Resolution Enhancement Method Research Articles

High-resolution 3D imaging in light-field microscopy through Stokes matrices and data fusion.

The trade-off between the lateral and vertical resolution has long posed challenges to the efficient and widespread application of Fourier light-field microscopy, a highly scalable 3D imaging tool. Although existing methods for resolution enhancement can improve the measurement result to a certain extent, they come with limitations in terms of accuracy and applicable specimen types. To address these problems, this paper proposed a resolution enhancement scheme utilizing data fusion of polarization Stokes vectors and light-field information for Fourier light-field microscopy system. By introducing the surface normal vector information obtained from polarization measurement and integrating it with the light-field 3D point cloud data, 3D reconstruction results accuracy is highly improved in axial direction. Experimental results with a Fourier light-field 3D imaging microscope demonstrated a substantial enhancement of vertical resolution with a depth resolution to depth of field ratio of 0.19%. This represented approximately 44 times the improvement compared to the theoretical ratio before data fusion, enabling the system to access more detailed information with finer measurement accuracy for test samples. This work not only provides a feasible solution for breaking the limitations imposed by traditional light-field microscope hardware configurations but also offers superior 3D measurement approach in a more cost-effective and practical manner.

Elimination of homogeneous broadening in 1H solid-state NMR.

1H solid-state NMR spectra are plagued by low resolution, necessitating the use of complex pulse sequences or specialized equipment. We introduce a new resolution enhancement method, inspired by super-resolution microscopy, that uses a 2D Hahn-echo experiment to constrain deconvolution. The result is an effective doubling of the MAS frequency.

A Spatial Resolution Enhancement Method of Microwave Radiation Imager Data Based on Data Matching and Transformer

A Spatial Resolution Enhancement Method of Microwave Radiation Imager Data Based on Data Matching and Transformer

A GPR Resolution Enhancement Method Based on Weakly Supervised Learning

Ground penetrating radar (GPR) is mainly used to detect underground structures in archaeology, military and other fields. In practice, the detection depth and resolution of GPR data are often difficult to balance. Therefore, it is vital to develop a method that can obtain both deeper distance and higher resolution. In this paper, a weakly supervised method for improving the resolution of GPR data based on the Cycle-Consistent Adversarial Network (Cycle-GAN) is proposed, which improves the quality of GPR data by learning the mapping relation between low-resolution and unpaired high-resolution data. The actual data is used to verify the validity and feasibility of the proposed method. Experimental results have shown that our method is able to recover detailed high-frequency components and the resolution is effectively improved.

Fast sparse Bayesian learning-based seismic resolution enhancement

Seismic high resolution processing plays a crucial role in seismic interpretation and reservoir characterization. However, the low and high frequency of seismic signal are lost owing to the effects of earth filtering and diffraction. The missing of low-frequency increases the energy of wavelet side lobe, which not only enhances the interference with adjacent events, but also lead to side lobe artifacts. High frequency attenuation is more serious, which significantly reduces the seismic resolution. In addition, the current resolution enhancement methods are difficult to achieve desirable result in noise contaminated environment. A novel resolution enhancement method that extends high and low frequencies simultaneously and improves the signal-to-noise ratio (SNR) under the framework of sparse decomposition theory is proposed. Firstly, an over-complete dictionary is constructed using non-zero phase Ricker wavelet and then the effective seismic signal is extracted by sparse decomposition using the fast sparse Bayesian learning (FSBL) algorithm. Secondly, the effective frequency range of the seismic signal is determined through a series of atoms obtained by sparse decomposition. Then, a desired broadband spectrum can be identified depending on this range. By approximating the broadband spectrum of seismic data, the weak high-frequency information can be extended. Furthermore, a correction term is introduced in frequency domain to suppress the side lobes of atoms. The side lobes are well suppressed without distorting the main lobe, meanwhile, both the low and high frequencies are extended. Finally, the validity of this method is verified by synthetic and field data. The results show that this method has better performance than the ordinary deconvolution and spectral whitening methods in processing high resolution data.

Deep neural network-enabled resolution enhancement for the digital light field display based on holographic functional screen

The digital light-field display suffers from low resolution due to lack of planar pixels transformed into spatial information, which limits three-dimensional (3D) visual effect obviously. Herein, a pre-coding optimization of rendering the elemental image array (EIA) using a resolution-enhanced deep neural network (RE-DNN) is proposed, and it is used to enhance each reconstructed view perspective with high clarity besides using the holographic functional screen (HFS). The RE-DNN is used to cooperate with the HFS to form additional effective visual pixels/sub-pixels without increasement of physical planar resolution. The simulation and experimental presented 3D images with high clarity are demonstrated, verifying the feasibility and superiority of the proposed resolution enhancement method.

Simplified Self-Coherent FSO Transmission Boosted by Digital Resolution Enhancer

The joint application of free-space optics (FSO) transmission with coherent detection has recently appeared to enable unprecedented optical wireless capacity. Nevertheless, due to the associated cost and power consumption, simplified self-coherent FSO links are currently being explored to optimize the performance vs. complexity tradeoff. In particular, the utilization of low-complexity virtual carrier self-coherent transmission provides an appealing cost-effective alternative for high-capacity FSO links. However, this transmission paradigm is associated with quantization noise problems due to limited digital-to-analog converter (DAC) resolution. Following all the stated requirements, this work presents a simplified virtual carrier-assisted self-coherent FSO link supported by a low-resolution DAC and Nyquist rate phase reconstruction using the DC-Value algorithm. The utilization of such a low-resolution DAC is aided by a digital resolution enhancement (DRE) method. Employing this simplified self-coherent approach, we experimentally demonstrated 100 Gbps FSO transmission over an outdoor 42 m link using a DAC with resolution as low as 3 bits.

A novel infrared spectral preprocessing method based on self-deconvolution and differentiation in the frequency domain

Spectral preprocessing is a crucial step before establishing a multivariate calibration model for Fourier transform infrared (FTIR) spectroscopic analysis. However, a single preprocessing method has limited effects, such as insufficient noise reduction or limited resolution improvement. As two classical preprocessing methods, Fourier self-deconvolution (FSD) and Fourier filtering and differentiation (FFD) have their benefits and drawbacks. FSD has low noise but limited resolution, while FFD could improve resolution and eliminate baseline simultaneously, but is susceptive to noise. By combining FSD and FFD, we design a novelty resolution enhancement method, termed Fourier self-deconvolution differentiation (FSDD), which can significantly improve spectral resolution with good noise and baseline robustness. Partial least squares regression (PLS) is combined with five preprocessing methods, FSD, FFD, direct derivation (DD), Savitsky-Golay smoothing and derivation (SGSD), and FSDD, to analyze the albiflorin dataset and the γ-polyglutamic acid (γ-PGA) fermentation dataset. Compared with other preprocessing methods, FSDD obtained the optimum performance merits, including root mean squared error of prediction (RMSEP), determination coefficient (R2), and Mean Absolute Error (MAE).

A novel physics-based resolution improvement method for neutron-density log

In the later stages of oilfield development, the accurate identification of thin reservoirs become increasingly crucial for oil and gas exploration. However, conventional logs have limited vertical resolution, making it challenging to identify thin reservoirs. This results in potential hydrocarbon layers getting overlooked during exploration. To address this issue, a novel resolution enhancement method for density and neutron porosity logs based on physical constraint optimization is proposed. The proposed method involves three main steps: (1) developing an objective function that relates the true formation density and porosity to the measurement based on the physics of nuclear, (2) setting density neutron cross-constraints using the volumetric model of logging interpretation, and (3) establishing the objective function matrix equations using sliding window technology and solving the matrix equations using the constrained least square method to obtain high-resolution density and neutron porosity logs. The proposed method is evaluated using both synthetic and field logs obtained from compensated density and compensated neutron tools. The results demonstrate an effective improvement in neutron porosity and density log resolution to 15 cm, which is significantly enhanced compared to the typical vertical resolution of 2 ft. This study offers a new perspective for enhancing the resolution of logs and contributes to the exploration and development of thin oil and gas resources. And the proposed method could be used in future exploration and production projects to identify potential hydrocarbon layers that might have been overlooked by conventional logs.

Expansion-enhanced super-resolution radial fluctuations enable nanoscale molecular profiling of pathology specimens

Expansion microscopy physically enlarges biological specimens to achieve nanoscale resolution using diffraction-limited microscopy systems1. However, optimal performance is usually reached using laser-based systems (for example, confocal microscopy), restricting its broad applicability in clinical pathology, as most centres have access only to light-emitting diode (LED)-based widefield systems. As a possible alternative, a computational method for image resolution enhancement, namely, super-resolution radial fluctuations (SRRF)2,3, has recently been developed. However, this method has not been explored in pathology specimens to date, because on its own, it does not achieve sufficient resolution for routine clinical use. Here, we report expansion-enhanced super-resolution radial fluctuations (ExSRRF), a simple, robust, scalable and accessible workflow that provides a resolution of up to 25 nm using LED-based widefield microscopy. ExSRRF enables molecular profiling of subcellular structures from archival formalin-fixed paraffin-embedded tissues in complex clinical and experimental specimens, including ischaemic, degenerative, neoplastic, genetic and immune-mediated disorders. Furthermore, as examples of its potential application to experimental and clinical pathology, we show that ExSRRF can be used to identify and quantify classical features of endoplasmic reticulum stress in the murine ischaemic kidney and diagnostic ultrastructural features in human kidney biopsies.

Повышение пространственного разрешения цифровых голограмм с помощью одномерного субпиксельного сканирования

The article considers the issue of increasing the spatial resolution during the registration of digital holograms. In practice, discretization is carried out by measuring signal samples using sensors with a certain finite aperture. The resolution is determined by the size of the aperture area over which the averaging takes place. The resolution enhancement method is based on the sampling equation for signals obtained by the subpixel shift method using generalized functions. A subpixel shift is carried out using a spatial shift by a value less than the element of resolution. Apertures of various shapes are used, for example, elliptical, diamond-shaped, hexagonal, but rectangular apertures are most commonly used. The discretization equation involves the use of a two-dimensional aperture function. Below is a way to increase the resolution when using a one-dimensional function. This can be done based on the structure of the holographic signal. In this case, you can use a subpixel shift in only one direction. Digital holography is distinguished by the fact that photodetector matrices which have a spatial resolution much lower than photographic media used in traditional holography methods are used to register the signal,. Therefore, digital holography uses optical schemes with small angles between interfering beams. However, certain restrictions are imposed on the shape of the objects under study. If the objects have a shape that is significantly different from a flat one, it is necessary to use traditional schemes. The article presents mathematical modeling of the resolution enhancement method based on the recovery of signals from a real hologram obtained in the usual way. The increase in resolution is achieved using a one-dimensional subpixel shift. The use of a one-dimensional subpixel shift makes it possible to significantly simplify the optical scheme of the holographic setup.

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

High-resolution terahertz digital holography based on frequency-domain diagonal extension imaging

By leveraging frequency-domain diagonal extension (FDDE) imaging in terahertz (THz) in-line digital holography, the resolution of THz digital holography would be dramatically improved. The holography system in this work which utilizes a CO2-pumped 2.52 THz (118.83 μm) continuous-wave laser as the source and a pyroelectric-array camera with a pixel pitch of 100 μm as the detector could attain a resolution of 150 μm (~1.26 λ) near to the resolution limit of the system. FDDE imaging is a simple and effective method for resolution enhancement, where no new detector or other equipment is employed. Samples are imaged twice from different angles and then the high-frequency components of images are stitched to improve the resolution. Different from the traditional FDDE, weight-FDDE (w-FDDE) which can reduce the visibility distinction between different orientations of the synthetic picture by adding weight factors to produce a superior synthetic image is proposed in this work.

Resolution Enhancement Method of L(0,2) Ultrasonic Guided Wave Signal Based on Variational Mode Decomposition, Wavelet Transform and Improved Split Spectrum Processing

Pipeline systems are prone to defects due to the harsh service conditions, which may induce catastrophic failure if found not in time. Ultrasonic guided wave (UGW) testing provides a convenient option for pipeline detection, showing high-efficiency, non-contact, long-distance and large-scale capabilities. To address the problem that UGW signals suffer from poor signal resolution that is mainly related to the coherent noise caused by the dispersion, multi-mode and mode conversion, an advanced signal processing method called VWISSP, based on variational mode decomposition (VMD), wavelet transform (WT), and improved split spectrum processing (ISSP) was proposed, of which SSP was improved by replacing the Gaussian filter bank with cosine filters of constant frequency-to-bandwidth and frequency-to-filter spacing ratios. Compared with ISSP, VWISSP shows better higher accuracy and resolution processing effects to noisy multi-defect UGW signals, which is manifested through the improvement of both the signal-to-noise ratio gain and the defect-to-noise gain. Only feature signals (defects and pipe end) are retained, whereas noise signals are eliminated completely.

Adversarial Spectral Super-Resolution for Multispectral Imagery Using Spatial Spectral Feature Attention Module

Acquiring high-quality hyperspectral imagery with high spatial and spectral resolution plays an important role in remote sensing. Due to the limited capacity of sensors, providing high spatial and spectral resolution is still a challenging issue. Spectral super-resolution (SSR) increases the spectral dimensionality of multispectral images to achieve resolution enhancement. In this article, we propose a spectral resolution enhancement method based on the generative adversarial network framework without introducing additional spectral responses prior. In order to adaptively rescale informative features for capturing interdependencies in the spectral and spatial dimensions, a spatial spectral feature attention module is introduced. The proposed method jointly exploits spatio-spectral distribution in the hyperspectral manifold to increase spectral resolution while maintaining spatial content consistency. Experiments are conducted on both synthetic Landsat 8 and Sentinel-2 radiance data and real coregistered advanced land image and Hyperion (MS and HS) images, which indicates the superiority of the proposed method compared to other state-of-the-art methods.

Symmetrization of Peaks in Chiral Chromatography with an Area-Invariant Resolution Enhancement Method.

A majority of enantiomeric separations show some degree of peak asymmetry, which is detrimental to quantitative and semiquantitative chiral analysis. This paper presents a simple and rapid peak symmetrization algorithm for the correction or reduction of peak tailing or fronting in exponentially modified Gaussians. Raw chromatographic data can be symmetrized by adding a correct fraction of the first derivative to the chromatogram. The area remains invariant since the area under the first derivative is zero for a pure Gaussian and numerically close to zero for asymmetric peaks. A method of easily extracting the distortion parameter is provided, as well as insight into how pre-smoothing the data with the "perfect smoother" algorithm can suppress high frequencies effectively. The central difference method is also used to compute the first derivative, reducing root-mean-square noise by up to 28% compared to the standard forward difference method. A survey of 40 chiral separations is presented, demonstrating the range of asymmetry observed in chiral separations. Examples of symmetrization of the peaks from enantiomers in comparable and disproportionate concentrations are also provided. Artifacts of deconvolution are discussed, along with methods to mitigate such artifacts.

Generating High-Resolution CT Slices from Two Image Series Using Deep-Learning-Based Resolution Enhancement Methods.

Medical image super-resolution (SR) has mainly been developed for a single image in the literature. However, there is a growing demand for high-resolution, thin-slice medical images. We hypothesized that fusing the two planes of a computed tomography (CT) study and applying the SR model to the third plane could yield high-quality thin-slice SR images. From the same CT study, we collected axial planes of 1 mm and 5 mm in thickness and coronal planes of 5 mm in thickness. Four SR algorithms were then used for SR reconstruction. Quantitative measurements were performed for image quality testing. We also tested the effects of different regions of interest (ROIs). Based on quantitative comparisons, the image quality obtained when the SR models were applied to the sagittal plane was better than that when applying the models to the other planes. The results were statistically significant according to the Wilcoxon signed-rank test. The overall effect of the enhanced deep residual network (EDSR) model was superior to those of the other three resolution-enhancement methods. A maximal ROI containing minimal blank areas was the most appropriate for quantitative measurements. Fusing two series of thick-slice CT images and applying SR models to the third plane can yield high-resolution thin-slice CT images. EDSR provides superior SR performance across all ROI conditions.

Programmable Aperture Using a Digital Micromirror Device for In-Line Holographic Microscopy

We present the novel use of a digital micromirror device (DMD) as a digitally adjustable and programmable aperture for lens-free digital in-line holographic microscopy (LFDIHMY). The proposed micro-electro-mechanical system (MEMS) based DMD aperture can be used as a compact and inexpensive replacement for existing mechanical based aperture control methods required for many resolution enhancement methods. Simulation of the DMD aperture in an in-line holography setup is presented using a general DMD model from literature. A hardware prototype was successfully implemented and demonstrated. The resolution of both simulated and experimental holograms recorded with a conventional pinhole aperture and the proposed DMD aperture are compared using a 1951 USAF resolution test target. We show that a resolution of 2.46 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm { \mu {\textbf {m}}}$ </tex-math></inline-formula> , close to the sensor pixel limit, can be obtained with each aperture. This demonstrates that a DMD can be used as a programmable replacement for a pinhole aperture.

Elevation Resolution Enhancement Method Using Non-Ideal Linear Motion Error of Airborne Array TomoSAR

Airborne array tomographic synthetic aperture radar (TomoSAR) is a major breakthrough, which can obtain three-dimensional (3D) information of layover scenes in a single pass. As a high-resolution SAR, airborne array TomoSAR has considerable potential for 3D applications. However, the original TomoSAR elevation resolution is limited by the baseline and platform length. In this study, a novel method for enhancing the elevation resolution is proposed. First, the actual curve trajectory observation model of airborne array TomoSAR is established. Subsequently, multi-channel image data are substituted into the model to obtain the observation equation. Furthermore, the azimuth and elevation directions of the two-dimensional observation scene are modeled uniformly. The scene reconstruction is realized through the two-dimensional joint solution. Finally, the observation equation is sparsely solved according to the sparse distribution characteristics of the target to obtain the image. The performance of the proposed method is verified via simulation and real-data experiments. The experimental results indicate that, compared with the traditional elevation resolution enhancement method, the proposed method improves the elevation resolution by two times. The proposed method also provides a new thinking for high-resolution SAR 3D imaging.

Complex-Amplitude-Modulation Vectorial Excitation Beam for High-Resolution Observation of Deep Regions in Two-Photon Microscopy.

In two-photon microscopy, aberration correction is an essential technique for realizing high resolution in deep regions. A spatial light modulator (SLM) incorporated into an optical system for two-photon microscopy performs pre-compensation on the wavefront of the excitation beam, restoring the resolution close to the diffraction limit even in the deep region of a biological sample. If a spatial resolution smaller than the diffraction limit can be achieved along with aberration correction, the importance of two-photon microscopy for deep region observation will increase further. In this study, we realize higher resolution observations in the deep region by combining two resolution-enhancement methods and an aberration correction method. Therefore, a z-polarizer is added to the aberration-correction optical system, and the SLM modulates the amplitude and phase of the excitation beam; in other words, complex-amplitude modulation is performed. The lateral resolution is found to be approximately 20% higher than the diffraction limit obtained using a circularly polarized beam. Verification was conducted by simulation and experimentation using model samples and ex vivo biological samples. The proposed method has the potential to be effective for live imaging and photostimulation of the deep region of the sample, although it requires only minor changes to the conventional optical system that performs aberration correction.