Image Points Research Articles

Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization.

PSNR-oriented models are a critical class of super-resolution models with applications across various fields. However, these models tend to generate over-smoothed images, a problem that has been analyzed previously from the perspectives of models or loss functions, but without taking into account the impact of data properties. In this paper, we present a novel phenomenon that we term the center-oriented optimization (COO) problem, where a model's output converges towards the center point of similar high-resolution images, rather than towards the ground truth. We demonstrate that the strength of this problem is related to the uncertainty of data, which we quantify using entropy. We prove that as the entropy of high-resolution images increases, their center point will move further away from the clean image distribution, and the model will generate over-smoothed images. Implicitly optimizing the COO problem, perceptual-driven approaches such as perceptual loss, model structure optimization, or GAN-based methods can be viewed. We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss). DECLoss utilizes the clustering property of contrastive learning to directly reduce the variance of the potential high-resolution distribution and thereby decrease the entropy. We evaluate DECLoss on multiple super-resolution benchmarks and demonstrate that it improves the perceptual quality of PSNR-oriented models. Moreover, when applied to GAN-based methods, such as RaGAN, DECLoss helps to achieve state-of-the-art performance, such as 0.093 LPIPS with 24.51 PSNR on 4× downsampled Urban100, validating the effectiveness and generalization of our approach.

Ultrasonic guided wave phased array based on reconstructed pulse-echo signals from phase information

Abstract Aiming at the inherent dispersion characteristics of ultrasonic guided waves and the problems of gratings and side lobes in classical total focusing method (TFM) imaging algorithms, this paper proposes a full focus imaging algorithm based on reconstruct signals from phase information. Utilizing the numerical solution of the dispersion curve in the wavenumber domain, the time domain signal of Lamb wave is propagated backward to revise the changes in waveform shape caused by dispersion, and the flight time of the pulse-echo is eliminated simultaneously. The phase of pulse-echo after dispersion compensation should be consistent with the excitation signal. And the pulse-echo should have a large waveform correlation coefficient with the excitation signal. For each imaging point, dispersion compensation for all collected signals is respectively implemented so that the sign coherence factor (SCF) and the waveform correlation factor (WCF) between pulse-echo and excitation waveform can be extracted to reconstruct scattering signals. The reconstructed signals only contain information about the pulse-echo from defect. TFM imaging of the reconstructed signal can effectively suppress the grating side lobes of classical TFM imaging and improve the signal-to-noise ratio (SNR).

An improved immersed boundary method for turbulent flow simulations on Cartesian grids: extension of a global geometric approach for thin boundary layers and strong flow incidence

This paper presents further improvements to the immersed boundary method introduced in [1]. The proposed developments take place during the pre- and post-processing stages of the simulation workflow and aim to further increase the accuracy of the approach for steady-state simulations of high Reynolds number turbulent flows around aerodynamic geometries facing strong incidence. To this end, the location of the forcing points is further optimized prior to simulation, with an adaptive and local modeling height that accounts for the evolution of the turbulent boundary layer thickness, especially at the leading edge. In addition, the direct extrapolation of the pressure solution at the wall is replaced by first- and second-order reconstructions using the normal pressure gradients interpolated at a new set of image points. This second approach is used only in post-processing, after the simulation, and prevents the degradation of the wall pressure in the presence of strong curvatures or thin boundary layers. These developments have been validated by simulating subsonic turbulent flows around the NACA0012 profile, 2D multi-element airfoil (2DMEA), and HL-CRM half-plane at significant angles of attack. Smooth and accurate skin pressure and friction coefficients are observed, in excellent agreement with body fitted wall-resolved solutions, even for coarser Cartesian meshes. Better drag and lift coefficients calculated by direct near-field integrations are obtained, along with accurate predictions of the near-wall flow physics throughout the turbulent boundary layer.

PPLICATION OF KINEMATIC APPROACHES IN GEOMETRIC PROBLEMS

This work is devoted to the use of kinematic methods in the process of solving geometric problems. The first part of the work is devoted to solving geometric problems by considering the movements of some image points. The minimum knowledge required to apply the kinematic method in elementary geometry is briefly outlined. Then a solution to a generalized version of a well-known geometric problem is presented. The importance of studying elements of operator calculus from an interdisciplinary perspective is also presented. The next part of the article examines problems selected according to the principle of gradual complication, from high school to bachelor’s degree. In these problems, the radius of curvature at various points is determined by kinematic way. Using the result of solving a kinematic problem about a body thrown in a horizontal direction, the expression for the radius of curvature of a parabola is determined. One physical method for determining the radius of curvature of a cycloid is presented. The problem of motion, which generates the equations of an ellipse and a hyperbola, is considered, and a kinematic version of determining the radii of curvature at the points of intersection of these curves and axes of symmetry is presented. The main methodological novelty of the work lies in the development, generalization and systematic presentation of kinematic problems that bring geometric results. Some methodological guidelines and arguments are also given to substantiate the importance of the discussed kinematic approaches to solving geometric problems.

JWST MIRI flight performance: Imaging

The Mid-Infrared Instrument (MIRI) aboard the James Webb Space Telescope (JWST) provides the observatory with a huge advance in mid-infrared imaging and spectroscopy covering the wavelength range of 5–28 µm. This paper describes the performance and characteristics of the MIRI imager as understood during observatory commissioning activities, and through its first year of science operations. We discuss the measurements and results of the imager’s point spread function, flux calibration, background, distortion and flat fields as well as results pertaining to best observing practices for MIRI imaging, and discuss known imaging artefacts that may be seen during or after data processing. Overall, we show that the MIRI imager has met or exceeded all its pre-flight requirements, and we expect it to make a significant contribution to mid-infrared science for the astronomy community for years to come.

Spatial heterogeneity and nonlinearity study of bike-sharing to subway connections from the perspective of built environment

Bike-sharing has become an effective solution to address the "last mile" challenge in public transit. Existing studies insufficiently consider architectural environmental factors from a design perspective in impacting bike-sharing-to-subway connection(BSC) behavior, and often relying on subjective assessments to explain the spatial heterogeneity of the built environment on BSC travel. To investigate the impact mechanisms of the built environment on BSC behaviors, this study used weekly data on bike-sharing travel trajectories from April 7 to April 14, 2021, in Shenzhen, China, as the research sample. By integrating large-scale urban point of interest(POI), street-view images and road-network data from Shenzhen, this study examines the mechanisms by which the built environment influences BSC travel. This study initially applied linear regression and multiscale geographically weighted regression (MGWR) to analyze the spatial heterogeneity of built environment impacts on BSC travel. Then, XGBoost's nonlinear relationships were used to explain and validate the MGWR results. This study reveals that functional diversity is the most significant factor influencing BSC travel. At subway stations where it is difficult to enhance functional diversity, improvements in road connectivity, vegetation, and enclosure can enhance BSC commuting. These findings are crucial for decreasing the reliance on motor vehicles and fostering sustainable urban growth.

Performance evaluation of a GAGG-SiPM based compton camera for gamma-ray astronomy

We fabricated and calibrated a GAGG-SiPM based Compton camera which can provide directional information of radiation sources for gamma-ray astronomy. The proposed Compton camera consists of two layers of 8 × 8 GAGG scintillator detectors, directly read out by an 8 × 8 SiPM (MicroFC-60035) array. Each GAGG crystal in the scatterer layer measures 6 × 6 × 8 mm3 in 7.2 mm pitches, while that in the absorber layer measures 6 × 6 × 12 mm3 in 7.2 mm pitches. A reconstruction software running on multiple GPUs based on the list mode MLEM algorithm for the Compton camera has also been developed. Point source imaging studies demonstrated an angular resolution (Full width at half maximum, FWHM) of 13.5°, and a measured intrinsic efficiency of 0.85% when a137Cs point source is placed 52 mm away from the scatterer with a scatterer-to-absorber distance of 57 mm. The camera's capability to simultaneously image point sources of different energies was assessed by imaging two distinct point sources, 22Na and 137Cs. The phantom imaging study was carried out with a U-shaped tube injected with 18F solution to verify the imaging capability of the system.

An efficient illumination compensation method for reverse time migration

AbstractBy directly solving the full two‐way wave equation, reverse time migration has superiority over other imaging algorithms in handling steeply dipping structures and other complicated geological models. Moreover, by incorporating the asymptotic inversion operator into reverse time migration imaging condition, the imaging algorithm is able to give a quantitative estimation of parameter perturbation in high‐frequency approximation sense. However, because conventional asymptotic inversion only accounts for geometrical spreading, uneven illumination due to irregular acquisition geometry and inhomogeneous subsurface at each image point is neglected. The omit of illumination compensation significantly affects the imaging quality. Wave‐equation‐based illumination compensation methods have been extensively studied in the past. However, the traditional wave‐equation‐based illumination compensation methods usually require high computational cost and huge storage. In this paper, we propose an efficient wave‐equation‐based illumination compensation method. Under high‐frequency approximation, we first define a Jacobian determinant to measure the regularity of subsurface illumination, and then illumination compensation operators are proposed based on the Jacobian. Through boundary integration, we further express the illumination compensation operators through extrapolated wavefields; the explicit computation of asymptotic Green's functions is thus avoided, and an efficient illumination compensation implementation for reverse time migration is achieved. Numerical results with both synthetic and field data validate the effectiveness and efficiency of the presented method.

Theranostic Intratumoral Convection-Enhanced Delivery of 124I-Omburtamab in Patients with Diffuse Intrinsic Pontine Glioma: Pharmacokinetics and Lesion Dosimetry.

Diffuse intrinsic pontine glioma (DIPG) is a rare childhood malignancy with poor prognosis. There are no effective treatment options other than external beam therapy. We conducted a pilot, first-in-human study using 124I-omburtamab imaging and theranostics as a therapeutic approach using a localized convection-enhanced delivery (CED) technique for administering radiolabeled antibody. We report the detailed pharmacokinetics and dosimetry results of intratumoral delivery of 124I-omburtamab. Methods: Forty-five DIPG patients who received 9.0-370.7 MBq of 124I-omburtamab intratumorally via CED underwent serial brain and whole-body PET/CT imaging at 3-5 time points after injection within 4, 24-48, 72-96, 120-144, and 168-240 h from the end of infusion. Serial blood samples were obtained for kinetic analysis. Whole-body, blood, lesion, and normal-tissue activities were measured, kinetic parameters (uptake and clearance half-life times) estimated, and radiation-absorbed doses calculated using the OLINDA software program. Results: All patients showed prominent activity within the lesion that was retained over several days and was detectable up to the last time point of imaging, with a mean 124I residence time in the lesion of 24.9 h and dose equivalent of 353 ± 181 mSv/MBq. Whole-body doses were low, with a dose equivalent of 0.69 ± 0.28 mSv/MBq. Systemic distribution and activities in normal organs and blood were low. Radiation dose to blood was very low, with a mean value of 0.27 ± 0.21 mGy/MBq. Whole-body clearance was monoexponential with a mean biologic half-life of 62.7 h and an effective half-life of 37.9 h. Blood clearance was biexponential, with a mean biologic half-life of 22.2 h for the rapid α phase and 155 h for the slower β phase. Conclusion: Intratumoral CED of 124I-omburtamab is a novel theranostics approach in DIPG. It allows for delivery of high radiation doses to the DIPG lesions, with high lesion activities and low systemic activities and high tumor-to-normal-tissue ratios and achieving a wide safety margin. Imaging of the actual therapeutic administration of 124I-omburtamab allows for direct estimation of the therapeutic lesion and normal-tissue-absorbed doses.

Imaging Point Defect and Planar Defect in Metals with 4D-STEM

Imaging Point Defect and Planar Defect in Metals with 4D-STEM

Weak-Texture Seafloor and Land Image Matching Using Homography-Based Motion Statistics with Epipolar Geometry

The matching of remote sensing images is a critical and necessary procedure that directly impacts the correctness and accuracy of underwater topography, change detection, digital elevation model (DEM) generation, and object detection. The texture of images becomes weaker with increasing water depth, and this results in matching-extraction failure. To address this issue, a novel method, homography-based motion statistics with an epipolar constraint (HMSEC), is proposed to improve the number, reliability, and robustness of matching points for weak-textured seafloor images. In the matching process of HMSEC, a large number of reliable matching points can be identified from the preliminary matching points based on the motion smoothness assumption and motion statistics. Homography and epipolar geometry are also used to estimate the scale and rotation influences of each matching point in image pairs. The results show that the matching-point numbers for the seafloor and land regions can be significantly improved. In this study, we evaluated this method for the areas of Zhaoshu Island, Ganquan Island, and Lingyang Reef and compared the results to those of the grid-based motion statistics (GMS) method. The increment of matching points reached 2672, 2767, and 1346, respectively. In addition, the seafloor matching points had a wider distribution and reached greater water depths of −11.66, −14.06, and −9.61 m. These results indicate that the proposed method could significantly improve the number and reliability of matching points for seafloor images.

Effect of light source wavelength on surface defect imaging in deep-water concrete dams

This study explores the relationship between surface defect imaging in deep-water concrete dams and the wavelength of the light source, aiming to improve concrete defect image quality by varying the wavelength. The underwater optical imaging model of concrete dam surfaces was developed by applying the principle of object reflection and the underwater optical propagation model. This model reflects the effect of different light wavelengths on underwater concrete defect imaging. Through a self-designed underwater optical darkroom test platform, defect image acquisition tests were conducted under various color light conditions. The results reveal the significant influence of light source wavelength on images captured in both air and water. Furthermore, under the same initial illuminance, blue light significantly enhanced the average gray value, sharpness, contrast, and the number of feature points in underwater defect images, exhibiting increments of 2.97 times, 0.48 times, 2.70 times, and 2.95 times, respectively, compared to white light. These findings can serve as a reference for the selection of color-light for the non-destructive testing of surface defects in actual underwater concrete dams.

Color image hybrid noise filtering algorithm based on deep convolution neural network

To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.

Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery

A calibration method of line-structured light system for measuring outer circle dimension during machining

The line-structured light system is widely utilized for various 3D profile measurements due to its convenience and high efficiency. However, the existing calibration methods primarily focus on establishing the relationship between object points and image points, which imposes stringent requirements on the calibration target. To address this issue, a practical calibration method based on interpolation function is proposed in order to measure a specific 3D profile - namely, the diameter of a rotating workpiece during machining. Firstly, the light stripe projected onto an ordinary machined workpiece by a light plane is captured using a Charge-Coupled Device (CCD) camera and subsequently fitted to a quadratic elliptic curve after undergoing image processing. Subsequently, the cubic spline interpolation function is selected to directly establish a mapping relationship between geometric characteristics of the elliptic curve and the diameter measurement. Several experiments are conducted to evaluate the proposed methods. The experimental results demonstrate that compared with two comparison methods, our approach reduces the calibration error of linear structured light systems by 69 % and 51 %, respectively; furthermore, it enables restoration of smooth and detailed 3D models through parallel movement of the system. Moreover, our entire calibration process proves practicable in simplifying experimental procedures while remaining suitable for industrial inspection applications.

RCI-Seg: Robust click-based interactive segmentation framework with deep reinforcement learning for biomedical images

Recently, interactive segmentation models have achieved remarkable success in the field of biomedical images. However, these models rely on the accurate and high-quality interaction information provided by users, otherwise the segmentation performance will be seriously affected. This problem is more severe in multi-target biomedical images, which means that an image contains multiple targets of interest. It is extremely challenging for users to always maintain high-quality interactions. In this paper, we propose a novel two-stage segmentation model with robust interaction points for biomedical images. In the first stage, we implement robust interaction points based on user initial interaction points and the deep reinforcement learning (DRL) model. Specifically, we build a reinforcement learning environment to simulate the movement of interaction points with agents, and obtain improved interaction points (clue points) that are beneficial for segmentation. In the second stage, we use a convolutional neural network (CNN) model to achieve segmentation by combining clue points and biomedical image. We validate the performance of our approach on five public biomedical image datasets. The experimental results show that the proposed approach outperforms several SOTA methods in multiple metrics.

High-Speed Schlieren Imaging And PIV Of Unsteady Free Jet Injection With Plume Formation

This work aims to investigate Schlieren image correlation velocimetry for flow field quantification of gaseous jet injection in non-manipulable apparatus like flow chambers. Such flow cases often struggle using tracer-based methods: Seeding particles may locally cause technical problems, or the plant design prevents the principal opportunity of tracer adding, both of which lead to a reduced method repertory. This is specifically the case under flow conditions, where contamination of apparatus internals by seeding particles should be avoided completely. Therefore, in this paper, we explore a seedless, refraction-based method to quantify the velocity field of a jet plume. As an experimental flow case, we examine a transient jet release into a closed compartment under nearly atmospheric conditions. For simplification and greater clarity, the laboratory model geometry is kept essentially two-dimensional, thus we chose a flat-jet nozzle as injection device. The experimental investigations cover high-speed Schlieren imaging and laser sheet visualization, followed by digital image correlation. Combining the traditionally qualitative Schlieren approach for flow visualization with Fast Fourier Transform and cross-correlation algorithms, we calculated the velocity vector field of an unsteady jet plume formation. Furthermore, we determined the axial jet velocity profile at steady-state conditions. Our research findings highlight the applicability of Schlieren image correlation velocimetry to quantify gaseous jet formation at encased flow sections spatially. Concluding, the results coincide with data from particle image velocimetry measurements and point out the potential of expanding flow diagnostics to hitherto hardly explored (industrial) flow cases.

Computation of Green’s Function in a Strongly Heterogeneous Medium Using the Lippmann–Schwinger Equation: A Generalized Successive Over-Relaxion plus Preconditioning Scheme

The computation of Green’s function is a basic and time-consuming task in realizing seismic imaging using integral operators because the function is the kernel of the integral operators and because every image point functions as the source point of Green’s function. If the perturbation theory is used, the problem of the computation of Green’s function can be transformed into one of solving the Lippmann–Schwinger (L–S) equation. However, if the velocity model under consideration has large scale and strong heterogeneity, solving the L–S equation may become difficult because only numerical or successive approximate (iterative) methods can be used in this case. In the literature, one of these methods is the generalized successive over-relaxation (GSOR) iterative method, which can effectively solve the L–S equation and obtain the desired convergent iterative series. However, the GSOR iterative method may encounter slow convergence when calculating the high-frequency Green’s function. In this paper, we propose a new scheme that utilizes the GSOR iterative with a precondition to solve the complex wavenumber L–S equation in a slightly attenuated medium. The complex wavenumber with imaginary components localizes the energy of the background Green’s function and reduces its singularity by enabling exponential decay. Introduction of the preconditioning operator can further improve the convergence speed of the GSOR iterative series. Then, we provide a preconditioned generalized successive over-relaxation (pre-GSOR) iterative format. Our numerical results show that if an appropriate damping factor and a proper preconditioning operator are selected, the method presented here outperforms the GSOR iterative for the real wavenumber L–S equation in terms of the convergence speed, accuracy, and adaptation to high frequencies.

Kalman Filter for a Particular Class of Dynamic Object Images

We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.

Point of care magnetic resonance neonatal neuroimaging applications and early imaging in infants under active therapeutic hypothermia: a perspective.

As care of the most vulnerable infants in the neonatal intensive care unit (NICU) evolves, improved and real-time understanding of brain health becomes key. The availability of an in-NICU magnetic resonance imaging (MRI) scanner provides unique options to bedside care providers and researchers. We present our perspective on the 1-Tesla MRI unit in our NICU and its utilities and applications both in the clinical and research fields. We also discuss our experience with early and serial MRI in a cohort of infants with hypoxic-ischemic encephalopathy while undergoing therapeutic hypothermia, using a compatible cooling blanket and monitoring apparatus with special insight into the planning and organization between providers, and parental perspectives around early, detailed imaging.