Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characte...Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy(TDTE) imaging.Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method(SCF-TFM),the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.展开更多
Super-resolution imaging is vital for optical applications, such as high capacity information transmission, real-time bio-molecular imaging, and nanolithography. In recent years, technologies and methods of super-reso...Super-resolution imaging is vital for optical applications, such as high capacity information transmission, real-time bio-molecular imaging, and nanolithography. In recent years, technologies and methods of super-resolution imaging have attracted much attention. Different kinds of novel lenses, from the superlens to the super-oscillatory lens, have been designed and fabricated to break through the diffraction limit. However, the effect of the super-resolution imaging in these lenses is not satisfactory due to intrinsic loss, aberration, large sidebands, and so on. Moreover, these lenses also cannot realize multiple super-resolution imaging. In this research, we introduce the solid immersion mechanism to Mikaelian lens(ML) for multiple super-resolution imaging. The effect is robust and valid for broadband frequencies. Based on conformal transformation optics as a bridge linking the solid immersion ML and generalized Maxwell's fish-eye lens(GMFEL), we also discovered the effect of multiple super-resolution imaging in the solid immersion GMFEL.展开更多
We use the label-free microsphere-assisted microscopy to image low-contrast hexagonally close-packed polystyrene nanoparticle arrays with diameters of 300 and 250 nm.When a nanoparticle array is directly placed on a g...We use the label-free microsphere-assisted microscopy to image low-contrast hexagonally close-packed polystyrene nanoparticle arrays with diameters of 300 and 250 nm.When a nanoparticle array is directly placed on a glass slide,it cannot be distinguished.If a 30-nm-thick Ag film is deposited on the surface of a nanoparticle array,the nanoparticle array with nanoparticle diameters of 300 and 250 nm can be distinguished.In addition,the Talbot effect of the 300-nm-diameter nanoparticle array is also observed.If a nanoparticle sample is assembled on a glass slide deposited with a 30-nm-thick Ag film,an array of 300-nm-diameter nanoparticles can be discerned.We propose that in microsphere-assisted microscopy imaging,the resolution can be improved by the excitation of surface plasmon polaritons(SPPs) on the sample surface or at the sample/substrate interface,and a higher near-field intensity due to the excited SPPs would benefit the resolution improvement.Our study of label-free super-resolution imaging of low-contrast objects will promote the applications of microsphere-assisted microscopy in life sciences.展开更多
We present an imaging approach via sparsity constraint and sparse speckle illumination which can dramatically en- hance the optical system＇s imaging resolution. When the object is illuminated by some sparse speckles ...We present an imaging approach via sparsity constraint and sparse speckle illumination which can dramatically en- hance the optical system＇s imaging resolution. When the object is illuminated by some sparse speckles and the sparse reconstruction algorithm is utilized to restore the blur image, numerical simulated results demonstrate that the image, whose resolution exceeds the Rayleigh limit, can be stably reconstructed even if the detection signal-to-noise ratio （SNR） is less than 10 dB. Factors affecting the quality of the reconstructed image, such as the coded pattern＇s sparsity and the detection SNR, are also studied,展开更多
A fully convolutional encoder-decoder network(FCEDN),a deep learning model,was developed and applied to image scanning microscopy(ISM).Super-resolution imaging was achieved with a 78μm×78μm field of view and 12...A fully convolutional encoder-decoder network(FCEDN),a deep learning model,was developed and applied to image scanning microscopy(ISM).Super-resolution imaging was achieved with a 78μm×78μm field of view and 12.5 Hz-40 Hz imaging frequency.Mono and dual-color continuous super-resolution images of microtubules and cargo in cells were obtained by ISM.The signal-to-noise ratio of the obtained images was improved from 3.94 to 22.81 and the positioning accuracy of cargoes was enhanced by FCEDN from 15.83±2.79 nm to 2.83±0.83 nm.As a general image enhancement method,FCEDN can be applied to various types of microscopy systems.Application with conventional spinning disk confocal microscopy was demonstrated and significantly improved images were obtained.展开更多
Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vi...Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.展开更多
A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing pr...A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.展开更多
Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,...Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.展开更多
Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identi...Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identification.But in traditional methods via deep convolution neural net-works,indiscriminately extracting and fusing spectral and spatial features makes it challenging toutilize the differentiated information across adjacent spectral channels.Thus,we proposed a multi-branch interleaved iterative upsampling hyperspectral image super-resolution reconstruction net-work(MIIUSR)to address the above problems.We reinforce spatial feature extraction by integrat-ing detailed features from different receptive fields across adjacent channels.Furthermore,we pro-pose an interleaved iterative upsampling process during the reconstruction stage,which progres-sively fuses incremental information among adjacent frequency bands.Additionally,we add twoparallel three dimensional(3D)feature extraction branches to the backbone network to extractspectral and spatial features of varying granularity.We further enhance the backbone network’sconstruction results by leveraging the difference between two dimensional(2D)channel-groupingspatial features and 3D multi-granularity features.The results obtained by applying the proposednetwork model to the CAVE test set show that,at a scaling factor of×4,the peak signal to noiseratio,spectral angle mapping,and structural similarity are 37.310 dB,3.525 and 0.9438,respec-tively.Besides,extensive experiments conducted on the Harvard and Foster datasets demonstratethe superior potential of the proposed model in hyperspectral super-resolution reconstruction.展开更多
Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process...Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.展开更多
Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supp...Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supply a high neutron flux and a time-consuming detection procedure involving direct collimating measurements.In this study,a new method based on low neutron flux was proposed.A single-pixel gamma-ray detector combined with random pattern gamma-ray masks was used to measure the characteristic gamma rays emitted from the sample.Images of the elemental distribution in the sample,comprising 30×30 pixels,were reconstructed using the maximum-likelihood expectation-maximization algorithm.The results demonstrate that the elemental imaging of the sample can be accurately determined using this method.The proposed approach,which eliminates the need for high neutron flux and scanning measurements,can be used for in-field imaging applications.展开更多
Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
Polygonati rhizoma is often used in Chinese medicine and as food.In this study,atmospheric pressure matrixassisted laser desorption ionization and quadruple-time-of-flight(MALDI-Q-TOF)mass spectrometry techniques were...Polygonati rhizoma is often used in Chinese medicine and as food.In this study,atmospheric pressure matrixassisted laser desorption ionization and quadruple-time-of-flight(MALDI-Q-TOF)mass spectrometry techniques were applied to P.rhizoma samples from Polygonatum cyrtonema Hua species.Positive ions were mainly detected in the mass range of m/z 200-600,while negative ions were mainly observed in the mass range of m/z 100-450.A total of 263 components were identified and the spatial distribution and changes in saccharides contents during the steaming process of P.rhizoma were investigated.Monosaccharide and disaccharide exhibit a relatively uniform distribution,while the oligosaccharides were mainly found in the bast of fresh P.rhizoma.Although the contents of monosaccharide and disaccharide were increased during steaming,that of trisaccharide,tetrasaccharide,and pentasaccharide were decreased.We used the 5 saccharide types with the greatest variation in content as variables for the principal component analysis(PCA)and cluster analysis.Both PCA and cluster analysis showed that these 5 saccharides can be used as markers in the steaming process of the P.rhizoma.Present study of mass spectrometry imaging provides novel insights into the spatiotemporal accumulation patterns of saccharides in P.rhizoma,improving our understanding of the steaming process.展开更多
In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate...In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate(μ_(max))of Brochothrix thermosphacta in chilled beef at isothermal temperatures(4-25℃).Three different methods were compared for model development,particularly using(Model Ⅰ)the predicted microbial loads from partial least squares regression of the whole spectral variables;(Model Ⅱ)the selected spectral variables related to microbial loads;and(Model Ⅲ)the first principal scores of HSI spectra by principal component analysis.Consequently,Model Ⅰ showed the best ability to predict the microbial loads of B.thermosphacta,with the coefficient of determination(R_(v)^(2))and root mean square error in internal validation(RMSEV)of 0.921 and 0.498(lg(CFU/g)).The T_(min)(-12.32℃)andμmax can be well estimated with R^(2) and root mean square error(RMSE)of 0.971 and 0.276(lg(CFU/g)),respectively.The upward trend ofμmax with temperature was similar to that of the plate count method.HSI technique thus can be used as a simple method for one-step growth simulation of B.thermosphacta in chilled beef during storage.展开更多
Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear ...Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.展开更多
Terahertz(THz)imaging has drawn significant attention because THz wave has a unique capability to transient,ultrawide spectrum and low photon energy.However,the low resolution has always been a problem due to its long...Terahertz(THz)imaging has drawn significant attention because THz wave has a unique capability to transient,ultrawide spectrum and low photon energy.However,the low resolution has always been a problem due to its long wavelength,limiting their application of fields practical use.In this paper,we proposed a complex one-shot super-resolution(COSSR)framework based on a complex convolution neural network to restore superior THz images at 0.35 times wavelength by extracting features directly from a reference measured sample and groundtruth without the measured PSF.Compared with real convolution neural network-based approaches and complex zero-shot super-resolution(CZSSR),COSSR delivers at least 6.67,0.003,and 6.96%superior higher imaging efficacy in terms of peak signal to noise ratio(PSNR),mean square error(MSE),and structural similarity index measure(SSIM),respectively,for the analyzed data.Additionally,the proposed method is experimentally demonstrated to have a good generalization and to perform well on measured data.The COSSR provides a new pathway for THz imaging super-resolution(SR)reconstruction below the diffraction limit.展开更多
A filtered ghost imaging(GI)protocol is proposed that enables the Rayleigh diffraction limit to be exceeded in an intensity correlation system;a super-resolution reconstructed image is achieved by low-pass filtering o...A filtered ghost imaging(GI)protocol is proposed that enables the Rayleigh diffraction limit to be exceeded in an intensity correlation system;a super-resolution reconstructed image is achieved by low-pass filtering of the measured intensities.In a lensless GI experiment performed with spatial bandpass filtering,the spatial resolution can exceed the Rayleigh diffraction bound by more than a factor of 10.The resolution depends on the bandwidth of the filter,and the relationship between the two is investigated and discussed.In combination with compressed sensing programming,not only high resolution can be maintained but also image quality can be improved,while a much lower sampling number is sufficient.展开更多
Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding th...Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding the application scope of well logging technology.This article reviews the development history of the technology and focuses on introducing key methods,software,and on-site applications of acoustic reflection imaging logging technology.Based on the analyses of major challenges faced by existing technologies,and in conjunction with the practical production requirements of oilfields,the further development directions of acoustic reflection imaging logging are proposed.Following the current approach that utilizes the reflection coefficients,derived from the computation of acoustic slowness and density,to perform seismic inversion constrained by well logging,the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves.It is essential to advance research in imaging of fractures within shale reservoirs,the assessment of hydraulic fracturing effectiveness,the study of geosteering while drilling,and the innovation in instruments of acoustic reflection imaging logging technology.展开更多
This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techni...This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 12174085)the Key Research and Development Project of Changzhou, Jiangsu Province, China (Grant No. CE20235054)the Postgraduate Research and Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX24 0833)。
文摘Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy(TDTE) imaging.Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method(SCF-TFM),the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.
基金Project supported by the National Natural Science Foundation of China (Grant No. 92050102)the National Key Research and Development Program of China (Grant No. 2020YFA0710100)the Fundamental Research Funds for Central Universities, China (Grant Nos. 20720200074, 20720220134, 202006310051, and 20720220033)。
文摘Super-resolution imaging is vital for optical applications, such as high capacity information transmission, real-time bio-molecular imaging, and nanolithography. In recent years, technologies and methods of super-resolution imaging have attracted much attention. Different kinds of novel lenses, from the superlens to the super-oscillatory lens, have been designed and fabricated to break through the diffraction limit. However, the effect of the super-resolution imaging in these lenses is not satisfactory due to intrinsic loss, aberration, large sidebands, and so on. Moreover, these lenses also cannot realize multiple super-resolution imaging. In this research, we introduce the solid immersion mechanism to Mikaelian lens(ML) for multiple super-resolution imaging. The effect is robust and valid for broadband frequencies. Based on conformal transformation optics as a bridge linking the solid immersion ML and generalized Maxwell's fish-eye lens(GMFEL), we also discovered the effect of multiple super-resolution imaging in the solid immersion GMFEL.
基金Project supported by the National Natural Science Foundation of China(Grant No.61673287)。
文摘We use the label-free microsphere-assisted microscopy to image low-contrast hexagonally close-packed polystyrene nanoparticle arrays with diameters of 300 and 250 nm.When a nanoparticle array is directly placed on a glass slide,it cannot be distinguished.If a 30-nm-thick Ag film is deposited on the surface of a nanoparticle array,the nanoparticle array with nanoparticle diameters of 300 and 250 nm can be distinguished.In addition,the Talbot effect of the 300-nm-diameter nanoparticle array is also observed.If a nanoparticle sample is assembled on a glass slide deposited with a 30-nm-thick Ag film,an array of 300-nm-diameter nanoparticles can be discerned.We propose that in microsphere-assisted microscopy imaging,the resolution can be improved by the excitation of surface plasmon polaritons(SPPs) on the sample surface or at the sample/substrate interface,and a higher near-field intensity due to the excited SPPs would benefit the resolution improvement.Our study of label-free super-resolution imaging of low-contrast objects will promote the applications of microsphere-assisted microscopy in life sciences.
基金Project supported by the National Natural Science Foundation of China(Grant No.61571427)
文摘We present an imaging approach via sparsity constraint and sparse speckle illumination which can dramatically en- hance the optical system＇s imaging resolution. When the object is illuminated by some sparse speckles and the sparse reconstruction algorithm is utilized to restore the blur image, numerical simulated results demonstrate that the image, whose resolution exceeds the Rayleigh limit, can be stably reconstructed even if the detection signal-to-noise ratio （SNR） is less than 10 dB. Factors affecting the quality of the reconstructed image, such as the coded pattern＇s sparsity and the detection SNR, are also studied,
基金Project supported by the China Postdoctoral Science Foundation,the National Key Research and Development Program of China for Y.S.(Grant No.2017YFA0505300)the National Science Foundation of China for Y.S.(Grant No.21825401)。
文摘A fully convolutional encoder-decoder network(FCEDN),a deep learning model,was developed and applied to image scanning microscopy(ISM).Super-resolution imaging was achieved with a 78μm×78μm field of view and 12.5 Hz-40 Hz imaging frequency.Mono and dual-color continuous super-resolution images of microtubules and cargo in cells were obtained by ISM.The signal-to-noise ratio of the obtained images was improved from 3.94 to 22.81 and the positioning accuracy of cargoes was enhanced by FCEDN from 15.83±2.79 nm to 2.83±0.83 nm.As a general image enhancement method,FCEDN can be applied to various types of microscopy systems.Application with conventional spinning disk confocal microscopy was demonstrated and significantly improved images were obtained.
基金supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (No.GK249909299001-036)National Key Research and Development Program of China (No. 2023YFB4502803)Zhejiang Provincial Natural Science Foundation of China (No.LDT23F01014F01)。
文摘Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.
基金Supported by the National Naturral Science Foundation of China(61301191)
文摘A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.
基金National Natural Science Foundation of China (62275267, 62335018, 12127805, 62105359)National Key Research and Development Program of China (2021YFF0700303, 2022YFE0100700)Youth Innovation Promotion Association, CAS (2021401)
文摘Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
基金the National Natural Science Foun-dation of China(Nos.61471263,61872267 and U21B2024)the Natural Science Foundation of Tianjin,China(No.16JCZDJC31100)Tianjin University Innovation Foundation(No.2021XZC0024).
文摘Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identification.But in traditional methods via deep convolution neural net-works,indiscriminately extracting and fusing spectral and spatial features makes it challenging toutilize the differentiated information across adjacent spectral channels.Thus,we proposed a multi-branch interleaved iterative upsampling hyperspectral image super-resolution reconstruction net-work(MIIUSR)to address the above problems.We reinforce spatial feature extraction by integrat-ing detailed features from different receptive fields across adjacent channels.Furthermore,we pro-pose an interleaved iterative upsampling process during the reconstruction stage,which progres-sively fuses incremental information among adjacent frequency bands.Additionally,we add twoparallel three dimensional(3D)feature extraction branches to the backbone network to extractspectral and spatial features of varying granularity.We further enhance the backbone network’sconstruction results by leveraging the difference between two dimensional(2D)channel-groupingspatial features and 3D multi-granularity features.The results obtained by applying the proposednetwork model to the CAVE test set show that,at a scaling factor of×4,the peak signal to noiseratio,spectral angle mapping,and structural similarity are 37.310 dB,3.525 and 0.9438,respec-tively.Besides,extensive experiments conducted on the Harvard and Foster datasets demonstratethe superior potential of the proposed model in hyperspectral super-resolution reconstruction.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12122403 and 12327807).
文摘Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.
基金supported by the National Natural Science Foundation of China(Nos.12105143 and 11975121)the China Postdoctoral Science Foundation(No.2023M741453)+1 种基金the Engineering Research Center of Nuclear Technology Application(No.HJSJYB2020-1)the Key Laboratory of Ionizing Radiation Metering and Safety Evaluation for Jiangsu Province Market Regulation,and the Jiangsu Province Excellent Postdoctoral Program(No.JB23057).
文摘Neutron-induced gamma-ray imaging is a spectroscopic technique that uses characteristic gamma rays to infer the elemental distribution of an object.Currently,this technique requires the use of large facilities to supply a high neutron flux and a time-consuming detection procedure involving direct collimating measurements.In this study,a new method based on low neutron flux was proposed.A single-pixel gamma-ray detector combined with random pattern gamma-ray masks was used to measure the characteristic gamma rays emitted from the sample.Images of the elemental distribution in the sample,comprising 30×30 pixels,were reconstructed using the maximum-likelihood expectation-maximization algorithm.The results demonstrate that the elemental imaging of the sample can be accurately determined using this method.The proposed approach,which eliminates the need for high neutron flux and scanning measurements,can be used for in-field imaging applications.
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金funded by the Science and Technology Innovation Program of Hunan Province(2022RC1224,2022ZYC010)the Changsha Science and Technology Program(kh2004018)the Training Program for Excellent Young Innovators of Changsha(kq2206064)。
文摘Polygonati rhizoma is often used in Chinese medicine and as food.In this study,atmospheric pressure matrixassisted laser desorption ionization and quadruple-time-of-flight(MALDI-Q-TOF)mass spectrometry techniques were applied to P.rhizoma samples from Polygonatum cyrtonema Hua species.Positive ions were mainly detected in the mass range of m/z 200-600,while negative ions were mainly observed in the mass range of m/z 100-450.A total of 263 components were identified and the spatial distribution and changes in saccharides contents during the steaming process of P.rhizoma were investigated.Monosaccharide and disaccharide exhibit a relatively uniform distribution,while the oligosaccharides were mainly found in the bast of fresh P.rhizoma.Although the contents of monosaccharide and disaccharide were increased during steaming,that of trisaccharide,tetrasaccharide,and pentasaccharide were decreased.We used the 5 saccharide types with the greatest variation in content as variables for the principal component analysis(PCA)and cluster analysis.Both PCA and cluster analysis showed that these 5 saccharides can be used as markers in the steaming process of the P.rhizoma.Present study of mass spectrometry imaging provides novel insights into the spatiotemporal accumulation patterns of saccharides in P.rhizoma,improving our understanding of the steaming process.
基金supported by Key Research&Development Program of Jiangsu Province in China(BE2020693)Major Project of Science and Technology of Anhui Province(201903a06020010)+1 种基金Joint Key Project of Science and Technology Innovation of Yangtze River Delta in Anhui Province(202004g01020009)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘In this work,one-step growth models using hyperspectral imaging(HSI)(400-1000 nm)were successfully developed in order to estimate the microbial loads,minimum growth temperature(T_(min))and maximum specific growth rate(μ_(max))of Brochothrix thermosphacta in chilled beef at isothermal temperatures(4-25℃).Three different methods were compared for model development,particularly using(Model Ⅰ)the predicted microbial loads from partial least squares regression of the whole spectral variables;(Model Ⅱ)the selected spectral variables related to microbial loads;and(Model Ⅲ)the first principal scores of HSI spectra by principal component analysis.Consequently,Model Ⅰ showed the best ability to predict the microbial loads of B.thermosphacta,with the coefficient of determination(R_(v)^(2))and root mean square error in internal validation(RMSEV)of 0.921 and 0.498(lg(CFU/g)).The T_(min)(-12.32℃)andμmax can be well estimated with R^(2) and root mean square error(RMSE)of 0.971 and 0.276(lg(CFU/g)),respectively.The upward trend ofμmax with temperature was similar to that of the plate count method.HSI technique thus can be used as a simple method for one-step growth simulation of B.thermosphacta in chilled beef during storage.
基金support from CAS West Light Grant (xbzgzdsys-202206)National Key Research and Development Program of China (2021YFA1401003).
文摘Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.
基金"XingLiaoYingCai"Talents of Liaoning Province,China(Grant No.XLYC2007074)Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program(Grant No.RC200512)+1 种基金Project supported by“XingLiaoYingCai"Talents of Liaoning Province,China(Grant No.XLYC2007074)Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program(Grant No.RC200512),。
文摘Terahertz(THz)imaging has drawn significant attention because THz wave has a unique capability to transient,ultrawide spectrum and low photon energy.However,the low resolution has always been a problem due to its long wavelength,limiting their application of fields practical use.In this paper,we proposed a complex one-shot super-resolution(COSSR)framework based on a complex convolution neural network to restore superior THz images at 0.35 times wavelength by extracting features directly from a reference measured sample and groundtruth without the measured PSF.Compared with real convolution neural network-based approaches and complex zero-shot super-resolution(CZSSR),COSSR delivers at least 6.67,0.003,and 6.96%superior higher imaging efficacy in terms of peak signal to noise ratio(PSNR),mean square error(MSE),and structural similarity index measure(SSIM),respectively,for the analyzed data.Additionally,the proposed method is experimentally demonstrated to have a good generalization and to perform well on measured data.The COSSR provides a new pathway for THz imaging super-resolution(SR)reconstruction below the diffraction limit.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2018YFB0504302 and 2017YFB0503301)Defense Industrial Technology Development Program(Grant No.D040301-1)。
文摘A filtered ghost imaging(GI)protocol is proposed that enables the Rayleigh diffraction limit to be exceeded in an intensity correlation system;a super-resolution reconstructed image is achieved by low-pass filtering of the measured intensities.In a lensless GI experiment performed with spatial bandpass filtering,the spatial resolution can exceed the Rayleigh diffraction bound by more than a factor of 10.The resolution depends on the bandwidth of the filter,and the relationship between the two is investigated and discussed.In combination with compressed sensing programming,not only high resolution can be maintained but also image quality can be improved,while a much lower sampling number is sufficient.
基金Supported by the PetroChina Science and Technology Project(2021DJ4002,2022DJ3908)。
文摘Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies,such as fractures,caves and faults,within a range of tens of meters from the wellbore,greatly expanding the application scope of well logging technology.This article reviews the development history of the technology and focuses on introducing key methods,software,and on-site applications of acoustic reflection imaging logging technology.Based on the analyses of major challenges faced by existing technologies,and in conjunction with the practical production requirements of oilfields,the further development directions of acoustic reflection imaging logging are proposed.Following the current approach that utilizes the reflection coefficients,derived from the computation of acoustic slowness and density,to perform seismic inversion constrained by well logging,the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves.It is essential to advance research in imaging of fractures within shale reservoirs,the assessment of hydraulic fracturing effectiveness,the study of geosteering while drilling,and the innovation in instruments of acoustic reflection imaging logging technology.
基金support from the National Natural Science Foundation of China(Nos.62205259,62075175,61975254,62375212,62005203 and 62105254)the Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology(No.B022420004)the Fundamental Research Funds for the Central Universities(No.ZYTS23125).
文摘This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.
