Resolution Enhancement Research Articles

Enhancement of the signal resolution in aptamer assay upon target binding by employing DNA probes and dual fluorophores

In this study, we aimed to significantly enhance the resolution of aptamer assays by using DNA probes and dual fluorophores. The resolution is defined as the smallest change in the target concentration that causes a reliable and distinguishable signal change. Therefore, resolution enhancement in the aptamer assay ensures improved analytical performance. We first examined four aptamer-probe combinations in the aptamer assay and investigated the resolution by observing a percentage signal response difference (% ΔAAD/SYBR). A large % ΔAAD/SYBR means a discernable signal change for a certain concentration difference, which indicates a high-resolution assay. Consequently, significant resolution enhancement (approximately 3–7 times) was achieved using two DNA probes. The improved analytical performance of the resolution-enhanced aptamer assay was further demonstrated by a wide working range of quantification (i.e., 10−2–102 mg/L, four orders of magnitude), a regression curve with improved fitness, and a lower limit of detection (LOD) response (2–6 times lower).

Characterization and Differentiation of Candida auris on Dixon’s Agar Using Raman Spectroscopy

Candida auris, an emerging multidrug-resistant fungal pathogen, poses significant challenges in healthcare settings due to its high misidentification rate and resilience to treatments. Despite advancements in diagnostic tools, a gap remains in rapid, cost-effective identification methods that can differentiate C. auris from other Candida species, particularly on non-standard culture media. We used Raman spectroscopy to characterize C. auris grown on modified Dixon’s agar (mDixon) and differentiated it from Candida albicans and Candida parapsilosis. Key Raman spectral markers at 1171 cm−1 and 1452 cm−1, linked to mannan and β-glucan composition, differentiated C. auris into two subgroups, A and B. Despite the spectral similarities of groups A and B with C. albicans and C. parapsilosis, respectively, all Candida species were distinguishable through principal component analysis (PCA). Additionally, this study is the first to demonstrate the distinct spectral signature of mDixon agar, achieved through spatially offset Raman spectroscopy (SORS), which enables accurate discrimination between the culture medium and fungal samples. The observed inter-individual variability within C. auris, coupled with the spectral overlap between C. auris subgroups and other Candida species, highlights a major challenge in differentiating closely related fungi due to their similar molecular composition. Enhancements in spectral resolution and further fluorescence minimization from the culture medium are needed to reliably detect the subtle biochemical differences within these species. Despite these challenges, the results underscore the potential of Raman spectroscopy as a real-time, non-destructive, and complementary tool for fungal pathogen identification.

Reflectance mapping with microsphere-assisted white light interference nanoscopy

The characterisation of novel materials presents a challenge that requires new and original developments. To face some of these demands for making measurements at the nanoscale, a new microsphere-assisted white light interference nanoscope performing local reflectance mapping is presented. This technique presents the advantages of being non-destructive, full-field and label-free. A 145 μm diameter microsphere, glued to the end of an optical fiber, is inserted inside the white light interference microscope to improve the lateral resolution from 940 nm to 520 nm. The acquisition and the Fourier transform processing of a stack of interference images superimposed on the virtual image produced by the microsphere allows the extraction of the local reflectance over a wavelength range of 460 nm to 900 nm and a field of view of 8 μm in diameter. The enhancement in the lateral resolution of the reflectance is demonstrated through the spectral distinction of neighboring ripples on a laser-textured colored stainless-steel sample that cannot be resolved without the microsphere, on regions with a surface of 279 × 279 nm2 horizontally spaced 279 nm apart. Future improvements could potentially lead to a lateral resolution of reflectance measurement over a 100 nm diameter area in air, paving the way to sub-diffraction reflectance mapping.

Stochastically structured illumination microscopy scan less super resolution imaging

In super-resolution, a varying illumination image stack is required. This enriched dataset typically necessitates precise mechanical control and micron-scale optical alignment and repeatability. Here, we introduce a novel methodology for super-resolution microscopy called stochastically structured illumination microscopy (S2IM), which bypasses the need for illumination control exploiting instead the random, uncontrolled movement of the target object. We tested our methodology within the clinically relevant ophthalmoscopic setting, harnessing the inherent saccadic motion of the eye to induce stochastic displacement of the illumination pattern on the retina. We opted to avoid human subjects by utilizing a phantom eye model featuring a retina composed of human induced pluripotent stem cells (iPSC) retinal neurons and replicating the ocular saccadic movements by custom actuators. Our findings demonstrate that S2IM unlocks scan-less super-resolution with a resolution enhancement of 1.91, with promising prospects also beyond ophthalmoscopy applications such as active matter or atmospheric/astronomical observation.

High spatial resolution φ-OFDR based on frequency-shift averaging and rotating vector summation

The coherent fading noise hinders the spatial resolution enhancements of phase optical frequency domain reflectometry (φ-OFDR). To mitigate the coherent fading noise, this study introduces frequency-shift averaging and rotating vector summation for strain measurements. The original Rayleigh backscattering signal was first divided into numerous sub-segments in frequency domain by using the frequency-shift averaging, and each sub-signal segment was transferred into vector signal with both intensity and phase information by using the rotating vector summation. Furthermore, a spatial position correction algorithm is incorporated to counteract the degradation of phase coherent, facilitating large-scale strain measurements. After optimizing the sub-frequency range and overlap ratio of the sub-signal in the frequency domain, we experimentally demonstrated that this method achieves strain measurement with a broad strain range of 2000 με, a high spatial resolution of 0.54 mm, and a high precision of 0.87%.

Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Operation of Photoeelectron Spectrometers for Non-Invasive Photon Diagnostics at the European X-Ray Free Electron Laser

Angle-resolved photoelectron spectrometers with microchannel plate detectors and fast digitizer electronics are versatile and powerful devices for providing non-invasive single-shot photon diagnostics at a MHz repetition rate X-ray free-electron lasers. In this contribution, we demonstrate and characterize the performance of our two operational photoelectron spectrometers for the application of hard X-rays and soft X-rays as well as new automation tools and online data analysis that enable continuous support for machine operators and instrument scientists. Customized software has been developed for the real-time monitoring of photon beam polarization and spectral distribution both in single-color and two-color operation. Hard X-ray operation imposes specific design challenges due to poor photoionization cross-sections and very high photoelectron velocities. Furthermore, recent advancements in machine learning enable resolution enhancement by training the photoelectron spectrometer together with an invasive high-resolution spectrometer, which generates a response function model.

A New qNMR Compliant Savitzky-Golay Apodization Function for Resolution Enhancement.

In this work, we introduce a novel NMR apodization function designed to enhance spectral resolution while maintaining compatibility with qNMR standards. This function is based on a modified Savitzky-Golay filter, adapted for time-domain application. It effectively suppresses the negative components typically associated with derivative spectra, while also ensuring the preservation of quantitative integrity in NMR analyses.

A Structured Illumination Microscope with the Combination of Optical Sectioning and Lateral Resolution Enhancement for Precise 3D Industrial Metrology

A Structured Illumination Microscope with the Combination of Optical Sectioning and Lateral Resolution Enhancement for Precise 3D Industrial Metrology

Super resolution reconstruction of fluorescence microscopy images by a convolutional network with physical priors

Super-solution fluorescence microscopy, such as single-molecule localization microscopy (SMLM), is effective in observing subcellular structures and achieving excellent enhancement in spatial resolution in contrast to traditional fluorescence microscopy. Recently, deep learning has demonstrated excellent performance in SMLM in solving the trade-offs between spatiotemporal resolution, phototoxicity, and signal intensity. However, most of these researches rely on sufficient and high-quality datasets. Here, we propose a physical priors-based convolutional super-resolution network (PCSR), which incorporates a physical-based loss term and an initial optimization process based on the Wiener filter to create excellent super-resolution images directly using low-resolution images. The experimental results demonstrate that PCSR enables the achievement of a fast reconstruction time of 100 ms and a high spatial resolution of 10 nm by training on a limited dataset, allowing subcellular research with high spatiotemporal resolution, low cell phototoxic illumination, and high accessibility. In addition, the generalizability of PCSR to different live cell structures makes it a practical instrument for diverse cell research.

Spectral Characteristics of Fundamental–Harmonic Pairs of Interplanetary Type III Radio Bursts Observed by PSP

Based on the observations by the Parker Solar Probe (PSP) during its encounter phases of approaching the Sun, I. C. Jebaraj et al. found that fundamental–harmonic (F-H) pairs constitute a majority of interplanetary (IP) type III radio bursts. In the present Letter, spectral characteristics of the IP F-H pairs are identified and analyzed further. The observations were made with the Radio Frequency Spectrometer (RFS) experiment on the PSP spacecraft in its encounter phase from the first to the ninth orbit as it traveled from 0.17 to 0.074 au from the Sun. The result shows that the occurrence rate of F-H pairs rises significantly with the rise in the number of IP type III radio bursts detected by the PSP or the enhancement in the time resolution of the RFS instrument. In particular, we compare the relationship between F and H spectral characteristics, such as the frequency-drift rate, emission intensity, relative bandwidth, duration, and fine structure. The results will be helpful for us to understand the physics underlying the generation and evolution of the IP F-H pairs as well as other IP type III radio bursts.

Resolution enhancement of SMOS brightness temperatures: Application to melt detection on the Antarctic and Greenland ice sheets

A large part of the surface of the Greenland Ice Sheet (GrIS) and the margins of Antarctica are melting every summer, affecting their surface mass balance. Wet/dry snow status has been detected for decades using the peaks of brightness temperature at 19 GHz, and more recently at L-band (1.4 GHz) using both the SMOS and SMAP missions. SMOS owns a longer time series than SMAP with data since 2010, but the 52.5°incidence bin in the Level 3 (L3) product from Centre Aval de Traitement des Données SMOS (CATDS) that was previously used to detect melt suffers from a coarse spatial resolution. For this reason, we developed a new SMOS enhanced resolution brightness temperature (TB) product building on the radiometer version of the Scatterometter Image Reconstruction (rSIR) algorithm. We also exploited the SMOS L1C observations near 40°incidence angle instead of 52.5°as the native spatial resolution of SMOS is better at low incidence. The new product is posted on a 12.5 km polar stereographic grid and covers all the GrIS and Antarctica for 2010–2024 with twice-daily morning and afternoon acquisitions. The effective spatial resolution was evaluated to ∼30 km, a 30% enhancement compared to the SMOS L3TB at 40°and almost a 50% enhancement compared to the SMOS L3TB at 52.5°. Then, we applied a melt detection algorithm to both the enhanced resolution product at 40°and the L3TB product at 52.5°which is used in the literature. The spatial resolution enhancement results not only in the detection of smaller melt regions but also in a widespread increase in the annual number of melt days. This increase is larger than 30 days per year in the GrIS percolation area and on multiple Antarctic ice shelves. This is primarily due to the mix of dry and wet snow regions near the ice shelves grounding line, resulting in lower brightness temperature peaks in the SMOS L3TB product due to a large power spread. These findings highlight the dependence of melt detection in particular, and geophysical applications in general, on the spatial resolution of passive microwave observations. This study provides a new open dataset suitable to monitor melt at the surface and at depth on the two main ice-sheets.

Overcoming artificial structures in resolution-enhanced Hi-C data by signal decomposition and multi-scale attention.

Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures in the enhanced data due to the need to divide the large contact matrix into small patches. In addition, previous deep learning methods largely focus on local patterns, which cannot fully capture the complexity of Hi-C data. Here we propose Smooth, High-resolution, and Accurate Reconstruction of Patterns (SHARP) for enhancing Hi-C data. It uses the novel approach of decomposing the data into three types of signals, due to one-dimensional proximity, contiguous domains, and other fine structures, and applies deep learning only to the third type of signals, such that enhancement of the first two is unaffected by the patches. For the deep learning part, SHARP uses both local and global attention mechanisms to capture multi-scale contextual information. We compare SHARP with state-of-the-art methods extensively, including application to data from new samples and another species, and show that SHARP has superior performance in terms of resolution enhancement accuracy, avoiding creation of artificial structures, identifying significant interactions, and enrichment in chromatin states.

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration

Background and objectiveFor the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. MethodsAs a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. ResultsFrom experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. ConclusionsThese findings suggest that the proposed network provides sharp high-resolution images from blurry low-resolution inputs. Furthermore, we demonstrate superior performance in camera calibration by using surgical microscopic images, thus showing its potential applications in the field of practical surgical navigation.

Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.

Learning infrared degradations for coherent visible image fusion in the undecimated dual-tree complex wavelet domain

Traditional infrared (IR) and visible (VIS) image fusion methods demand identical resolution levels for source images, which can be problematic due to the inherent low-resolution nature of IR imagery. In this paper, we introduce an innovative image fusion approach that harmonizes resolution across IR-VIS source images, leading to the generation of fused images with higher resolution. We employ a convolutional neural network model to recover the real-time image degradations in IR data, with a particular focus on super-resolution through the multi degradation resolution enhancement network (MDREN). We adopt undecimated dual-tree complex wavelet transform (UDT-CWT) in our fusion process due to its near shift invariance and better directionality capabilities. This results in coherent information of the fused images with minimized noise and loss. Experiments employing five image quality assessment measures are used to compare the proposed method to nine state-of-the-art approaches and show its efficacy.

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.

Dual-spherical-wave optical scanning holographic microscopy with resolution enhancement and an extended depth of field

Conventional optical scanning holographic microscopy (OSHM) is realized by using a plane wave and a spherical wave as the illumination. The resolution of the conventional OSHM is limited by the numerical aperture of the spherical wave and cannot exceed the Rayleigh limit. In this paper, the OSHM by using dual spherical waves as the illumination is studied. The model of a Gaussian wave, together with the constraints on the signal strength and the visibility, are considered in the analysis of the dual-spherical-wave (DS) OSHM. For an object located at the symmetry plane of the DS-OSHM, the resolution can slightly exceed the Rayleigh limit, and the depth of field (DoF) is extended significantly. For a transparent object, the noise in the DS-OSHM is less than that of the conventional OSHM, thanks to the highly divergent scanning beam. If the object is off the symmetry plane, the resolution is still enhanced, but the extension of the DoF is limited. In addition, a clear reconstructed image will be observed not only at the original object plane, but also at its mirror plane. To the best of our knowledge, this phenomenon is reported for the first time.

Spatial resolution limit for a solid immersion lens

The solid immersion (SI) effect is widely used to increase the spatial resolution of optical focusing systems and even overcome the Abbe diffraction limit. Resolution enhancement offered by a SI lens is mostly a function of its geometry and refractive index nSI. While SI lenses are relatively well understood, the scaling of the resolution enhancement by such lenses is still a subject of debate, with some works reporting ≃nSI and ≃nSI2 dependencies for the hemispherical and hyperhemispherical SI lens configurations, respectively. In this paper, we offer a general argument for a resolution limit for SI optics and, then, verify it via the numerical analysis of the hemispherical and hyperhemispherical silicon SI lenses designed for the terahertz (THz) range. In fact, we find that there is no contradiction in the reported resolution enhancements ≃nSI and ≃nSI2; however, they happen in different operation regimes. We then demonstrate that the resolution values reported for the different SI lens arrangements in the visible (VIS), near-, and middle-infrared (NIR and MIR), as well as THz bands obey the derived limit. Our findings will be useful for the further design and applications of SI optics.

Resolution enhancement for high-numerical aperture extreme ultraviolet lithography by split pupil exposures: a modeling perspective

Resolution enhancement for high-numerical aperture extreme ultraviolet lithography by split pupil exposures: a modeling perspective