Super-resolution imaging of low-contrast periodic nanoparticle arrays by microsphere-assisted microscopy

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.

DEEP-UV CONFOCAL FLUORESCENCE IMAGING AND SUPER-RESOLUTION OPTICAL MICROSCOPY OF BIOLOGICAL SAMPLES

A wide range of techniques has been developed to image biological samples at high spatial and temporal resolution.In this paper,we report recent results from deep-UV confocal fAuorescence microscopy to image inherent emission from fuorophores such as tryptophan,and structured ilumination microscopy(SIM)of biological materials.One motivation for developing deep-UV fhuorescence imaging and SIM is to provide methods to complement our measurements in the emerging field of X-ray coherent diffractive imaging.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Hyperspectral Image Super-Resolution Meets Deep Learning:A Survey and Perspective

Hyperspectral image super-resolution,which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation,aims to improve the spatial resolution of the hyperspectral image,which is beneficial for subsequent applications.The development of deep learning has promoted significant progress in hyperspectral image super-resolution,and the powerful expression capabilities of deep neural networks make the predicted results more reliable.Recently,several latest deep learning technologies have made the hyperspectral image super-resolution method explode.However,a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent.To this end,in this survey,we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information.Then,we review the learning-based methods in three categories,including single hyperspectral image super-resolution,panchromatic-based hyperspectral image super-resolution,and multispectral-based hyperspectral image super-resolution.Subsequently,we summarize the commonly used hyperspectral dataset,and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively.Moreover,we briefly introduce several typical applications of hyperspectral image super-resolution,including ground object classification,urban change detection,and ecosystem monitoring.Finally,we provide the conclusion and challenges in existing learning-based methods,looking forward to potential future research directions.

In vivo imaging of the neuronal response to spinal cord injury:a narrative review

Deciphering the neuronal response to injury in the spinal cord is essential for exploring treatment strategies for spinal cord injury(SCI).However,this subject has been neglected in part because appropriate tools are lacking.Emerging in vivo imaging and labeling methods offer great potential for observing dynamic neural processes in the central nervous system in conditions of health and disease.This review first discusses in vivo imaging of the mouse spinal cord with a focus on the latest imaging techniques,and then analyzes the dynamic biological response of spinal cord sensory and motor neurons to SCI.We then summarize and compare the techniques behind these studies and clarify the advantages of in vivo imaging compared with traditional neuroscience examinations.Finally,we identify the challenges and possible solutions for spinal cord neuron imaging.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

SOFFLFM:Super-resolution optical fluctuation Fourierlight-field microscopy

Fourier light-field microscopy(FLFM)uses a microlens aray(MLA)to segment the Fourierplane of the microscopic objective lens to generate multiple two-dimensional perspective views,thereby reconstructing the threedimensional(3D)structure of the sample using 3D deconvo-lution calculation without scanning.However,the resolution of FLFM is stil limited by dif-fraction,and furthermore,it is dependent on the aperture division.In order to improve itsresolution,a super-resolution opticai fuctuation Fourier light-field microscopy(SOFFLFM)wasproposed here,in which the super-resolution optical fluctuation imaging(SOFI)with the abilityof super-resolution was introduced into FLFM.SOFFLFM uses higher-order cumulants statis-tical analysis on an image sequence collected by FLFM,and then carries out 3D deconvolutioncalculation to reconstruct the 3D structure of the sample.The theoretical basis of SOFFLFM onimproving resolution was explained and then verified with the simulations.Simulation resultsdemonstrated that SOFFLFM improved the lateral and axial resolution by more than V2 and 2times in the second-and fourth-order accumulations,compared with that of FLFM.

Single-cell volumetric imaging with light field microscopy: Advances in systems and algorithms

Single-cell volumetric imaging is essential for researching individual characteristics of cells.As a nonscanning imaging technique,lighteld microscopy(LFM)is a critical tool to achieve realtime three-dimensional imaging with the advantage of single-shot.To address the inherent limits including nonuniform resolution and block-wise artifacts,various modied LFM strategies have been developed to provide new insights into the structural and functional information of cells.This review will introduce the principle and development of LFM,discuss the improved approaches based on hardware designs and 3D reconstruction algorithms,and present the applications in single-cell imaging.

IRMIRS:Inception-ResNet-Based Network for MRI Image Super-Resolution

Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

Lipid droplets imaging with three-photon microscopy

Lipid droplets(LDs)participate in many physiological processes,the abnormality of which will cause chronic diseases and pathologies such as diabetes and obesity.It is crucial to monitor the distribution of LDs at high spatial resolution and large depth.Herein,we carried three-photon imaging of LDs in fat liver.Owing to the large three-photon absorption cross-section of the luminogen named NAP-CF_(3)(1:67×10^(-79) cm^(6) s^(2)),three-photon fluorescence fat liver imaging reached the largest depth of 80μm.Fat liver diagnosis was successfully carried out with excellent performance,providing great potential for LDs-associated pathologies research.

Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.

Polarimetric super-resolution algorithm for radar range imaging via spatial smoothing processing

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.

Deep learning facilitated whole live cell fast super-resolution imaging

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.

Near-field multiple super-resolution imaging from Mikaelian lens to generalized Maxwell's fish-eye lens

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.

Super-resolution imaging in glycoscience: New developments and challenges

Carbohydrates on cell surfaces play a crucial role in a wide variety of biological processes,including cell adhesion,recognition and signaling,viral and bacterial infection,in°ammation and metastasis.However,owing to the large diversity and complexity of carbohydrate structure and nongenetically synthesis,glycoscience is the least understood¯eld compared with genomics and proteomics.Although the structures and functions of carbohydrates have been investigated by various conventional analysis methods,the distribution and role of carbohydrates in cell membranes remain elusive.This review focuses on the developments and challenges of super-resolution imaging in glycoscience through introduction of imaging principle and the available°uorescent probes for super-resolution imaging,the labeling strategies of carbohydrates,and the recent applications of super-resolution imaging in glycoscience,which will promote the super-resolution imaging technology as a promising tool to provide new insights into the study of glycoscience.

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

A Review of Clinical Applications for Super-resolution Ultrasound Localization Microscopy

Microvascular structure and hemodynamics are important indicators for the diagnosis and assessment of many diseases and pathologies.The structural and functional imaging of tissue microvasculature in vivo is a clinically significant objective for the development of many imaging modalities.Contrast-enhanced ultrasound(CEUS)is a popular clinical tool for characterizing tissue microvasculature,due to the moderate cost,wide accessibility,and absence of ionizing radiation of ultrasound.

A probability theory for filtered ghost imaging

Based on probability density functions,we present a theoretical model to explain filtered ghost imaging(FGI)we first proposed and experimentally demonstrated in 2017[Opt.Lett.425290(2017)].An analytic expression for the joint intensity probability density functions of filtered random speckle fields is derived according to their probability distributions.Moreover,the normalized second-order intensity correlation functions are calculated for the three cases of low-pass,bandpass and high-pass filterings to study the resolution and visibility in the FGI system.Numerical simulations show that the resolution and visibility predicted by our model agree well with the experimental results,which also explains why FGI can achieve a super-resolution image and better visibility than traditional ghost imaging.

Classification of Human Protein in Multiple Cells Microscopy Images Using CNN

The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473.

Aggregation-induced emission luminogen for in vivo three-photon fuorescence lifetime microscopic imaging

Compared with visible light,near infrared(NIR)light has deeper penetration in biological tisues.Three-photon fuorescence microscopy(3PFM)can effectively utilize the NIR excitation to obtain high-contrast images in the deep tisue.However,the weak three photon fluorescence signals may be not well presented in the traditional fuorescence intensity imaging mode.Fluorescence lifetime of certain probes is insensitive to the intensity of the excitation laser.Moreover,fluorescence lifetimne imaging microscopy(FLIM)can detect weak signals by utilizing time correlated single photon counting(TCSPC)technique.Thus,it would be an improved strategy to combine the 3PFM imaging with the FLIM together.Herein,DCDPP-2TPA,a novel agegation-induced emission luminogen(AIEgen),was adopted as the fluorescent probes.The three-photon absorption cros-section of the AlEgen,which has a deep-red fluorescence emission,was proved to be large.DCDPP-2TPA nanoparticles were synthesized,and the three photon fluorescence lifetime of which was measured in water.Moreover,in vrivo thre-photon fuorescence lifetime microscopic imaging of a craniotomy mouse was conducted via a home made optical system.High contrast cerebrovascular images of different vertical depths were obtained and the maximun depth was about 600 pumn.Even reaching the depth of 600 pum,tiny capillary vessels as small as 1.9 pum could still be distinguished.The three photon fuorescence lifetimes of the capillaries in some representative images were in accord with that of DCDPP-2TPA nanoparticles in water.A vivid 3D reconstruction was further organized to present a wealth of lifetime information.In the future,the combination strategy of 3PFM and FLIM could be further applied in the brain functional imaging.