FOV stitching and Fourier ptychography were employed.However,these methods were extremely slow and imposed numerous constraints on samples.In 2016,a mesoscopic objective lens was introduced to address these challenges,achieving a 6 mm FOV and 0.7 mm resolution,thereby increasing the imaging throughput of conventional objective lenses by orders of magnitude.1 In the same year,this technology was recognized as one of the top ten physics breakthroughs worldwide by Physics World.Since then,mesoscopic imaging technology has gradually gained momentum and has been applied in various fields.
基金supported by the National Key R&D Program of China(2022YFC3401100)the National Natural Science Foundation of China(62025501,92150301,62335008,62405010,and 62305004)the Postdoctoral Fellowship Program of CPSF(GZB20250669)。
文摘Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads to limited depth penetration and compromised imaging fidelity.To overcome these challenges,we propose a novel imaging system,confocal2 spinning-disk image scanning microscopy(C^(2)SD-ISM).It integrates a spinning-disk(SD)confocal microscope,which physically eliminates out-of-focus signals,forming the first confocal level.A digital micromirror device(DMD)is employed for sparse multifocal illumination,combined with a dynamic pinhole array pixel reassignment(DPA-PR)algorithm for ISM super-resolution reconstruction,forming the second confocal level.The dual confocal configuration enhances system resolution,while effectively mitigating scattering background interference.Compared to computational out-of-focus signal removal,SD preserves the original intensity distribution as the penetration depth increases,achieving an imaging depth of up to 180μm.Additionally,the DPA-PR algorithm effectively corrects Stokes shifts,optical aberrations,and other non-ideal conditions,achieving a lateral resolution of 144 nm and an axial resolution of 351 nm,and a linear correlation of up to 92%between the original confocal and the reconstructed image,thereby enabling high-fidelity super-resolution imaging.Moreover,the system's programmable illumination via the DMD allows for seamless realization with structured illumination microscopy modality,offering excellent scalability and ease of use.Altogether,these capabilities make the C^(2)SD-ISM system a versatile tool,advancing cellular imaging and tissue-scale exploration for modern bioimaging needs.
文摘In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.
基金funded by the First-Class Discipline Research Special Project of Inner Mongolia(YLXKZX-NSD-040)the Natural Science Foundation of Inner Mongolia(2022LHQN04003,2023QN04009)+1 种基金the Fundamental Research Funds for the Inner Mongolia University of Finance and Economics(NCXKY25019,NCYWZ22003)the National Social Science Fund of China(22BZS134).
文摘Fires are one of the most destructive natural disasters and have serious long-term effects on the environment,economy,and human health.In Inner Mongolia Autonomous Region,China,frequent fire disturbance occurs due to the intensification of climate change and human activities.It is crucial to understand the fire regime and estimate the probability of regional fire occurrence and reducing fire losses.However,most studies have primarily focused on the dynamic changes,probability of occurrence,and driving mechanisms of wildfires in the grassland and forest land ecosystems in Inner Mongolia,while insufficient research has been conducted on the spatiotemporal variations in active fires and their impact on the wildfire risk in forest land and grassland.Therefore,in this study,we analyzed the active fire regime based on Moderate Resolution Imaging Spectroradiometer(MODIS)thermal anomalies and burned area products from 2000 to 2022.Combined with climate,topographic,landscape,anthropogenic,and vegetation datasets,logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models were chosen to estimate the probability of active fire occurrence at the seasonal timescale.The results revealed that:(1)a total of 100,343 active fires occurred in Inner Mongolia and the burned area reached 6.59×104 km².The number of ignition point exhibited a significant increasing trend,while the burned area exhibited a nonsignificant decreasing trend;(2)four active fire belts were detected,namely,the Hetao-Tumochuan Plain fire belt,Xiliao River Plain fire belt,Songnen Plain fire belt,and Hailar River Eroded Plain fire belt.The centroid of the active fires has shifted 456.4 km toward the southwest;(3)RF model achieved the highest accuracy in estimating the probability of active fire occurrence,followed by CNN,and LR and SVM models had lower accuracies;and(4)the distribution of the high and extremely high fire risk areas largely aligned with the four fire belts.The probability of active fire occurrence was the highest in spring,followed by that in autumn,and it gradually decreased in summer and winter.Our results revealed active fires migrated to the southwest and ignition sources increased,despite reduction of the burned area was not significant.The RF model outperformed the other models in predicting the probability of active fire occurrence.These findings contribute to future fire prevention and prediction in Inner Mongolia.
文摘Introduction of the stepwise phase dispersion compensation layer allowed broadband achromatic metalens to have a high numerical aperture,which enabled high-resolution metalens imaging.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.
文摘The global shortage of fossil fuels,along with the widespread pollution caused by their use,urgently calls for the development of reliable clean energy resources.Among various fundamental strategies,the production of green hydrogen through photochemical or electrochemical water splitting has been extensively studied.
基金supported by National Natural Science Foundation of China(12402336,82201637,U20A2070,and 12025202)National High-Level Talent Project(YQR23069)+6 种基金Natural Science Foundation of Jiangsu Province(BK20230876)the Young Elite Scientist Sponsorship Program by CAST(YESS20210238)Forwardlooking layout projects(1002-ILB24009)Zhejang Provincial Medical and Health Technology Project(Grant No.2024KY246,2025KY180)Scientific Research Foundation of Hangzhou City University(No.J-202402)Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence,Zhejiang University(Grant No.BMI2400025)Hangzhou Science and Technology Bureau.
文摘Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.
文摘Spectral imaging—a suite of techniques combining image acquisition with extremely high color resolution—plays an ever-increasing role in many fields,such as biomedicine,agriculture,geology,archeology,and environmental control.^(1-3)The capability of visualizing,in real time,a tissue or terrain with spatially resolved chemical sensitivity can literally mean the difference between life and death.To appreciate the significance,consider how in vivo spectroscopic sensing of malignant tissue empowers the surgeon to minimize collateral damage during tumor removal while keeping the risk of cancer recurrence low.
文摘A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.
