Resolution Enhancement Research Articles

Improving PRISMA hyperspectral spatial resolution and geolocation by using Sentinel-2: development and test of an operational procedure in urban and rural areas

Hyperspectral (HS) satellites like PRISMA (PRecursore IperSpettrale della Missione Applicativa) offer remarkable capabilities, yet they are constrained by a relatively coarse spatial resolution, curbing their efficacy in those applications that require pinpoint accuracy. Here we propose a fusion process, aimed at the enhancement of PRISMA HS spatial resolution by using the spatial and spectral information of Sentinel-2 multispectral (MS) data (HS-MS fusion process), validated against four airborne HS flights simultaneous to satellite overpasses on different land use distributions. Adopting the PRISMA panchromatic (PAN) image, the proposed solution was also compared with the results of a HS-PAN pansharpening process. A two-steps operational workflow is proposed, based on two state-of-the-art and open-source algorithms. The first step consisted of the geocoding of PRISMA L2 products using Senintel-2 as reference and was accomplished with the phase-based algorithm implemented in AROSICS (Automated and Robust Open-Source Image Co-registration Software). The geometric displacement in L2 data was found to be between 80 m and 250 m, irregularly spatially distributed throughout the same scene and among scenes, and it was corrected by means of thousands of regularly spatially distributed tie points. A second-order polynomial transformation function was integrated in the algorithm. The second step consisted of employing the HySure (HS Super resolution) fusion algorithm to perform both the HS-MS fusion and the HS-PAN pansharpening, returning a PRISMA HS improved dataset with a spatial resolution of 10 m and 5 m, respectively. Four different per-band accuracy metrics were used to evaluate the accuracy of both products against airborne data. Overall, HS-MS data achieved increased accuracy in all validation metrics, i.e. + 28 % (root mean square error, RMSE), +23 % (spectral angle mapper, SAM), +7% (peak signal-to-noise ratio, PSNR) and + 11 % (universal image quality index, UIQI), with respect of HS-PAN data. These outcomes showed that using the spectral information of Sentinel-2 both spectral and spatial patterns were reconstructed more consistently in three different urban and rural scenarios, avoiding the presence of blur and at-edge artefacts as opposed to HS-PAN pansharpening, therefore suggesting an optimal strategy for satellite HS data resolution enhancement.

Resolution Enhancement by Variable Zoom Trajectory in X-Ray Computed Tomography

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection.

Simultaneous improvement of the sensitivity and resolution of CPT magnetometers based on phase delay and differential method.

We report a method to enhance the sensitivity of coherent population trapping (CPT) magnetometers using a combination of left-handed and right-handed circularly polarized light phase-delay detection and a differential detection scheme. The approach can achieve a four third-fold enhancement of the CPT dispersion signal slope and a three-fold reduction in noises. The proposed method experimentally exhibits a four third-fold magnetic field resolution enhancement in CPT open-loop measurements, and the differential method could achieve a sensitivity of 1 p T/H z at 10 Hz and a sensitivity of 0.4 p T/H z at 50-100 Hz in the CPT closed-loop measurement, which is a four-fold sensitivity enhancement compared to the single-transmitted CPT magnetometer.

Infrared spectrum resolution enhancement model via Gabor transform regularization for object detection

In this paper, we proposed a novel resolution enhancement model for the unknown instrument function and infrared spectral signal estimation. The Gabor transform can be utilized on the degraded spectrum to save the proposed resolution enhancement model from converging the mistake spectral signal. In the proposed model, the spatial random noises are regularized by the total variation regularization, as well as the spectral local smoothness is reduced by the Gabor coefficient distribution regularization. Then, the split Bregman iteration scheme is introduced to alternately optimize the instrument function and latent spectrum, which shown excellent convergence and stability. After those steps, the high-resolution spectral signals can be produced by our proposed model. Comparing with the existing resolution enhancement algorithms, the proposed model can produce the comparable quality even if the observed spectrum exists the low contrast.

SeisResoDiff: Seismic resolution enhancement based on a diffusion model

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

A novel signal resolution enhance method based on CBAM-ResNet for bearing intelligent fault diagnosis

Abstract In health condition monitoring of mechanical equipment, the signal is the key source of information. However, signal resolution is often degraded due to factors such as equipment vibration and electromagnetic interference. To address this issue, an Efficient Sub-pixel Convolutional Attention Residual Network (ESPCARN) built on the idea of signal resolution improvement is proposed in this paper. Firstly, the original low-resolution samples are input into a CBAM-ResNet to obtain more feature information of the channels and space within the residual connection and a multi-feature mapping with four channels was generated. Subsequently, the four-channel low-resolution features are aligned periodically through sub-pixel convolution layer, resulting in a set of high-resolution samples and the feature dimension of the data was increased to four times that of the original low-resolution data, thereby realizing the resolution enhancement. Finally, two experiments with different working conditions are established to evaluate the performance of the proposed fault diagnosis method, and the experimental results verified the efficacy of the ESPCARN framework.

An Underwater Image Enhancement Method Based on Diffusion Model Using Dual-Layer Attention Mechanism

Diffusion models have been increasingly utilized in various image-processing tasks, such as segmentation, denoising, and enhancement. These models also show exceptional performance in enhancing underwater images. However, conventional models for underwater image enhancement often face the challenge of simultaneously improving color restoration and super-resolution. This paper introduces a dual-layer attention mechanism that integrates spatial and channel attention to enhance color restoration, while preserving critical image features. Additionally, specific scale factors and interpolation methods are employed during the upsampling process to increase resolution. The proposed DL-UW method achieves significant enhancements in color, illumination, and resolution for low-quality underwater images, resulting in high PSNR, SSIM, and UIQM values. The model demonstrates a robust performance on different datasets, confirming its broad applicability and effectiveness.

A Waveform and Velocity Ambiguity Resolution Method for Corner Radar

Millimeter-wave radar is experiencing an increasing demand for higher resolution and elevation measurement, necessitating its evolution from 3D millimeter-wave radar systems to 4D millimeter-wave radar. Unlike front automotive radar, corner radar is particularly interested in close-range targets. This article proposes a composite waveform tailored for automotive corner radar, employing different waveform schemes for various distances. This allows millimeter-wave angle radar to achieve superior range resolution and velocity resolution in the close and medium ranges. However, enhancing velocity resolution inevitably results in a reduction in the maximum unambiguous velocity. Consequently, a velocity ambiguity resolution method based on target parameter matching for front and rear frames is proposed to address this issue. The feasibility of the composite waveform and method are verified through simulation. Finally, a radar designed with the ALPS PRO chip is employed to test the designed waveform and velocity ambiguity resolution method, yielding results that are largely consistent with the simulation outcomes. The findings indicate a notable enhancement in resolution for medium distances ranging from 60 to 100 m when employing the proposed waveform. Moreover, the velocity ambiguity resolution method effectively resolves velocity ambiguity, leading to enhanced accuracy in velocity measurements. This suggests that the composite waveform and algorithm are well suited for current automotive angle radar applications.

A study of the temperature-dependent stress yielding behavior of a gelatin-based hydrogel ink and its effects on the enhancement of the 3D printing resolution

Though existing studies had continuously improved the hydrogel printing resolution by facilitating ink solidification, it was literally challenging to exceed the size limit of the printing needle even if there was an ideal ink that could solidify instantaneously. One possible solution to this hurdle is to stretch the hydrogel ink with a high printing speed and try maintaining its deformation without strain recovery. In this study, a gelatin/alginate/hydroxyapatite(HA) ink was printed at multiple temperatures, and a much lower yielding strain occurred at −1 °C than 2–5 °C according to our rheological tests. At −1 °C, the hydrogel ink could be highly stretched to produce an ultrahigh resolution at 81 ± 13 μm while maintaining uniform and continuous. Furthermore, our biological tests found a high scaffold resolution greatly promoted cell adhesion, proliferation, osteogenic differentiation and biomineralization. We stepped further from existing studies, by coming up with a new concept of utilizing ink stress-yielding in improving printing resolution, which would broaden our understanding to the processing-structure-property relationship in hydrogel-based 3D printing.

Elevation Resolution Enhancement Oriented 3D Ultrasound Imaging.

Three-dimensional (3D) ultrasound imaging can be accomplished by reconstructing a sequence of two-dimensional (2D) ultrasound images. However, 2D ultrasound images usually suffer from low resolution in the elevation direction, thereby impacting the accuracy of 3D reconstructed results. The lateral resolution of 2D ultrasound is known to significantly exceed the elevation resolution. By combining scanning sequences acquired from orthogonal directions, the effects of poor elevation resolution can be mitigated through a composite reconstructing process. Moreover, capturing ultrasound images from multiple perspectives necessitates a precise probe positioning method with a wide angle of coverage. Optical tracking is popularly used for probe positioning for its high accuracy and environment-robustness. In this paper, a novel large-angle accurate optical positioning method is used for enhancing resolution in 3D ultrasound imaging through orthogonal-view scanning and composite reconstruction. Experiments on two phantoms proved that our method could significantly improve reconstruction accuracy in the elevation direction of the probe compared with single-angle parallel scanning. The results indicate that our method holds the potential to improve current 3D ultrasound imaging techniques.

Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models

Abstract. We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs).

Type-printable photodetector arrays for multichannel meta-infrared imaging

Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers considerable advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently typical multichannel infrared imaging systems consist of separating optical gratings or merging multi-cameras, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present printable graphene plasmonic photodetector arrays driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses with zero-bias operation by directly rescaling the ferroelectric superdomain instead of reconstructing the separated gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and offer potential solutions in efficient edge detection in human-brain-type machine vision.

Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model.

Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high-field and ultra-high-field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high-resolution MRI improve the quality of PETimages? This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high-field and ultra-high-fieldMRI. From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor-corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevindynamics. Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state-of-the-art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra-high-field MRI exhibit marked improvements in signal-to-noise characteristics when contrasted with those generated from high-field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer'sdisease. This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTAmodels.

Full-angle single-shot quantitative phase imaging based on Kramers-Kronig relations.

As a non-interference and non-iterative method, annular-illumination quantitative phase imaging based on Kramers-Kronig relations (AIKK) can realize phase measurement with full-angle resolution enhancement under multiple exposures. In order to completely record the object spectrum with a single shot, we proposed a colorful complementary illumination method in the recording process. The angle of this illumination mode is not symmetrical with each other, so the spectrum between the three channels can complement each other to avoid spectrum loss caused by spectrum conjugation. Meanwhile, the three spectral segments of full-angle information spectrum respectively carried by three wavelengths can be recorded. Additionally, the numerical filter is applied to correct the overlapped spectrum in the reconstruction process. Simulation and experimental results show that this method can achieve high spatiotemporal resolution quantitative phase measurement.

Machine learning-based prediction approach for ranging resolution enhancement of FMCW LiDAR system with LSTM networks

Ranging resolution is one of the most important factors of frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR) systems, which is determined by the effective sweep bandwidth and observation time of the ranging signal used for Fourier transform. Conventional methods for resolution enhancement either require expensive devices or complicated operation with human intervention, which is high-cost and inefficient. For the first time, a machine learning-based prediction approach is proposed for resolution improvement. A long short-term memory (LSTM) network with a special architecture, which is driven by a theoretical model of FMCW ranging signals, is established for signal prediction. A resampling-based preprocessing block is then designed to denoise the original ranging data and enhance its feature. Finally, multi-step prediction of the input time-series signal is performed using the well-trained LSTM network to increase effective data length so as to enhance the resolution. The effectiveness of the proposed method is verified through several experiments. The results show that the proposed method not only has the advantages of low-cost and high-efficiency, but also enables the FMCW LiDAR system to possess larger equivalent bandwidth, better ranging resolution, and higher signal-to-noise ratio (SNR), enlightening the development of machine learning in high-precision ranging systems.

Simulating multiple variability in spatially resolved transcriptomics with scCube

A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube (https://github.com/ZJUFanLab/scCube), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.

Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network. This deep learning protocol allows one to regain high-resolution signals and suppress undersampling artifacts, as well as furnish high-fidelity signal intensities along with the accelerated pure shift acquisition, benefitting from the introduction of the attention mechanism to highlight the spectral feature and information of interest. Extensive results of simulated and experimental NMR data demonstrate that this attention-assisted deep learning protocol enables the effective recovery of weak signals that are almost drown in the serious undersampling artifacts, and the distinction and recognition of close chemical shifts even though using merely 5.4% data, highlighting its huge potentials on fast pure shift NMR spectroscopy. As a result, this study affords a promising paradigm for the AI-assisted NMR protocols toward broader applications in chemistry, biology, materials, and life sciences, and among others.

High-resolution, large field-of-view label-free imaging via aberration-corrected, closed-form complex field reconstruction

Computational imaging methods empower modern microscopes to produce high-resolution, large field-of-view, aberration-free images. Fourier ptychographic microscopy can increase the space-bandwidth product of conventional microscopy, but its iterative reconstruction methods are prone to parameter selection and tend to fail under excessive aberrations. Spatial Kramers–Kronig methods can analytically reconstruct complex fields, but is limited by aberration or providing extended resolution enhancement. Here, we present APIC, a closed-form method that weds the strengths of both methods while using only NA-matching and darkfield measurements. We establish an analytical phase retrieval framework which demonstrates the feasibility of analytically reconstructing the complex field associated with darkfield measurements. APIC can retrieve complex aberrations of an imaging system with no additional hardware and avoids iterative algorithms, requiring no human-designed convergence metrics while always obtaining a closed-form complex field solution. We experimentally demonstrate that APIC gives correct reconstruction results where Fourier ptychographic microscopy fails when constrained to the same number of measurements. APIC achieves 2.8 times faster computation using image tile size of 256 (length-wise), is robust against aberrations compared to Fourier ptychographic microscopy, and capable of addressing aberrations whose maximal phase difference exceeds 3.8π when using a NA 0.25 objective in experiment.

A gravitational eye: a method for extracting maximum information from gravitational potentials

Gravity measurements have uses in a wide range of fields including geological mapping and mine-shaft inspection. The specific application under consideration sets limits on the survey and the amount of information that can be obtained. For example, in a conventional gravity survey at the Earth’s surface a gravimeter is translated on a two-dimensional planar grid taking measurements of the vertical component of gravity. If, however, the survey points cannot be chosen so freely, for example if the gravimeter is constrained to operate in a tunnel where only a one-dimensional line of data could be taken, less information will be obtained. To address this situation, we investigate an alternative approach, in the form of an instrument which rotates around a central point measuring the gravitational potential or its radial derivative on the boundary of a sphere. The ability to record additional components of gravity by rotating the gravimeter will give more information than obtained with a single measurement traditionally taken at each point on a survey, consequently reducing ambiguities in interpretation. We term a device which measures the potential, or its radial derivatives, around the surface of a sphere a gravitational eye. In this article we explore ideas of resolution and propose a thought experiment for comparing the performance of diverse types of gravitational eye. We also discuss radial analytic continuation towards sources of gravity and the resulting resolution enhancement, before finally discussing the possibility of using cold-atom gravimetry and gradiometry to construct a gravitational eye. If realised, the gravitational eye will offer revolutionary capability enabling the maximum information to be obtained about features in all directions around it.