Application of Superresolution Techniques to Radar Imaging

Modern spectral estimation techniques (superresolution in technical jargon) have been applied to many fields of signal processing since many years[1][2]. Application to radar imaging, mainlyto ISAR (Inverse Synthetic Aperture Radar) is documented in some recent papers[3] to [6]. Applications have been attempted also to SAR (Synthetic Aperture Radar)[7][8]. In these fields the benefit ofspectral estimation reveals in a resolution beyond the Rayleigh limits set by compressed pulse andsynthetic aperture lengths. Furthermore very low sidelobes of point scatterer response are obtained.In this paper superresolution has been applied both to simulated stepped-frequency ISAR dataand to real ERS-1 SAR data; the achieved results are encouraging and suggest a more extensivepractical application of the technique. The paper is organized in two parts. In the first we have applied the autoregressive (AR) and the minimum variance (MV)-Capon methods to improve therange resolution of simulated ISAR data. In the second part we have conceived an upgraded versionof spectral analysis (SPECAN) processing to obtain a SAR image of better quality. The method hasbeen tested on recorded live data of ERS-1 mission.

Efficient hybrid method for time reversal superresolution imaging

An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.

Influences of phase performances shift on superresolution of annular filters

This paper investigates the influences of phase shift on superresolution performances of annular filters. Firstly, it investigates the influence of phase shift on axial superresolution. It proves theoretically that axial superresolution can not be obtained by two-zone phase filter with phase shift n, and it gets the phase shift with which axial superresolution can be brought by two-zone phase filter. Secondly, it studies the influence of phase shift on transverse superresolution. It finds that the three-zone phase filter with arbitrary phase shift has an almost equal optimal transverse gain to that of commonly used three-zone phase filter, but can produce a much higher axial superresolution gain. Thirdly, it investigates the influence of phase shift on three-dimensional superresolution. Three-dimensional superresolution capability and design margin of three-zone complex filter with arbitrary phase shift are obtained, which presents the theoretical basis for three-dimensional superresolution design. Finally, it investigates the influence of phase shift on focal shift. To obtain desired focal shifts, it designs a series of three-zone phase filters with different phase shifts. A spatial light modulator （SLM） is used to implement the designed filters. By regulating the voltage imposed on the SLM, an accurate focal shift control is obtained,

Dynamic Data Updating Algorithm for Image Superresolution Reconstruction

A dynamic data updating algorithm for image superesolution is proposed. On the basis of Delaunay triangulation and its local updating property, this algorithm can update the changed region directly under the circumstances that only a part of the source images has been changed. For its high efficiency and adaptability, this algorithm can serve as a fast algorithm for image superesolution reconstruction.

Superresolution live-cell imaging reveals that the localization of TMEM106B to filopodia in oligodendrocytes is compromised by the hypomyelination-related D252N mutation

Hypomyelination leukodystrophies constitute a group of heritable white matter disorders exhibiting defective myelin development.Initially identified as a lysosomal protein,the TMEM106B D252N mutant has recently been associated with hypomyelination.However,how lysosomal TMEM106B facilitates myelination and how the D252N mutation disrupts that process are poorly understood.We used superresolution Hessian structured illumination microscopy(Hessian-SIM)and spinning discconfocal structured illumination microscopy(SD-SIM)to find that the wild-type TMEM106B protein is targeted to the plasma membrane,filopodia,and lysosomes in human oligodendrocytes.The D252N mutation reduces the size of lysosomes in oligodendrocytes and compromises lysosome changes upon starvation stress.Most importantly,we detected reductions in the length and number of filopodia in cells expressing the D252N mutant.PLP1 is the most abundant myelin protein that almost entirely colocalizes with TMEM106B,and coexpressing PLP1 with the D252N mutant readily rescues the lysosome and filopodia phenotypes of cells.Therefore,interactions between TMEM106B and PLP1 on the plasma membrane are essential for filopodia formation and myelination in oligodendrocytes,which may be sustained by the delivery of these proteins from lysosomes via exocytosis.

Lightweight and Efficient Attention-Based Superresolution Generative Adversarial Networks

To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.

Wide-field in situ multiplexed Raman imaging with superresolution

Because of the fingerprint-like specificity of its characteristic spectrogram, Raman spectral imaging has been applied widely in various research areas. Using a combination of structured illumination with the surface- enhanced Raman scattering （SERS） technique, wide-field Raman imaging is developed with a significant improve- ment in spatial resolution. As a result of the relatively narrow Raman characteristic peaks, optically encoded SERS nanoparticles can be used to perform multiplexed imaging. The results show excellent superresolution wide-fidd multiplexed imaging performance. The developed technique has extraordinary potential for applications in biological imaging and other related fields.

Direct detection of the transient superresolution effect of nonlinear saturation absorption thin films

Using a strong nonlinear saturation absorption effect is one technique for breaking through the diffraction limit. In this technique, formation of a dynamic and reversible optical pinhole channel and transient superresolution is critical. In this work, a pump–probe transient detection and observation–experimental setup is constructed to explore the formation process directly. A Ge2Sb2Te5 thin film with strong nonlinear saturation absorption is investigated. The dynamic evolution of the optical pinhole channel is detected and imaged, and the transient superresolution spot is directly captured experimentally. Results verify that the superresolution effect originates from the generation of an optical pinhole channel and that the formation of the optical pinhole channel is dynamic and reversible. A good method is provided for direct detection and observation of the transient process of the superresolution effect of nonlinear thin films.

Superresolution imaging of DNA tetrahedral nanostructures in cells by STED method with continuous wave lasers

DNA tetrahedral nanostructures are considered to be uew nanocarriers because they can be precisely controlled and hold excellent penetration ability to the cellular membrane. Although the DNA tetrahedral nanostructure is extensively studied in biology and medicine, its behavior in the cells with nanoscale resolution is not understood clearly. In this letter, we demonstrate superrcsolution fluorescence imaging of the distribution of DNA tetrahedral nanostructures in the cell with a simulated emission depletion （STED） microscope, which is built based on a conventional eonfocal microscope and can t）rovide a resolution of 70 nm.

Analysis of Strehl ratio limit with superresolution binary phase filters

Several pupil filtering techniques have been developed in the last few years to obtain transverse superresolution （a narrower point spread function core）. Such a core decrease entails two relevant limitations： a decrease of the peak intensity and an increase of the sidelobe intensity. Here, we calculate the Strehl ratio as a function of the core size for the most used binary phase filters. Furthermore, we show that this relation approaches the fundamental limit of the attainable Strehl ratio at the focal plane for any filter. Finally, we show the calculation of the peak-to-sidelobe ratio in order to check the system viability in every application.

Superresolution imaging of telomeres with continuous wave stimulated emission depletion (STED) microscope

The significant role of telomeres in cells has attracted much attention since they were discovered.Fluorescence imaging is an effective method to study subcellular structures like telomeres.However,the diffraction limit of traditional optical microscope hampers further investigation on them.Recent progress on superresolution fluorescence microscopy has broken this limit.In this work,we used stimulated emission depletion(STED) microscope to observe fluorescence-labeled telomeres in interphase cell nuclei.The results showed that the size of fluorescent puncta representing telomeres under the STED microscope was much smaller than that under the confocal microscope.Two adjacent telomeres were clearly separated via STED imaging,which could hardly be discriminated by confocal microscopy due to the diffraction limit.We conclude that STED microscope is a more powerful tool that enable us to obtain detailed information about telomeres.

Multiplexed Superresolution CRISPR Imaging of Chromatin in Living Cells

Visualization of the spatiotemporal organization of chromatin is highly desirable in the study of genome function regulations.Clustered regularly interspa-ced short palindromic repeats(CRISPR)/CRISPR-associated endonuclease system has shown great promise for application in real-time chromatin imag-ing due to its DNA targeting ability in living cells.Previous studies typically used fluorescent proteins to generate fluorescent signals which,however,have trade-offs among signal intensity,multiplexibility,and simplicity.

BAYESIAN IMAGE SUPERRESOLUTION AND HIDDEN VARIABLE MODELING

Superresolution is an image processing technique that estimates an original high-resolutionimage from its low-resolution and degraded observations.In superresolution tasks,there have beenproblems regarding the computational cost for the estimation of high-dimensional variables.Theseproblems are now being overcome by the recent development of fast computers and the developmentof powerful computational techniques such as variational Bayesian approximation.This paper reviewsa Bayesian treatment of the superresolution problem and presents its extensions based on hierarchicalmodeling by employing hidden variables.

Advanced Generative Adversarial Network for Image Superresolution

The superresolution(SR)method based on generative adversarial networks(GANs)cannot adequately capture enough diversity from training data,resulting in misalignment between input low resolution(LR)images and output high resolution(HR)images.GAN training has difficulty converging.Based on this,an advanced GAN-based image SR reconstructionmethod is presented.First,the dense connection residual block and attention mechanism are integrated into the GAN generator to improve high-frequency feature extraction.Meanwhile,an added discriminator is added into the GAN discriminant network,which forms a dual discriminator to ensure that the process of training is stable.Second,the more robust Charbonnier loss is used instead of the mean square error(MSE)loss to compare similarities between the obtained image and actual image,and the total variation(TV)loss is employed to smooth the training results.Finally,the experimental results indicate that global structures can be better reconstructed using the method of this paper and texture details of images compared with other SOTA methods.The peak signal-to-noise ratio(PSNR)values by the method of this paper are improved by an average of 2.24 dB,and the structural similarity index measure(SSIM)values are improved by an average of 0.07.

Terahertz technology in intraoperative neurodiagnostics:A review

Terahertz(THz)technology offers novel opportunities in biology and medicine,thanks to the unique features of THzwave interactions with tissues and cells.Among them,we particularly notice strong sensitivity of THz waves to the tissue water,as a medium for biochemical reactions and a main endogenous marker for THz spectroscopy and imaging.Tissues of the brain have an exceptionally high content of water.This factor,along with the features of the structural organization and biochemistry of neuronal and glial tissues,makes the brain an exciting subject to study in the THz range.In this paper,progress and prospects of THz technology in neurodiagnostics are overviewed,including diagnosis of neurodegenerative disease,myelin deficit,tumors of the central nervous system(with an emphasis on brain gliomas),and traumatic brain injuries.Fundamental and applied challenges in study of the THz-wave–brain tissue interactions and development of the THz biomedical tools and systems for neurodiagnostics are discussed.

Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data

In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.

Possible implications of dysregulated nicotinic acetylcholine receptor diffusion and nanocluster formation in myasthenia gravis

Myasthenia gravis is a rare and invalidating disease affecting the neuromuscular junction of voluntary muscles.The classical form of this autoimmune disease is characterized by the presence of antibodies against the most abundant protein in the neuromuscular junction,the nicotinic acetylcholine receptor.Other variants of the disease involve autoimmune attack of non-receptor scaffolding proteins or enzymes essential for building or maintaining the integrity of this peripheral synapse.This review summarizes the participation of the above proteins in building the neuromuscular junction and the destruction of this cholinergic synapse by autoimmune aggression in myasthenia gravis.The review also covers the application of a powerful biophysical technique,superresolution optical microscopy,to image the nicotinic receptor in live cells and follow its motional dynamics.The hypothesis is entertained that anomalous nanocluster formation by antibody crosslinking may lead to accelerated endocytic internalization and elevated turnover of the receptor,as observed in myasthenia gravis.

Resolution of Two Unequally Bright Points of Apodized Optical Systems with Asymmetric Circular Antenna Arrays

Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete asymmetric apodization have suppressed side-lobes and a narrower central peak in the image plane termed as PSF good side on alternatively the right and left of the strong spectral point facilitates to detect the presence of weak spectral point in the vicinity of bright spectral point. The results of investigations on optimum discrete pupil function with semicircular arrays on the intensity distributions in the composite image of two object points with widely varying in their intensities under various degree of coherence of illumination have been studied. Sparrow resolution limits and the dip in central intensity as function of degree of coherence of the illumination (γ), intensity ratio (α), degree of asymmetric apodization (b) and number of discrete elements in semicircular array (n). The efficiency of aperture functions is discussed in terms of these parameters. Pupil function capabilities in redistribution of energy in composite image of two object points in close vicinity have been verified for different considerations. Current study has found an improvement in two-point resolution characteristics compared to their unapodized counter part. Fourier analytical properties of an optical system are presented for evaluation of this practical problem.

THREE ASPECTS OF ISAR SIGNAL PROCESSING

Inverse synthetic aperture radar (ISAR) is a high resolution imaging radar of non-cooperative target. Its signal processing involves two links,i. e. motion compensation and image formation. In this Paper, three critical problems of ISAR signal processing are investigated.They are: (1) superresolution ISAR imaging with multilayer neural network ; (2) motion compensation of ISAR in frequency domain and (3) cross-range scaling of ISAR. The proposed approaches are used to process the real data of model B-52 collected in a microwave anechoic chamber. The reconstructed images show that the proposed approaches are correct and effective.

EIGNVALUE SHIFT SUPPERRESOLUTION ALGORITHM

This paper provides a new approach to spatial spectral estimation, called eigenvalue shift superresolution algorithm. This method avoids the inverse covariance matrix. It can be applied to the processing of data received by spatially distributed array of sensors.