Super-resolution Capability Research Articles

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system

Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.

Spectral domain strategies for hyperspectral super-resolution: Transfer learning and channel enhance network

As the network structures continue to innovate and evolve, significant achievements have been achieved in hyperspectral image super-resolution tasks. However, how to further explore the spectral domain potential from prior knowledge and channel-enhanced structures to achieve better performance has inspired the following two works: Firstly, to systematically compare prior knowledge of spectral with spatial domain for HSI-SR tasks, four transfer learning strategies are proposed. The superior performance of the Relevant Channel/Random Space (RCRS) strategy reveals the importance of spectral feature reconstruction in HSI-SR tasks. Meanwhile, an interesting phenomenon has been observed that even without training on real datasets, the model can already exhibit a core or even decent super-resolution capability based solely on prior knowledge of above four strategies. Secondly, a dual-branch channel network with complementary channel feature extraction (CCFE) and adjacent channel feature extraction (ACFE) module is designed for spectral feature enhancement, which demonstrate superior performance compared to state-of-the-art methods on six datasets. To conclude, the effectiveness of RCRS strategy with pseudo prior channel knowledge on seven dual-input and eight single-input networks, as well as superiority of the proposed channel-enhanced network indicate the importance of spectral properties for HSI-SR tasks.

High-resolution ultrasound imaging based on spatio-temporal MUSIC

Abstract Time-reversal MUltiple SIgnal Classification (TR-MUSIC) is known for its super-resolution capability in locating point scatterers. However, a prerequisite for the application of the TR-MUSIC method is a thorough understanding of the properties of the medium and its background Green’s function. In general, it is difficult to obtain an accurate model of the Green’s function in advance for non-uniform or complex media, necessitating the use of approximate Green’s functions. When TR-MUSIC relies on these approximations, the impact on lateral resolution may be minimal, but the axial resolution is significantly reduced. In addition, we have proposed the Range-MUSIC (R-MUSIC) method, which exploits the linear relationship between the rotation of the echo phases and the positions of the scatterers. This approach uses subbands of different frequencies, differentiating multiple point scatterers by the speed of phase rotation associated with each frequency. By analyzing the profile in the time domain of echoes within the noise subspace, R-MUSIC achieves super-resolution in the axial direction without improving lateral resolution. Therefore, our study combines R-MUSIC with TR-MUSIC to improve axial resolution while maintaining lateral resolution. Through simulation and experimental evidence, we have shown that this combined approach yields better imaging performance than either method used independently.

Enhancing subsurface multiphase flow simulation with Fourier neural operator

Accurate numerical modeling of multiphase flow in subsurface oil and gas reservoirs is critical for optimizing hydrocarbon recovery. However, traditional physics-based algorithms face substantial computational hurdles due to the need for fine grid resolution and the inherent geological heterogeneity. To overcome these challenges, data-driven surrogate models solving the flow governing partial differential equations (PDEs) offer a promising alternative to enhance the efficiency of hydrocarbon production operations. In this study, we employ the Fourier Neural Operator (FNO) to extract spectral information from the reservoir properties, thereby facilitating the solution of coupled porous flow PDEs. Our focus is on two-phase flow dynamics, specifically exploring how water injection enhances reservoir pressure and displaces oil. This scenario involves solving a set of nonlinearly coupled PDEs with highly heterogeneous coefficients. Numerical results demonstrate that the developed FNO accurately predicts the reservoir pressure distributions. We further observe that the FNO's zero-shot super-resolution capability is sensitive to abrupt local changes in the reservoir pressure near injection and production wells. To enhance its accuracy, we propose a multi-fidelity FNO model that exhibits better adaptability across various grid configurations. After moderate training on graphics processing units (GPUs), the FNO achieves a speedup of three orders of magnitude compared to traditional numerical PDE solvers. Our experiments confirm the FNO's potential to replace repetitive physics-based simulations, significantly advancing computational efficiency in the uncertainty quantification of reservoir performance.

Anisotropic regularization for sparsely sampled and noise-robust Fourier ptychography.

Fourier ptychography (FP) is a powerful computational imaging technique that provides super-resolution and quantitative phase imaging capabilities by scanning samples in Fourier space with angle-varying illuminations. However, the image reconstruction in FP is inherently ill-posed, particularly when the measurements are noisy and have insufficient data redundancy in the Fourier space. To improve FP reconstruction in high-throughput imaging scenarios, we propose a regularized FP reconstruction algorithm utilizing anisotropic total variation (TV) and Tikhonov regularizations for the object and pupil functions, respectively. To solve this regularized FP problem, we formulate a reconstruction algorithm using the alternating direction method of multipliers and show that our approach successfully recovers high-quality images with sparsely sampled and/or noisy measurements. The results are quantitatively and qualitatively compared against various FP reconstruction algorithms to analyze the effect of regularization under harsh imaging conditions. In particular, we demonstrate the effectiveness of our method on the real experimental FP microscopy images, where the TV regularizer effectively suppresses the measurement noise while maintaining the edge information in the biological specimen and helps retrieve the correct amplitude and phase images even under insufficient sampling.

Photoswitching Reagent for Super-Resolution Fluorescence Microscopy.

Single-molecule localization microscopy (SMLM) has revolutionized optical microscopy by exceeding the diffraction limit and revealing previously unattainable nanoscale details of cellular structures and molecular dynamics. This super-resolution imaging capability relies on fluorophore photoswitching, which is crucial for optimizing the imaging conditions and accurately determining the fluorophore positions. To understand the general on and off photoswitching mechanisms of single dye molecules, various photoswitching reagents were evaluated. Systematic measurement of the single-molecule-level fluorescence on and off rates (kon and koff) in the presence of various photoswitching reagents and theoretical calculation of the structure of the photoswitching reagent-fluorophore pair indicated that the switch-off mechanism is mainly determined by the nucleophilicity of the photoswitching reagent, and the switch-on mechanism is a two-photon-induced dissociation process, which is related to the power of the illuminating laser and bond dissociation energy of this pair. This study contributes to a broader understanding of the molecular photoswitching mechanism in SMLM imaging and provides a basis for designing improved photoswitching reagents with potential applications extending to materials science and chemistry.

Acoustofluidic scanning fluorescence nanoscopy with a large field of view

Large-field nanoscale fluorescence imaging is invaluable for many applications, such as imaging subcellular structures, visualizing protein interactions, and high-resolution tissue imaging. Unfortunately, conventional fluorescence microscopy requires a trade-off between resolution and field of view due to the nature of the optics used to form the image. To overcome this barrier, we developed an acoustofluidic scanning fluorescence nanoscope that simultaneously achieves superior resolution, a large field of view, and strong fluorescent signals. The acoustofluidic scanning fluorescence nanoscope utilizes the superresolution capabilities of microspheres that are controlled by a programmable acoustofluidic device for rapid fluorescence enhancement and imaging. The acoustofluidic scanning fluorescence nanoscope resolves structures that cannot be resolved with conventional fluorescence microscopes with the same objective lens and enhances the fluorescent signal by a factor of ~5 without altering the field of view of the image. The improved resolution realized with enhanced fluorescent signals and the large field of view achieved via acoustofluidic scanning fluorescence nanoscopy provides a powerful tool for versatile nanoscale fluorescence imaging for researchers in the fields of medicine, biology, biophysics, and biomedical engineering.

Deep learning empowers photothermal microscopy with super-resolution capabilities.

In the past two decades, photothermal microscopy (PTM) has achieved sensitivity at the level of a single particle or molecule and has found applications in the fields of material science and biology. PTM is a far-field imaging method; its resolution is restricted by the diffraction limits. In our previous work, the modulated difference PTM (MDPTM) was proposed to improve the lateral resolution, but its resolution improvement was seriously constrained by information loss and artifacts. In this Letter, a deep learning approach of the cycle generative adversarial network (Cycle GAN) is employed for further improving the resolution of PTM, called DMDPTM. The point spread functions (PSFs) of both PTM and MDPTM are optimized and act as the second generator of Cycle GAN. Besides, the relationship between the sample's volume and the photothermal signal is utilized during dataset construction. The images of both PTM and MDPTM are utilized as the inputs of the Cycle GAN to incorporate more information. In the simulation, DMDPTM quantitatively distinguishes a distance of 60 nm between two nanoparticles (each with a diameter of 60 nm), demonstrating a 4.4-fold resolution enhancement over the conventional PTM. Experimentally, the super-resolution capability of DMDPTM is verified by restored images of Au nanoparticles, achieving the resolution of 114 nm. Finally, the DMDPTM is successfully employed for the imaging of carbon nanotubes. Therefore, the DMDPTM will serve as a powerful tool to improve the lateral resolution of PTM.

Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging

Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear SIM. With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various vulnerable bioprocesses, revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

High-resolution display screen as programmable illumination for Fourier ptychography

We introduce a new programmable illumination strategy for Fourier ptychographic microscopy (FPM), utilizing a high-resolution organic light-emitting diode (OLED) display screen at the vicinity of the sample to achieve super-resolution and quantitative phase imaging of biological samples. High-density pixels in the OLED screen allow for the angle-scanning illumination required for FP by placing the sample slide directly on the screen, where multiple images of the sample are captured while scanning the pixels on the screen. We discuss the design of the OLED-FP illumination and the corresponding image reconstruction strategies and experimentally validate the super-resolution and phase imaging capabilities of FP using a smartphone screen as illumination. Further, we extend our method to patterned illumination strategies, which multiplexes multiple illumination pixels to achieve full-field FP imaging with a reduced number of measurements, where we identify an optimal illumination pattern and density to maximize the imaging speed without sacrificing the image quality. Our approach offers a simple and compact programmable illuminator that can effectively transform any microscope into a compact FPM with resolution improvement and phase imaging capabilities.

LPSRGAN: Generative adversarial networks for super-resolution of license plate image

This paper proposes a super-resolution algorithm for reconstructing license plate images based on generative adversarial networks (GAN) for improving the recognition rate of low-resolution license plate images. To this end, this paper first designs a new image degradation model, called the n-stage random combination degradation model (n-RCD), which extends the traditional degradation model horizontally and vertically to better simulate the features of low-resolution license plate images in natural scenes. Subsequently, to address the problem that existing GAN models cannot adapt to the complex degradation space in natural scenes, this paper proposes a new network structure called LPSRGAN, which optimizes the SRGAN model from the generator and discriminator to improve its super-resolution capability. In addition, this paper also proposes a perceptual optical character recognition (OCR) loss for license plate images, which defines the connectionist temporal classification (CTC) loss of the output of the OCR network with character label values to better preserve the character details and features of the license plate images. Finally, the experiments reported in this paper show that the proposed algorithm improves the recognition rate of the original low-resolution license plate image by 12.48%, and the recognition rate of the reconstructed image reaches 93.90%. Based on this, this paper also tests the algorithm on actual captured data, and the results show that it performs well in natural scenes, demonstrating the algorithm has broad application prospects.

Correlative super-resolution bright-field and fluorescence imaging by microsphere assisted microscopy.

The resolution of fluorescence imaging has been significantly enhanced with the development of super-resolution imaging techniques, surpassing the diffraction limit and reaching sub-diffraction scales of tens of nanometers. However, the resolution of the bright-field images of cells is restricted by the diffraction limit, leading to a significant gap between the resolutions of fluorescence and bright-field imaging, which hinders the research of the precise distribution of intracellular nanostructures. A microsphere superlens offers a promising solution by providing label-free super-resolution imaging capabilities compatible with fluorescence super-resolution imaging. In this study, we used microsphere superlenses to simultaneously enhance the resolution of bright-field and fluorescence imaging, achieving correlated super-resolution bright-field and fluorescence imaging. Compared to conventional bright-field images, we improved the imaging resolution from λ/1.3 to λ/4.2. A correlative super-resolution of mouse skeletal muscle cells was achieved, enabling the clear observation of the precise distribution of nanoparticles in mouse skeletal muscle cells. Furthermore, microsphere superlenses inherit the advantages of optical imaging, which is expected to enable the capturing of ultrafast biological activity within living cells with extremely high temporal resolutions.

A mathematical theory of super-resolution and two-point resolution

Abstract This paper focuses on the fundamental aspects of super-resolution, particularly addressing the stability of super-resolution and the estimation of two-point resolution. Our first major contribution is the introduction of two location-amplitude identities that characterize the relationships between locations and amplitudes of true and recovered sources in the one-dimensional super-resolution problem. These identities facilitate direct derivations of the super-resolution capabilities for recovering the number, location, and amplitude of sources, significantly advancing existing estimations to levels of practical relevance. As a natural extension, we establish the stability of a specific $l_{0}$ minimization algorithm in the super-resolution problem. The second crucial contribution of this paper is the theoretical proof of a two-point resolution limit in multi-dimensional spaces. The resolution limit is expressed as $$\begin{align*}\mathscr R = \frac{4\arcsin \left(\left(\frac{\sigma}{m_{\min}}\right)^{\frac{1}{2}} \right)}{\Omega} \end{align*}$$ for ${\frac {\sigma }{m_{\min }}}{\leqslant }{\frac {1}{2}}$ , where ${\frac {\sigma }{m_{\min }}}$ represents the inverse of the signal-to-noise ratio ( ${\mathrm {SNR}}$ ) and $\Omega $ is the cutoff frequency. It also demonstrates that for resolving two point sources, the resolution can exceed the Rayleigh limit ${\frac {\pi }{\Omega }}$ when the signal-to-noise ratio (SNR) exceeds $2$ . Moreover, we find a tractable algorithm that achieves the resolution ${\mathscr {R}}$ when distinguishing two sources.

Roadmap on Label-Free Super-Resolution Imaging.

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.

Revealing the predictive power of neural operators for strain evolution in digital composites

The demand for high-performance materials, along with advanced synthesis technologies such as additive manufacturing and 3D printing, has spurred the development of hierarchical composites with superior properties. However, computational modelling of such composites using physics-based solvers, while enabling the discovery of optimal microstructures, have prohibitively high computational cost hindering their practical application. To this extent, we show that Neural Operators (NOs) can be used to learn and predict the strain evolution in 2D digital composites. Specifically, we consider three architectures, namely, Fourier NO (FNO), Wavelet NO (WNO), and Multi-wavelet NO (MWT). We demonstrate that by providing a few initial strain frames as input, NOs can accurately predict multiple future time steps in an extremely data-efficient fashion, especially WNO. Further, once trained, NOs forecast the strain trajectories for completely unseen boundary conditions. Among NOs, only FNO offers super-resolution capabilities for estimating strains at multiple length scales, which can provide higher material and pixel-wise resolution. We also show that NOs can generalize to arbitrary geometries with finer domain resolution without the need for additional training. Based on all the results presented, we note that the FNO exhibits the best performance among the NOs, while also giving minimum inference time that is almost three orders magnitude lower than the conventional finite element solutions. Thus, FNOs can be used as a surrogate for accelerated simulation of the strain evolution in complex microstructures toward designing the next composite materials.

Label-free super-resolution microscopy based on non-diffraction superoscillation beam (NDSB) illumination

Due to the diffraction properties of light waves, optical microscopy suffers from a fundamentally limited resolution of ∼λ/2, where λ relates to the illumination wavelength. Though near-field methods capturing the evanescent waves can reach a resolution of several nanometers, the extremely short working distance has restrained their applications. Typical far-field super-resolution microscopies using fluorescence to monitor the labelled samples also have their limitations in applications because of their phototoxicity and complex preprocessing. We hereby proposed a label-free super-resolution microscopy based on non-diffraction superoscillation beam (NDSB) illumination. We developed a transmission-mode confocal optical microscopy and experimentally demonstrated an imaging resolution of 0.285 λ at the illumination wavelength of 632.8 nm. As NDSB owns the capabilities of self-healing and deep penetration in dense media, we could clearly visualize the fine complex structures while NDSB illuminated from the backside of a 175-µm-thick glass substrate. As a result of NDSB's excellent penetration and superoscillatory transverse size, the 3D super-resolution capability of the proposed microscopy was experimentally verified. Owing to the advantages of label-free imaging, far field, super resolution, self-healing behaviour and deep penetration, we believe the proposed NDSB microscopy bears the potential to become a promising tool for 3D super-resolution visualization in both biological and nonbiological samples.

Alignment of magnetic sensing and clinical magnetomyography.

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG.

Super-resolution THz endoscope based on a hollow-core sapphire waveguide and a solid immersion lens.

To address a challenging problem of super-resolution terahertz (THz) endoscopy, in this paper, an antiresonant hollow-core waveguide was coupled with a sapphire solid immersion lens (SIL), aimed at subwavelength confinement of guided mode. The waveguide is formed by a polytetrafluoroethylene (PTFE)-coated sapphire tube, the geometry of which was optimized to ensure high optical performance. SIL was judiciously designed, fabricated of bulk sapphire crystal, and then mounted at the output waveguide end. Study of the field intensity distributions at the shadow side of the waveguide-SIL system revealed the focal spot diameter of ≃0.2λ at the wavelength of λ = 500 μm. It agrees with numerical predictions, overcomes the Abbe diffraction limit, and justifies super-resolution capabilities of our endoscope.

Distinguishing Single-Metal Nanoparticles with Subdiffraction Spatial Resolution Using Variable-Polarization Fourier Transform Nonlinear Optical Microscopy.

The development and use of interferometric variable-polarization Fourier transform nonlinear optical (vpFT-NLO) imaging to distinguish colloidal nanoparticles colocated within the optical diffraction limit is described. Using a collinear train of phase-stabilized pulse pairs with orthogonal electric field vectors, the polarization of nonlinear excitation fields are controllably modulated between linear, circular, and various elliptical states. Polarization modulation is achieved by precise control over the time delay separating the orthogonal pulse pairs to within hundreds of attoseconds. The resultant emission from gold nanorods is imaged to a 2D array detector and correlated to the excitation field polarization and plasmon resonance frequency by Fourier transformation. Gold nanorods with length-to-diameter aspect ratios of 2 support a longitudinal surface plasmon resonance at approximately 800 nm, which is resonant with the excitation fundamental carrier wavelength. Differences in the intrinsic linear and circular dichroism resulting from variation in their relative alignment with respect to the laboratory frame enable optical differentiation of nanorods separated within 50 nm, which is an approximate 5-fold improvement over the diffraction limit of the microscope. The experimental results are supported by analytical simulations. In addition to subdiffraction spatial resolution, the vpFT-NLO method intrinsically provides the polarization- and frequency-dependent resonance response of the nanoparticles-providing spectroscopic information content along with super-resolution imaging capabilities.