Spectral Resolution Enhancement Research Articles

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

Enhanced Spectral Resolution and Two-Dimensional Correlation Spectroscopy (2D-COS).

The apparent enhancement of spectral resolution is one of the attractive features of two-dimensional correlation spectroscopy (2D-COS). Highly overlapped adjacent bands often encountered in one-dimensional spectra may be effectively differentiated and identified by spreading peaks along the second dimension. This differentiating feature or selectivity is especially prominent in asynchronous spectra, where even a slight difference in the variation patterns of overlapped bands in response to a given perturbation results in the generation of cross-peaks. While cross-peaks in asynchronous spectra can identify signals originating from different moieties or bands, they do not effectively specify which regions of spectra actually share the same molecular origin. Overreliance on asynchronous spectra alone risks the potential false negative assessment or lack of sufficient specificity, leading to the failure of classifying signals into a reasonable set of component groups. The combined use of synchronous and asynchronous spectra coupled with the scaling techniques, elimination of anti-correlated negative synchronous peaks, and a robust line shape narrowing method provides a means to achieve both selectivity and specificity for resolution-enhancement of 2D-COS.

Snapshot-Type Polarization Spectral Imaging Resolution Enhancement Based on Dual-Channel Complementary Microarray Coding Imaging Fusion

Snapshot-type polarization spectral imaging is a crucial dynamic high-speed target detection method in remote sensing in space because it has a high temporal resolution. However, the sparse representation of snapshot-type imaging in spectral or spatial dimension leads to low spectral or spatial resolution. To resolve the problem we propose double-channel complementary microarray coding snapshot-type polarization spectral imaging by utilizing the transmission and reflection complementary principle of a single microarray coding plate, the dual-channel multispectral and hyper-spectral image fused to enhancing the resolution of polarization spectral imaging. This study combined with the principle of intensity modulation used in full polarization measurements, and established the spectral modulation and reconstruction model of dual-channel complementary microarray coding. According to the principle of dual-channel complementary snapshot polarization spectral imaging proposed in this paper, the dual-channel multispectral and hyper-spectral image fusion model was setted up. This study improved the snapshot polarization spectral imaging spectral and spatial resolutions, increased the spatial resolution PSNR by 3 dB, and made the spectral resolution significantly higher. The findings of this study can assist in achieving high temporal resolution, high spectral resolution, high spatial resolution, and fully polarized snapshot-type polarization spectral imaging.

Parameter Estimation in Spectral Resolution Enhancement Based on Forward-Backward Linear Prediction Total Least Square Method.

In a Fourier transform infrared (IR) spectrometer, the Michelson interference signal extrapolation method based on linear prediction is often used to improve spectral resolution. In this method, an autoregressive (AR) model is established for the Michelson interference signal in the spectrometer. Once the AR model parameters are determined, the AR process is predictable. The interference signal can be used to figure out the AR model's parameters. Based on this, the AR model can be used to extrapolate the interference signal to improve the spectral resolution. In this paper, the forward-backward linear prediction total least squares (FB-TLS) method is proposed to estimate the parameters of the AR model. The parameters that are estimated are used to improve the IR spectral resolution. By simulating different order and signal-to-noise ratio situations, the effects of the Burg, the least square, and the FB-TLS parameter estimation methods on spectral resolution enhancement are studied. The simulation results demonstrate that the FB-TLS parameter estimation method can effectively suppress noise and avoid spurious peaks. The experimental results demonstrate that the FB-TLS parameter estimation method is effective for spectral resolution enhancement technology based on linear prediction. When the FB-TLS method is used to enhance NH3 IR spectral resolution from 2 cm-1 to 1 cm-1, the spectral prediction error in the NH3 characteristic band is only 0.21% compared with the measured NH3 spectrum, whose spectral resolution is 1 cm-1.

Fourier spectral resolution enhancement algorithm based on linear prediction

Fourier transform infrared spectroscopy (FTIR) has been widely used for real-time monitoring of ambient gases, but for quantitative analysis of certain gases, higher spectral resolution is required to quantify more accurately. In this paper, a Fourier spectral resolution enhancement algorithm based on linear prediction is proposed. Firstly, the linear prediction model for interference signals is studied, utilizing the forward and backward linear prediction total least squares method for parameter estimation, minimum error criterion for determining the autoregressive (AR) model order, and sliding window technique for multi-step prediction. Then, the impact of the signal-to-noise ratio, enhancement factor, and initial resolution of the FTIR spectrum on the accuracy of the algorithm was studied through simulations. Finally, the algorithm is applied to spectral resolution enhancement and quantitative analysis of ammonia (NH3). The experimental results show that when the spectral resolution of NH3 is enhanced from 4 cm−1 to 2 cm−1 and 1 cm−1, the spectral error of the characteristic band is 0.16% and 0.26%, respectively. Additionally, the accuracy of concentration inversion is improved by 2.13% and 4.06%, respectively. The relative error of concentration inversion is 0.69% and 2.15%, respectively, when compared to the measured spectrum at corresponding resolutions. The algorithm proves effective in enhancing spectral resolution and improving the accuracy of quantitative analysis.

Spectral resolution enhancement for impulsive stimulated Brillouin spectroscopy by expanding pump beam geometry.

Brillouin microscopy has recently emerged as a powerful tool for mechanical property measurements in biomedical sensing and imaging applications. Impulsive stimulated Brillouin scattering (ISBS) microscopy has been proposed for faster and more accurate measurements, which do not rely on stable narrow-band lasers and thermally-drifting etalon-based spectrometers. However, the spectral resolution of ISBS-based signal has not been significantly explored. In this report, the ISBS spectral profile has been investigated as a function of the pump beam's spatial geometry, and novel methodologies have been developed for accurate spectral assessment. The ISBS linewidth was found to consistently decrease with increasing pump-beam diameter. These findings provide the means for improved spectral resolution measurements and pave the way to broader applications of ISBS microscopy.

Spectral resolution enhancement of plasmonic color mixing sensor with extremely compact footprint and wide range tuning

Spectral resolution enhancement of plasmonic color mixing sensor with extremely compact footprint and wide range tuning

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.

Ultrafast Magic Angle Spinning Solid-State NMR Spectroscopy: Advances in Methodology and Applications.

Solid-state NMR spectroscopy is one of the most commonly used techniques to study the atomic-resolution structure and dynamics of various chemical, biological, material, and pharmaceutical systems spanning multiple forms, including crystalline, liquid crystalline, fibrous, and amorphous states. Despite the unique advantages of solid-state NMR spectroscopy, its poor spectral resolution and sensitivity have severely limited the scope of this technique. Fortunately, the recent developments in probe technology that mechanically rotate the sample fast (100 kHz and above) to obtain "solution-like" NMR spectra of solids with higher resolution and sensitivity have opened numerous avenues for the development of novel NMR techniques and their applications to study a plethora of solids including globular and membrane-associated proteins, self-assembled protein aggregates such as amyloid fibers, RNA, viral assemblies, polymorphic pharmaceuticals, metal-organic framework, bone materials, and inorganic materials. While the ultrafast-MAS continues to be developed, the minute sample quantity and radio frequency requirements, shorter recycle delays enabling fast data acquisition, the feasibility of employing proton detection, enhancement in proton spectral resolution and polarization transfer efficiency, and high sensitivity per unit sample are some of the remarkable benefits of the ultrafast-MAS technology as demonstrated by the reported studies in the literature. Although the very low sample volume and very high RF power could be limitations for some of the systems, the advantages have spurred solid-state NMR investigation into increasingly complex biological and material systems. As ultrafast-MAS NMR techniques are increasingly used in multidisciplinary research areas, further development of instrumentation, probes, and advanced methods are pursued in parallel to overcome the limitations and challenges for widespread applications. This review article is focused on providing timely comprehensive coverage of the major developments on instrumentation, theory, techniques, applications, limitations, and future scope of ultrafast-MAS technology.

Hybrid microlens array/grating fabrication based on microfluidic system and its application in the enhancement of spectral resolution

Nowadays, micro-spectrometer has become an important research direction in the field of spectral analysis. The hybrid microlens array (MLA)/gratings element performs both focusing and diffraction duties, and its short focal length allows the optical system to be smaller. In this work, a unique and cost-effective fabrication method for hybrid MLA/grating based on the microfluidic system is described. By this method, the grating line-density, microlens curvature and aperture size of the hybrid MLA/grating can be effectively controlled, so as to bring different diffraction and focusing effects. Finite element simulation is used to describe the principle of the method. The relationship between bulge height and membrane thickness, pressure, hole size was investigated using a profile observation system. Wave optics simulation and diffraction measurements were used to demonstrate the optical properties of this hybrid element. It can be found that the diffracted light is converged at focus points after MLA/grating, and the full width at half maximum (FWHM) is significantly narrowed. The diffraction experimental results are consistent with the simulation results. The MLA/grating structure reduces FWHM of first-order diffracted light by a factor of 7 compared to same type of the planar grating. Therefore, the MLA/grating element can effectively improve the spectral resolution. Due to its small focal length, the MLA/grating has the potential to be used in micro-spectrometers.

A 3-Stage Spectral-Spatial Method for Hyperspectral Image Classification

Hyperspectral images often have hundreds of spectral bands of different wavelengths captured by aircraft or satellites that record land coverage. Identifying detailed classes of pixels becomes feasible due to the enhancement in spectral and spatial resolution of hyperspectral images. In this work, we propose a novel framework that utilizes both spatial and spectral information for classifying pixels in hyperspectral images. The method consists of three stages. In the first stage, the pre-processing stage, the Nested Sliding Window algorithm is used to reconstruct the original data by enhancing the consistency of neighboring pixels and then Principal Component Analysis is used to reduce the dimension of data. In the second stage, Support Vector Machines are trained to estimate the pixel-wise probability map of each class using the spectral information from the images. Finally, a smoothed total variation model is applied to ensure spatial connectivity in the classification map by smoothing the class probability tensor. We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral datasets with 10 to 50 training labels for each class. The results show that our method gives the overall best performance in accuracy even with a very small set of labeled pixels. Especially, the gain in accuracy with respect to other state-of-the-art algorithms increases when the number of labeled pixels decreases, and, therefore, our method is more advantageous to be applied to problems with a small training set. Hence, it is of great practical significance since expert annotations are often expensive and difficult to collect.

Heterogeneity-Aware Recurrent Neural Network for Hyperspectral and Multispectral Image Fusion

Due to the hardware limitations of remote imaging sensors, it is challenging to acquire images with high resolution in both the spatial and spectral domains. An effective and economical way to obtain high-resolution hyperspectral images (HR HSI) is to fuse low-resolution hyperspectral images (LR HSI) and high-resolution multispectral images (HR MSI). However, most existing deep learning based fusion methods employ the same network for all of the spectra without exploring their complex regional heterogeneity of hyperspectral characteristics. Taking various intrinsic spatial and spectral characteristics across different pixels into consideration, this paper proposes a mixture of recurrent neural networks (RNNs) under the variational probabilistic framework for spatial and spectral resolution enhancement. More specifically, we firstly cluster spectral characteristics into different groups, and employ different RNN experts for various spectra generation under the guidance of clustering. Moreover, a cluster-specific learnable Gaussian prior is proposed to provide a prior knowledge of heterogeneity. Further, an online variational inference scheme is derived for end-to-end optimization. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed model on both synthetic and real datasets, compared with state-of-the-art unsupervised fusion methods.

Biomolecular changes in gills of Gambusia affinis studied using two dimensional correlation infrared spectroscopy coupled with chemometric analysis

The pesticide use in agriculture production at reduced cost results in increased usage. Excessive use of this is en-routed to the aquatic environment causing serious threats. Fishes are used as a bioindicator of toxicants due to pollution in the aquatic environment. These are suitable biomarkers for assessment in the ecosystem. Hence the study of pesticides toxicity in fish provides useful information about the aquatic biota. 2DCOS infrared spectral analysis gives fine spectral resolutions of overlapping bands. The enhancement in spectral resolution and overlapping bands in second dimensions is an advantage in interpreting complex spectra. The synchronous 2DCOS spectra show amide of proteins as a predominant occurring followed by phospholipids esters of fatty acids at 1771 cm−1. The asynchronous +ve and -ve cross peaks +(1651, 1691) and – (1623, 1651) results in an overlapped contribution of β sheet (1691 cm−1) with a decrease in α helix (1651 cm−1) of protein secondary structure. The principal component analysis (PCA) shows samples are well discriminated with a high loading value for 0.51 ppb (T3) treatment. The hierarchical analysis shows clustering of samples identical in nature. The Receiver Operation Characteristic analysis (ROC) validates experimental outcome as measured from the area under the curve.

Hybrid Mla/Grating Fabrication Based on Microfluidic System and its Application in the Enhancement of Spectral Resolution

Hybrid Mla/Grating Fabrication Based on Microfluidic System and its Application in the Enhancement of Spectral Resolution

Micro two-dimensional slit-array for super resolution beyond pixel Nyquist limits in grating spectrometers.

This paper presents a new micro 2-D slit-array device for spectral resolution enhancement in grating spectrometers. The 2-D slit-array is encoded in Hadamard matrix and the device is fabricated based on the micro-electromechanical system (MEMS) technology. By just using this 2-D slit-array to replace the single slit in the conventional grating spectrometer, real-time super spectral resolution detection beyond the pixel Nyquist limit, which is determined by the size of the detector pixel, can be realized. Furthermore, no other configuration of the spectrometer is changed, no movable parts are used, and the spectral range and instrument size remain almost unchanged while the resolution is improved. A series of experimental verifications for the feasibility of this design are included in this work.

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR.

Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.

Image Spectral Resolution Enhancement for Mapping Native Plant Species in a Typical Area of the Three-River Headwaters Region, China

Large-scale multispectral remote sensing data are often unavailable for some practical applications. Spectral resolution enhancement for large-scale multispectral remote sensing images by incorporating small-scale hyperspectral remote sensing images is an alternative way to generate remote sensing images with both large spatial range and high spectral resolution. This paper proposes an improved spectral resolution enhancement method (ISREM) using spectral matrix and weighting the spectral angle of the transformation matrix. ISREM is tested in a typical area of the Three-River Headwaters region (TRHR) to produce a synthetic hyperspectral image (HSI). Two existing spectral resolution enhancement methods, the color resolution improvement software package (CRISP) and spectral resolution enhancement method (SREM), are adopted to compare with ISREM. To further test the practicality of the synthetic HSIs generated by the ISREM, CRISP and SREM, they are used to estimate the coverage of native plant species (NPS) using support vector machines (SVM) and random forest (RF) regressions. The experimental results are as follows. (1) For the Pearson correlation coefficient between the synthetic HSI and original image, ISREM yielded the largest value of 0.9582, followed by CRISP and SREM with values of 0.9480 and 0.9514. For spectral similarity, the HSI generated by the ISREM was the closest to the original reference HSI in the spectral curve. It also showed the best cumulative performance with the use of the three quality evaluation indexes. (2) The identification accuracies of native plant species were 93.51%, 90.91%, 89.61% and 89.61% using generated HSIs and original multispectral image (MSI) within a threshold of 20%, respectively. Compared with original MSI, the synthetic HSI showed better ability to identify NPS in the study area, which further illustrated the effectiveness of the ISREM. (3) The ISREM can reduce the strict requirement of pure pixels and maintain the quality of synthetic HSI by spectral angle weighting. Hence, the proposed ISREM outperforms the existing CRISP and SREM methods in image spectral resolution enhancement of multispectral remote sensing images.

Merging Gradient-Based Methods to Improve Benchtop NMR Spectroscopy: A New Tool for Flow Reaction Optimization.

Emerging low cost, compact NMR spectrometers that can be connected in-line to a flow reactor are suited to study reaction mixtures. The main limitation of such spectrometers arises from their lower magnetic field inducing a reduced sensitivity and a weaker spectral resolution. For enhancing the spectral resolution, the merging of Pure-Shift methods recognized for line narrowing with solvent elimination schemes was implemented in the context of mixtures containing protonated solvents. One more step was achieved to further enhance the resolution power on compact systems, thanks to multiple elimination schemes prior to Pure-Shift pulse sequence elements. For the first time, we were able to remove up to 6 protonated solvent signals simultaneously by dividing their intensity by 500 to 1700 with a concomitant spectral resolution enhancement for signals of interest from 9 to 12 as compared to the standard 1D 1 H. Then, the potential of this new approach was shown on the flow synthesis of a complex benzoxanthenone structure.

Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

High-resolution proton-detected MAS experiments on self-assembled diphenylalanine nanotubes enabled by fast MAS and high magnetic field

The advent of ultrahigh magnetic field and fast magic-angle-spinning (MAS) probe technology has led to dramatically enhanced spectral resolution and sensitivity in solid-state NMR spectroscopy. In particular, proton-based multidimensional solid-state NMR techniques have become feasible to investigate the structure and dynamics at atomic resolution, due to the increased chemical shift span and spectral resolution. Herein, the benefits of faster MAS and higher magnetic field are demonstrated on a self-assembled diphenylalanine (Phe-Phe) nanomaterial. Proton-detected 2D 1H/1H single-quantum/single-quantum (SQ/SQ) correlation, double-quantum/single-quantum (DQ/SQ) correlation, and 1H chemical shift anisotropy/chemical shift (CSA/CS) correlation spectra obtained at two different spinning speeds (60 and 100 kHz) and two different magnetic fields (600 and 900 MHz) are reported. The dramatic enhancement of proton spectral resolution achieved with the use of a 900 MHz magnetic field and 100 kHz MAS is remarkable and enabled the measurement of proton CSA tensors, which will be useful to better understand the self-assembled structures of Phe-Phe nanotubes. We also show through numerical simulations that the unaveraged proton-proton dipolar couplings can result in broadening of CSA lines, leading to inaccurate determination of CSA tensors of protons. Thus, our results clearly show the insufficiency of a 600 MHz magnetic field to resolve 1H spectra lines and the inability of a moderate spinning speed of 60 kHz to completely suppress 1H–1H dipolar couplings, which further justify the pursuit of ultrahigh magnetic field beyond 1 GHz and ultrafast MAS beyond 100 kHz.