High-precision Imaging Research Articles

W1-Net: a highly scalable ptychography convolutional neural network

X-ray ptychography is a coherent diffraction imaging technique that allows for the quantitative retrieval of both the amplitude and phase information of a sample in diffraction-limited resolution. However, traditional reconstruction algorithms require a large number of iterations to obtain phase and amplitude images exactly, and the expensive computation precludes real-time imaging. To solve the inverse problem of ptychography data, PtychoNN uses deep convolutional neural networks for real-time imaging. However, its model is relatively simple, and its accuracy is limited by the size of the training dataset, resulting in lower robustness. To address this problem, a series of W-Net neural network models have been proposed which can robustly reconstruct the object phase information from the raw data. Numerical experiments demonstrate that our neural network exhibits better robustness, superior reconstruction capabilities and shorter training time with high-precision ptychography imaging.

High-precision 3D imaging using spectral encoding based on the mode-locked optical frequency comb: publisher’s note

This publisher’s note reports corrections made to Appl. Opt. 63, 7637 (2024)APOPAI0003-693510.1364/AO.537171.

PointNet + + Based Concealed Object Classification Utilizing an FMCW Millimeter-Wave Radar

AbstractIn the field of millimeter-wave (MMW) imaging, the integration of artificial intelligence (AI) has emerged as a crucial solution for addressing automation challenges. In this study, concealed object classification was successfully achieved on point cloud data from MMW radar high-precision imaging using the PointNet + + deep learning method. The utilized dataset comprises point cloud data generated through the transformation of 3D models and reconstruction of physical objects with an accuracy of less than 1 mm via MMW radar scanning. Classification accuracy was significantly improved by introducing data enhancement techniques, including the generation of homologous data and optimization of sampling points. After several evaluations, 300 epochs of training were conducted using 8192 sampling points, the results showed an accuracy of 0.998 for the training dataset and 0.996 for the test dataset. Moreover, evaluations of samples not included in the original dataset as well as multi-surface scans of concealed objects within the cardboard both resulted in correct predictions, which further validates the effectiveness and reliability of the study and demonstrates the potential of AI applied to MMW imaging.

Defect detection using integration of ultrasonic least-squares reverse time migration and generative adversarial network

ABSTRACT Accurate detection and characterisation of defects in high-density polyethylene (HDPE) materials are important for the safety of industrially critical structures. Ultrasonic non-destructive evaluation (UNDE) has proven to be a powerful tool for detecting and characterising defects in engineered materials. However, efficient and high-precision defect imaging in these highly attenuating materials remains a significant challenge for UNDE. Least-squares reverse time migration (LSRTM) offers the potential to reconstruct high-precision images of reflectivity. Yet, the conventional LSRTM iteratively updates the reflectivity model by minimising the data residuals, making it computationally expensive. In this paper, an efficient ultrasonic LSRTM algorithm within a deep learning framework is proposed. Building upon this, a generative adversarial network (GAN) is integrated to further enhance the reconstruction results by reducing artefacts in the images. Simulation and experimental results show that the proposed ultrasonic LSRTM-GAN can generate high-quality images, effectively enabling precise defect detection in HDPE.

Multiplex Collagen Fingerprinting for the Staging of Hepatic Fibrosis Using High-Precision Fluorescence-Guided SERS Imaging.

Hepatic fibrosis is a common chronic liver disease, and its severe progression can culminate in cirrhosis and hepatocellular carcinoma (HCC). Precise diagnosis and staging of hepatic fibrosis are essential to prevent liver cirrhosis and HCC. Simultaneous detection of multiplex collagen biomarkers within liver tissue is crucial for staging hepatic fibrosis. We herein for the first time constructed multiplex collagen fingerprinting for the staging of hepatic fibrosis using high-precision fluorescence-guided surface-enhanced Raman scattering (SERS) imaging. SERS/fluorescent probes, collectively referred to as SF, comprising silver nanoparticles (Ag NPs), Raman reporters, and FAM-labeled collagen targeting peptides. These probes exhibit exceptional aqueous dispersion and stability, attributed to the increased number of Asp residues in CTP. Meanwhile, SF probes, namely SF-I, SF-IV, and SF-D have demonstrated specific targeting of type I, type IV, and denatured collagen, respectively, within hepatic fibrotic tissues. The results from fluorescence-guided SERS imaging underscore the method's capacity for typing, localization, and quantification of collagen, thus providing novel insights into collagen's role in the development of hepatic fibrosis. The collagen fingerprinting strategy offers a potent toolkit for the multifaceted profiling of collagen superfamilies, holding significant implications for the precise staging of hepatic fibrosis.

High-Precision Heterogeneous Satellite Image Manipulation Localization: Feature Point Rules and Semantic Similarity Measurement

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.

High-precision 3D imaging using spectral encoding based on the mode-locked optical frequency comb

A spectral encoding imaging scheme based on the optical frequency comb (OFC) is proposed to improve the axial capability. The surface topography information of the measured sample is encoded to the frequency and phase of the mode-locked OFC, then extracted from the interference spectrum through our well-designed data processing algorithm to obtain the relative position and depth of multiple pixels simultaneously. Finally, only one-dimensional (1D) scanning is required to reconstruct the three-dimensional (3D) shape of the measured object. With the comprehensive utilization of spatial dispersion and spectral interference technique, a 3D imaging system with axial resolution of 12.5 µm, axial measurement accuracy of 0.6 µm, and depth measurement range greater than 28 mm, is experimentally demonstrated.

High-Precision ISAR Imaging for Microwave Photonic Radar: Compensating Spatial-Variant RCM and Component Phase Errors

High-Precision ISAR Imaging for Microwave Photonic Radar: Compensating Spatial-Variant RCM and Component Phase Errors

High-precision integral imaging 3D salient object detection and reconstruction with texture features based on E2E-TransGAN

Under the framework of integral imaging, a high-precision 3D salient object detection and high-quality texture features reconstruction method is proposed by using the element to element-transformer and generative adversarial network (E2E-TransGAN). The proposed method fully exploits the global salient clues of the element image array to establish cross-regional element image dependency, thereby the accuracy of the salient prediction improved. Meanwhile, our proposed E2E-TransGAN algorithm stratifies the binary salient object area and the grayscale salient object edge, effectively addressing salient region prediction errors, obvious background noise, and color loss. The experimental results show that the detection accuracy of the proposed method improves to an average of 90% across various evaluation metrics. Compared with other methods, the proposed method can reconstruct 3D images with texture features, which have high values of PSNR and SSIM, at 30 and 0.95 on average, respectively. We have experimentally verified that the proposed method achieves precise 3D object salient detection and restores the detailed textures and depth information of 3D salient objects. The proposed method provides strong support for efficient and accurate medical image analysis and simplifies subsequent computer vision tasks.

Optimizing algorithm for high-precision imaging stitching systems based on spline surfaces

As optical systems continue to advance, non-uniform rational B-spline (NURBS) surfaces increasingly being considered in asymmetric optical systems due to their localized control characteristics. However, the representation of NURBS surfaces has complicated the analysis of these systems, leading to a significant computational burden. To address this challenge, we propose an optimizing algorithm for imaging optical systems based on high-precision ray tracing of NURBS surfaces. This method initiates with getting a knot grid as prior information, in conjunction with the Newton-Raphson algorithm, to obtain high-precision numerical solutions for the intersection of rays with a NURBS surface. Building upon this methodology, we introduce an optimization technique that includes shape evaluation to generate an evaluation function specific to NURBS surfaces. This approach is then applied within a rapid optimization process that accounted for the region of ray influence. Under consistent control point grids and sampling ray conditions, we present an off-axis four-mirror system to showcase that our algorithm has achieved a computational efficiency improvement of approximately 14 times compared to the previous method. This high-precision imaging design based on spline surfaces fulfills the need for efficient and accurate algorithms for NURBS surface applications in various imaging systems, providing guidance for practical applications.

A novel high-precision lightweight concrete bridge crack image recognition method based on knowledge distillation

A novel high-precision lightweight concrete bridge crack image recognition method based on knowledge distillation

Superresolution imaging of antibiotic-induced structural disruption of bacteria enabled by photochromic glycomicelles

Bacterial evolution, particularly in hospital settings, is leading to an increase in multidrug resistance. Understanding the basis for this resistance is critical as it can drive discovery of new antibiotics while allowing the clinical use of known antibiotics to be optimized. Here, we report a photoactive chemical probe for superresolution microscopy that allows for the in situ probing of antibiotic-induced structural disruption of bacteria. Conjugation between a spiropyran (SP) and galactose via click chemistry produces an amphiphilic photochromic glycoprobe, which self-assembles into glycomicelles in water. The hydrophobic inner core of the glycomicelles allows encapsulation of antibiotics. Photoirradiation then serves to convert the SP to the corresponding merocyanine (MR) form. This results in micellar disassembly allowing for release of the antibiotic in an on-demand fashion. The glycomicelles of this study adhere selectively to the surface of a Gram-negative bacterium through multivalent sugar-lectin interaction. Antibiotic release from the glycomicelles then induces membrane collapse. This dynamic process can be imaged in situ by superresolution spectroscopy owing to the "fluorescence blinking" of the SP/MR photochromic pair. This research provides a high-precision imaging tool that may be used to visualize how antibiotics disrupt the structural integrity of bacteria in real time.

Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging

Abstract. The surface temperature of snow cover is a key variable, as it provides information about the current state of the snowpack, helps predict its future evolution, and enhances estimations of the snow water equivalent. Although satellites are often used to measure the surface temperature despite the difficulty of retrieving accurate surface temperatures from space, calibration–validation datasets over snow-covered areas are scarce. We present a dataset of extensive measurements of the surface radiative temperature of snow acquired with an uncooled thermal-infrared (TIR) camera. The set accuracy goal is 0.7 K, which is the radiometric accuracy of the TIR sensor of the future CNES/ISRO TRISHNA mission. TIR images have been acquired over two winter seasons, November 2021 to May 2022 and February to May 2023, at the Col du Lautaret, 2057 m a.s.l. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration with rough thermal regulation (7–39 °C). An improved setup with a stabilized internal temperature was developed for the second campaign, and comprehensive laboratory experiments were carried out in order to characterize the physical properties of the components of the TIR camera and its calibration. Thorough processing, including radiometric processing, orthorectification, and a filter for poor-visibility conditions due to fog or snowfall, was performed. The result is two winter season time series of 130 019 maps of the surface radiative temperature of snow with meter-scale resolution over an area of 0.5 km2. The validation was performed against precision TIR radiometers. We found an absolute accuracy (mean absolute error, MAE) of 1.28 K during winter 2021–2022 and 0.67 K for spring 2023. The efforts to stabilize the internal temperature of the TIR camera therefore led to a notable improvement of the accuracy. Although some uncertainties persist, particularly the temperature overestimation during melt, this dataset represents a major advance in the capacity to monitor and map surface temperature in mountainous areas and to calibrate–validate satellite measurements over snow-covered areas of complex topography. The complete dataset is provided at https://doi.org/10.57932/8ed8f0b2-e6ae-4d64-97e5-1ae23e8b97b1 (Arioli et al., 2024a) and https://doi.org/10.57932/1e9ff61f-1f06-48ae-92d9-6e1f7df8ad8c (Arioli et al., 2024b).

The numerical reconstruction methods for characterizing defects by full matrix data using an ultrasonic array

Abstract This study introduces ultrasonic imaging and inversion methods for the quantitative characterization of crack defects. The Total Focusing Method (TFM) is a high-precision ultrasonic imaging method, but it may produce length errors when used for large-angle defect detection. An SV transverse wave TFM method is introduced, significantly reducing length errors in a 60° crack to 8.31%. However, the ultrasonic image method is influenced by factors like array position, limiting its practicality. To overcome this limitation, the study proposes an inversion method using the subarray scattering coefficient matrix, which is more stable and less sensitive to array positioning. Comparative analysis with finite element simulations and experiments indicates optimal array positions associated with different methods. At these positions, both methods yield angular characterization errors for the 60° crack of less than 1%, validating the proposed techniques for non-destructive testing applications.

QRAGE-Simultaneous multiparametric quantitative MRI of water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength.

To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging. In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.

Vibration-attitude integrated control of large membrane diffraction space telescope

The development of telescopes with large apertures is driven by the increasing demand for higher imaging resolution and coverage. Large membrane diffraction space telescopes are particularly attractive due to their lightweight nature, ease of folding and unfolding, and high-precision imaging capabilities. However, their substantial size and flexibility make them susceptible to long-duration, low-frequency vibrations during attitude maneuvers, which degrade attitude and imaging accuracy and risk instrument damage. Consequently, an urgent need exists for a high-precision integrated control scheme. In this paper, the integrated control of vibration and attitude in a large space telescope is studied using cable actuators and control moment gyroscopes (CMGs). Since cables can only withstand limited tension, the unilateral and constrained characteristics of the control input are taken into account during the control design process. Firstly, a rigid-flexible coupling dynamic model of the space telescope is established using the velocity variation principle with hybrid coordinates. Additionally, a two-body astrodynamic model is developed to assess the impact of gravity gradient torque. Next, combining the computational torque method and H∞ control theory, an integrated control scheme is proposed, which achieves vibration and attitude control during maneuvers. Then, this paper optimizes the cable actuator positions via the discrete particle swarm optimization (PSO) algorithm and plans various attitude maneuver paths. Finally, numerical simulations are adopted to demonstrate the effectiveness of the control scheme. The results show that this integrated control scheme swiftly suppresses structural vibrations during attitude maneuvers, enabling high-precision attitude control of the space telescope.

Graphic association learning: Multimodal feature extraction and fusion of image and text using artificial intelligence techniques

With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.

High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

Dynamic 4D facial capture pipeline with appearance driven progressive retopology based on optical flow

This paper presents a production-oriented 4D facial reconstruction pipeline designed to produce high-fidelity facial mesh sequences with a consistently structured topology, while preserving the wireframe structure specified by artists. We have designed and developed a compact, efficient, and fast optical capture system based on synchronized camera arrays for high-precision dynamic 3D facial imaging. Unlike prevailing methods that primarily concentrate on single-frame reconstruction, often reliant on labor-intensive manual annotation, our framework exploits the constraint of appearance consistency to autonomously establish feature correspondence and uphold temporal coherence within the mesh. Consequently, our approach eliminates mesh drifting and jitter, enabling full parallelization for dynamic facial expression capture. The proposed pipeline decouples the non-linear deformation of facial expressions from the rigid movements of the skull through a stable external device. Leveraging progressive retopology, our methodology employs artist-guided templates as priors, ensuring the preservation of wireframe structures across the result sequence. Progressive retopology is achieved by constraining different fine-grained features of 3D landmarks, scan surface shapes, and appearance textures. The results of our study showcase facial mesh sequences with production-quality topology, adept at faithfully reproducing character expressions from photographs while achieving artist-friendly stable facial movements.

High-Precision Dichotomous Image Segmentation With Frequency and Scale Awareness.

Dichotomous image segmentation (DIS) with rich fine-grained details within a single image is a challenging task. Despite the plausible results achieved by deep learning-based methods, most of them fail to segment generic objects when the boundary is cluttered with the background. In fact, the gradual decrease in feature map resolution during the encoding stage and the misleading texture clue may be the main issues. To handle these issues, we devise a novel frequency-and scale-aware deep neural network (FSANet) for high-precision DIS. The core of our proposed FSANet is twofold. First, a multimodality fusion (MF) module that integrates the information in spatial and frequency domains is adopted to enhance the representation capability of image features. Second, a collaborative scale fusion module (CSFM) which deviates from the traditional serial structures is introduced to maintain high resolution during the entire feature encoding stage. In the decoder side, we introduce hierarchical context fusion (HCF) and selective feature fusion (SFF) modules to infer the segmentation results from the output features of the CSFM module. We conduct extensive experiments on several benchmark datasets and compare our proposed method with existing state-of-the-art (SOTA) methods. The experimental results demonstrate that our FSANet achieves superior performance both qualitatively and quantitatively. The code will be made available at https://github.com/chasecjg/FSANet.