Improvement In Spatial Resolution Research Articles

An improvement in accuracy and spatial resolution of the pattern-matching sea ice drift from SAR imagery

ABSTRACTSea ice drift is a crucial parameter for sea ice flux, atmospheric and ocean circulation, and ship navigation. Pattern matching is widely used to retrieve sea ice drift from Synthetic Aperture Radar (SAR) data, but it often yields mismatched vectors and coarse spatial resolution. This study presents a framework to enhance the spatial resolution and accuracy of pattern-matching sea ice drift derived from SAR images. The framework employs the Accelerated-KAZE feature extraction method and Brute-Force feature matcher to extract feature-tracking sea ice drift vectors from SAR data, with mismatched vectors subsequently removed. The pattern-matching vectors are then refined by fusing with these feature-tracking vectors, using a Co-Kriging algorithm. Using the sea ice drift product from the Technical University of Denmark space as the pattern-matching vector field for refinement, the framework's effectiveness is evaluated by comparing the refined vectors with buoy displacements and pattern-matching vectors across five selected regions. Results show a reduction in velocity and direction root mean square error (RMSE) by 0.47 km/d (22%) and 4.97° (28%), respectively, and an enhanced spatial resolution from 10 km to 1 km. The findings demonstrate the framework's success in improving the accuracy and resolution of pattern-matching sea ice drift from SAR imagery.

Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks.

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micrometer-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive postprocessing times. In this study, we present a contrast-free super-resolution power Doppler (CS-PD) technique that uses deep networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS-PD could achieve an approximate twofold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS-PD showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio (PSNR) of 25.52. Moreover, CS-PD was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS-PD was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep neural network also allows CS-PD to be implemented for real-time imaging. These features of CS-PD offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

A Technical Analysis of Digital Image and Video Processing

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 M m 2 for a 0.35 M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel’s edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing.

Resolution enhancement via guided filtering for spatial-frequency multiplexing single-shot high-speed imaging.

The frequency recognition algorithm for multiple exposures (FRAME) is a progressive single-shot high-speed videography technique that employs the spatial-frequency multiplexing concept to provide high temporal and spatial resolution. However, the inherent crosstalk from the zero-frequency component to the carrier-frequency component leads to resolution degradation and artifacts. To improve recovered frames' quality, we propose a FRAME reconstruction method using guided filters for a removal of the zero-frequency component, which can minimize the artifacts while enhance spatial resolution. A total variation (TV) denoising operation is involved to remove artifacts further to achieve optimized performances. Simulations and experiments were conducted to demonstrate the robust and efficient post-processing capability of the proposed method. With a two-frame experimental system, the results of a USAF 1951 resolution target reveal a 1.8-fold improvement in spatial resolution from 16 lp/mm to 28.5 lp/mm. For complex dynamic scenarios, the wide field of high-speed fuel spray was shot and the proposed method can resolve two droplets with a 30 μm distance which outperforms the traditional method.

Self-assembly crack metallic network applied on light-addressable potentiometric sensor for optimizing photoelectric conversion efficiency

Light-addressable potentiometric sensor (LAPS) is a kind of biochemical sensor based on semiconductor field effect addressed by light. In order to improve spatial resolution of chemical imaging system based on LAPS, metallic network is applied to suppress crosstalk. In this study, crack metallic network (CMN) is proposed to shrink electrode coverage and optimize photoelectric conversion efficiency. The crack pattern on colloidal film is used as template and crack metallic network is prepared on substrate surface by magnetron sputtering. LAPS with crack metallic network has a satisfactory sensor performance and a certain improvement of spatial resolution. Meanwhile, the coverage of crack metallic network remarkably reduces to 8.9 % and relative optical transmittance significantly increases to 90.95 %, corresponding to the photocurrent improvement. The results indicate crack metallic network effectively increases photocurrent magnitude under an acceptable spatial resolution.

Impact of Wolfmet Tungsten Alloys as Parallel-Hole Collimator Material on Single-Photon Emission Computed Tomography Image Quality and Functional Parameters: A Simulating Medical Imaging Nuclear Detectors Monte Carlo Study.

Objectives Collimators have a significant role in image quality and detectability in single-photon emission computed tomography (SPECT) imaging. Using an appropriate alloy that effectively absorbs scattered photons, without induced secondary x-rays, and with proper rigidity and weight may provide an effective approach to the image improvement that conventionally collimators made of lead (Pb). Materials and Methods A Siemens E.CAM SPECT imaging system equipped with low-energy high-resolution (LEHR) collimator was simulated by the Simulating Medical Imaging Nuclear Detectors Monte Carlo program. Experimental and simulated data were compared based on a 2-mm 99m Tc point source in an acrylic cylindrical Deluxe phantom (Data Spectrum, Inc). Seven types of tungsten (W) alloys (Wolfmet), with W content from 90 to 97% by weight, were then used as collimator materials of the simulated system. Camera parameters, such as energy- and spatial resolution, image contrast, and collimator-related parameters, such as fraction of septal penetration, scatter-to-primary ratios, and percentage of induced secondary x-rays, due to interactions in the collimator, were evaluated. Results Acceptable conformity was found for the simulated and experiment systems in terms of energy spectra, 10.113 and 10.140%, full width at half-maximum (FWHM) of the point spread function (PSF) curves, 8.78 and 9.06 mm, sensitivity, 78.46 and 78.34 cps/MBq, and contrast in images of 19.1 mm cold spheres in the Deluxe phantom, 79.17 and 78.97%, respectively. Results on the parameters of the simulated system with LEHR collimator made from the alloys showed that the alloy consisting of 90% W, 6% nickel, and 4% copper provided an FWHM of 8.76 mm, resulting in a 0.2% improvement in spatial resolution. Furthermore, all the Wolfmet collimators showed a 48% reduction in the amount of X-rays production compared to the Pb. Conclusion A Wolfmet LEHR collimator, made by a combination of W (90%), Ni (6%), and Cu (6%) provides a better image quality and detectability compared to the Pb.

Spatial resolution improvement of the multispectral satellite images in the absence of high spatial resolution images through integration of image fusion and deep learning methods

Spatial resolution improvement of the multispectral satellite images in the absence of high spatial resolution images through integration of image fusion and deep learning methods

Indocyanine green based fluorescence imaging improved by deep learning.

Intraoperative identification of malignancies using indocyanine green (ICG) based fluorescence imaging could provide real-time guidance for surgeons.Existing ICG-based fluorescence imaging mostly operates in the NIR-I (700-1,000 nm) or the NIR-IIa' windows (1,000-1,300 nm), which not optimal in terms of spatial resolution and contrast since their light scattering is higher than the NIR-IIb window (1,500-1,700 nm). It is highly desired to achieve ICG-based fluorescence imaging in the NIR-IIb window, but it is hindered by its ultra-low NIR-IIb emission tail of ICG. Herein we employ a generative adversarial network (GAN) to generate NIR-IIb ICG images directly from the acquired NIR-I ICG images. This approach was investigated by in vivo imaging of sub-surface vascular, intestine structure, and tumors, and their results demonstrated significant improvement in spatial resolution and contrast for ICG-based fluorescence imaging. It is potential for deep learning to improve ICG-based fluorescence imaging in clinical diagnostics and image-guided surgery in clinics. This article is protected by copyright. All rights reserved.

Spatial and spectral characteristics in realizations of broadband terahertz spectroscopy on a subwavelength scale

In this work, we take aim at the fundamental challenge for realizations of broadband terahertz (THz) spectroscopy on a subwavelength scale. We introduce apertured THz microjets in this effort to resolve the fundamental limits of spatial resolution and spectral bandwidth. The THz microjets are formed as intense foci at the rear of engineered (microcomposite) spheres and are coupled through subwavelength (circular) apertures. Such coupling enables effective transmission of THz power through samples with broad spectral bandwidths and fine spatial resolutions. We show that the apertures function as high-pass filters, with their diameter d enabling strong transmission above a cutoff frequency fc. Our theoretical and experimental results reveal that the values for d and fc are prescribed by a fixed spatial-spectral product dfc, whereby reductions in d (to improve the spatial resolution) can raise fc into the targeted spectrum (at the expense of spectral bandwidth). We use this understanding to demonstrate broadband (0.3–0.7 THz) THz spectroscopy of lactose at the subwavelength (365 µm) scale. These results for apertured THz microjets represent a 20-fold improvement in spatial resolution over analogous apertured THz plane waves. Overall, our findings show promise for studies of carcinogenesis, pathogenesis, and the like.

Use of Generative Adversarial Network Algorithm in Super-Resolution Images to Increase the Quality of Digital Elevation Models Based on ALOS PALSAR Data

Digital elevation models are responsible for providing altimetric information on a surface to be mapped. While global models of low and medium spatial resolution are available open source by several space agencies, the high- resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Here we address this limitation by the utilization of deep learning algorithms coupled with Single Image Super-Resolution techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based (Generative Adversarial Network-based) methodology enables the improvement of the initial spatial resolution of low-resolution images. In the geospatial data context, for example, these algorithms can be used with digital elevation models and satellite images. The methodological approach uses a dataset with digital elevation models SRTM (Shuttle Radar Topography Mission) (30 meters of spatial resolution) and ALOS PALSAR (12.5 meters of spatial resolution), created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained. It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased. Furthermore, the visual analysis of the generated image against the high- and low-resolution ones showed a great similarity between the first two.

High-Resolution Distributed Analysis on Brillouin Dynamic Grating by Differential Measurement and Matched Filtering

Improvement of spatial resolution in the distributed analysis on Brillouin dynamic grating (BDG) in a polarization-maintaining fiber (PMF) is demonstrated using a differential measurement (DM) scheme and a matched RF low-pass filter. The DM is applied to remove the reflection signals from uncorrelated positions, and a RF low pass filter optimized for a target spatial resolution is used to suppress the high-frequency common noise remaining in the BDG spectrum. In experimental confirmation distribution of the BDG spectra in a 1 km PMF is measured with a 5 cm spatial resolution, which is equal to the nominal spatial resolution determined by the synthesis of optical coherence function in the correlation-domain analysis system.

Tunable Spatial Resolution of a Scanning Probe Microscopy (SPM) by an Efficient Cryosorption Pump

Scanning Probe Microscopy (SPM) is a useful instrument to study the surface topography of the sample at an atomic resolution. SPM system is sensitive to vibration and contamination on the surface, and therefore a vibration-free and clean environment are required to improve spatial resolution in Z-height. In this paper, a detailed new design of stand alone cost effective, vibration free activated carbon based cryosorption pump integrated and its detailed integration with SPM system is discussed. The efficiency of cryosorption pump depends on the radial temperature distribution, which was analysed using transient thermal analysis in Ansys software. The temperature profile obtained from the thermal simulation results reveals that a spiral wound copper mesh based partition surrounding the activated carbon in cryosorption pump improves the temperature distribution in the radial direction. The pump can operate with a maximum of 25 litres of Liquid nitrogen consumption and 4.5 hrs of continuous operation. The activated carbon was used as an adsorbent showed a hexagonal crystal structure from the X-Ray Diffraction (XRD) studies and its average pore size was obtained from by Brunauer-Emmett-Teller (BET) analysis was 0.34 nm. These tests were conducted to determine the effect on the pump-down characteristics of developed cryosorption pump. The improvement in spatial resolution was studied through detailed experimental analysis in Conductive Atomic Force Mode (CAFM) by Piezo Force Microscopy (PFM) which showed a 50 percent decrease of the current when imaged in a vacuum environment, phase and magnitude images showed the less distorted and clear visibility of the piezo domains. The magnetic interaction by Magnetic Force Microscopy (MFM) Q-factor was studied at the resonance frequency of 61.5 kHz. MFM showed that the Q-factor improved from 20 to 500 at 1E-3 mbar operating pressure. The drift of the MFM tip with respect to holding time showed a saturated Q-factor at 500. Non-contact mode by Atomic Force Microscopy (AFM) showed a spatial resolution enhancement of 0.89 nm.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Suppression of the Talbot effect in Fourier transform acousto-optic imaging.

We report on the observation and correction of an imaging artifact attributed to the Talbot effect in the context of acousto-optic imaging using structured acoustic waves. When ultrasound waves are emitted with a periodic structure, the Talbot effect produces π-phase shifts of that periodic structure at every half of the Talbot distance in propagation. This unwanted artifact is detrimental to the image reconstruction, which assumes near-field diffraction is negligible. Here, we demonstrate both theoretically and experimentally how imposing an additional phase modulation on the acoustic periodic structure induces a symmetry constraint leading to the annihilation of the Talbot effect. This will significantly improve the acousto-optic image reconstruction quality and allows for an improvement of the reachable spatial resolution of the image.

Deep Neural Network for Blind Visual Quality Assessment of 4K Content

The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement in spatial resolution. However, the high spatial resolution brings a great challenge for video transmission and storage. Therefore, it is necessary to compress or downscale the 4K content before transmitting it to end-users. Existing blind image quality assessment (BIQA) methods are not suitable for 4K contents due to the high spatial resolution and specific distortions caused by upscaling methods. In this paper, we propose a deep learning-based BIQA model for 4K content, aiming to recognize true and pseudo 4K content and meanwhile evaluate their visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction. Then, we extract various visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score of each patch respectively. The overall quality index is obtained through averaging the results of all patches. The proposed model is trained via the multi-task learning manner and the uncertainty principle is introduced to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

Bioinspired color-near infrared endoscopic imaging system for molecular guided cancer surgery.

Fluorescently guided minimally invasive surgery is improving patient outcomes and disease-free survival, but biomarker variability hinders complete tumor resection with single molecular probes. To overcome this, we developed a bioinspired endoscopic system that images multiple tumor-targeted probes, quantifies volumetric ratios in cancer models, and detects tumors in ex vivo samples. We present a new rigid endoscopic imaging system (EIS) that can capture color images while simultaneously resolving two near-infrared (NIR) probes. Our optimized EIS integrates a hexa-chromatic image sensor, a rigid endoscope optimized for NIR-color imaging, and a custom illumination fiber bundle. Our optimized EIS achieves a 60% improvement in NIR spatial resolution when compared to a leading FDA-approved endoscope. Ratio-metric imaging of two tumor-targeted probes is demonstrated in vials and animal models of breast cancer. Clinical data gathered from fluorescently tagged lung cancer samples on the operating room's back table demonstrate a high tumor-to-background ratio and consistency with the vial experiments. We investigate key engineering breakthroughs for the single-chip endoscopic system, which can capture and distinguish numerous tumor-targeting fluorophores. As the molecular imaging field shifts toward a multi-tumor targeted probe methodology, our imaging instrument can aid in assessing these concepts during surgical procedures.

Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging

Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Here we develop a deep-learning based single-frame super-resolution microscopy (SFSRM) method which utilizes a subpixel edge map and a multicomponent optimization strategy to guide the neural network to reconstruct a super-resolution image from a single frame of a diffraction-limited image. Under a tolerable signal density and an affordable signal-to-noise ratio, SFSRM enables high-fidelity live-cell imaging with spatiotemporal resolutions of 30 nm and 10 ms, allowing for prolonged monitoring of subcellular dynamics such as interplays between mitochondria and endoplasmic reticulum, the vesicle transport along microtubules, and the endosome fusion and fission. Moreover, its adaptability to different microscopes and spectra makes it a useful tool for various imaging systems.

Enhanced light-field image resolution via MLA translation.

This work describes a method that effectively improves the spatial resolution of light-field images without sacrificing angular resolution. The method involves translating the microlens array (MLA) linearly in both x- and y-directions in multiple steps to achieve 4 ×, 9 ×, 16 × and 25 × spatial resolution improvements. Its effectiveness was firstly validated through simulations with synthetic light-field images, demonstrating that distinct spatial resolution increments can be achieved by shifting the MLA. An MLA-translation light-field camera was built based on an industrial light-field camera, with which detailed experimental tests were carried out on a 1951 USAF resolution chart and a calibration plate. Qualitative and quantitative results prove that MLA translations can significantly improve measurement accuracy in x- and y- directions while preserving z-direction accuracy. Finally, the MLA-translation light-field camera was used to image a MEMS chip to demonstrate that finer structures of the chip can be acquired successfully.

3D Electronic Sensors for Bio‐Interfaced Electronics and Soft Robotics

AbstractMulti‐dimensional electronic devices for sensing applications have received immense attention in the fields of bio‐interface electronics and soft robotics to achieve structural manifolds in recent years. The structural diversities of sensors are tuned based on the environments in which they encounter multipurpose and complex form factors of electronics. 3D electronics are one of the emerging sensor platforms characterized by structural adaptability and compatibility on free target surfaces, resulting in unprecedented improvement of spatial resolution. Herein, the recent progress made in the fabrication and usage of 3D electronic sensors that are classified into two groups based on the methodological concepts used for fabrication in bio‐interfaced electronics and soft robotics applications, is reviewed. The latest advances in the design and development of 3D printable and 2D to 3D shape‐deformable electronic sensors are reviewed with a focus on printable materials and deformation mechanisms, respectively. 3D printing techniques enable the facile fabrication of arbitrary and seamless electronics without the use of conventional microfabrication technologies. Deformable electronics cover their self‐tune architectures and functions depending on mechanical deformation based on buckling, origami, and kirigami fabrication strategies. Finally, the printed and deformable 3D sensors show their potential for use in next‐generation sensing platforms.

A phantom for the quantitative determination and improvement of the spatial resolution in slice-selective 2D-FT magnetic resonance micro-imaging and -microscopy based on Deep X-ray Lithography (DXRL)

Introduction: The most important assessed quality-control (QC) criteria for improvements in high-resolution imaging are represented by the contrast-to-noise-ratio and spatial resolution. Ultra-High-Field (UHF) Magnetic-Resonance-scanners (B ≥ 7 T) for medical research allowed for the improvement in spatial resolution up to the microimaging and nominal microscopy range [pixel-size: ps < (100 μm)2], even in-vivo on humans just recently. Preclinical MRI- and dedicated MR-microscopy (MRM) scanners already allow for microimaging and MRM (1-256 μm) but lack a sensible spatial resolution phantom for QC and performance improvements in hardware, pulse-sequencing and MRprotocols. In most scientific MRI articles, the spatial resolution is characterized by the ps, though this measurement parameter only limits the actual resolution.Methods: Here the Modulation-Transfer-Function (MTF) is used as evaluation concept for the determination of the spatial resolution in MRM using simple intensity profiles. The resolution limit is defined using a critical modulation-level. In approaching visual impressions on spatial resolution an additional criterion derived from the Modulation-depth-to-Noise-Ratio (MNR) is proposed. A practical method for assessment based on a concrete phantom design and its realization is shown.Results: The phantom design consists of several sets of fine grids, specifically featuring high structural anisotropy for optimum SNR and CNR, with different spatial periods ranging from a1 = 256 μm down to a8 = 2 μm, not only for a quick visual qualitative check, but also for quantification of resolution using the MTF for two different spatial encodings in two orthogonal in-plane directions. The challenging demands on the manufacturing technology especially with regard to the aspect-ratio are approached using Deep-X-Ray-Lithography (DXRL) relying on the high brilliance of Synchroton-radiation. Smallest grid plates with width of 4 μm corresponding to 125 line pairs/mm at a plate depth of 100 μm were achieved.Discussion: MR-microscopic images, originating from a microscopy insert on a human UHF-MR-scanner, were used for demonstration of the evaluation process with two independent resolution-criteria. The developed prototype offers unique possibilities for quantitative resolution QC on UHF human and preclinical MR-scanners. Such a resolution-phantom might be very important for the improvement of MR-pulse-sequences, MR-protocols and even hardware. In principle the phantom can also be used for other microscopic imaging-modalities as for instance μCT and Optical-Coherence-Tomography (OCT).