Multilayer Imaging Research Articles

Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.

3D micromorphological reconstruction and roughness characterization of wood surface based on sequence images

A novel non-contact optical inspection technique is proposed to precisely reconstruct the three-dimensional surface structure of wood and evaluate its roughness. The feasibility of using image fusion techniques to characterize the 3D roughness of wood surfaces is explored by combining high-resolution serial image acquisition techniques with advanced data processing methods. The work focuses on examining various cut surfaces of three distinct wood species: Larch, Catalpa, and Toona sinensis. The proposed 3D roughness parameters are verified using 3D reconstruction and precise measurements. The experimental results demonstrate that the proposed method exhibits strong adaptability in measuring wood surface roughness and achieves a high degree of consistency with the results obtained from traditional digital microscope systems. This study not only validates the possible use of multilayer sequence image fusion technology in measuring wood roughness, but also establishes a solid basis for the creation of 3D assessment criteria for wood surface roughness and associated inspection systems.

Lensless Mueller holographic microscopy with robust noise reduction for multiplane polarization imaging

Lensless holographic microscopy has emerged as a powerful and cost-effective tool for computational imaging, offering high resolution over a large field of view, beneficial for various biological applications. However, conventional approaches can struggle with contrast and accurate visualization of diverse components over the samples, which can directly affect the diagnostic precision of the techniques. Mueller imaging, while offering detailed, stain-free observations of polarized light responses in samples, often has a limited field of view and single plane information. This is due to the use of high NA microscope objectives and generally complex hardware setups, thus narrowing its practical effectiveness. This work introduces a Lensless Mueller Holographic Microscopy (LMHM) system that overcomes these limitations, enabling large field of view, volumetric multi-layer imaging, and Mueller matrix computation using in-line lens-free holography setup. The proposed system provides precision visualization of polarization information in samples, offering high-quality features due to the incorporation of a numerical multi-height Gerchberg-Saxton reconstruction algorithm with additional complex field filtering and a physical rotating diffuser. The proposed LMHM framework is validated with a calibrated USAF 1951 birefringent test target. A multiplane sample containing cloth fiber is utilized to study the LMHM capabilities of imaging volumetric samples. Finally, the LMHM is used to analyze two mice’s brain slices, effectively showcasing this organ’s anatomy. Among other structures in the brain, the proposed method easily allows the visualization of, e.g., the corpus callosum. These results constitute a proof-of-concept evaluation for bioimaging applications.

A novel multi-layer image encryption algorithm based on 2D drop-wave function

A novel multi-layer image encryption algorithm based on 2D drop-wave function

Health Warnings on Instagram Advertisements for Synthetic Nicotine E-Cigarettes and Engagement

Synthetic nicotine is increasingly used in e-cigarette liquids along with flavors to appeal to youths. Regulatory loopholes have allowed tobacco manufacturers to use social media to target youths. To analyze the extent to which synthetic nicotine e-cigarette brands have implemented US Food and Drug Administration (FDA) health warning requirements and to evaluate the association between health warnings and user engagement on Instagram. In this cross-sectional study, posts from 25 brands were analyzed across a 14-month period (August 2021 to October 2022). A content analysis was paired with Warning Label Multi-Layer Image Identification, a computer vision algorithm designed to detect the presence of health warnings and whether the detected health warning complied with FDA guidelines by (1) appearing on the upper portion of the advertisement and (2) occupying at least 20% of the advertisement's area. Data analysis was performed from March to June 2024. Synthetic nicotine e-cigarette advertisement on Instagram. The outcome variables were user engagement (number of likes and comments). Negative binomial regression analyses were used to evaluate the association between the presence and characteristics of health warnings and user engagement. Of a total of 2071 posts, only 263 (13%) complied with both FDA health warning requirements. Among 924 posts with health warnings, 732 (79%) displayed warnings in the upper image portion, and 270 (29%) had a warning covering at least 20% of the pixel area. Posts with warnings received fewer comments than posts without warnings (mean [SD], 1.8 [2.5] vs 5.4 [11.7] comments; adjusted incident rate ratio [aIRR], 0.70; 95% CI, 0.57-0.86; P < .001). For posts containing warnings, a larger percentage of the warning label's pixel area was associated with fewer comments (aIRR, 0.96; 95% CI, 0.93-0.99; P = .003). Flavored posts with health warnings placed in the upper image portion received more likes than posts with warnings in the lower portion (mean [SD], 34.6 [35.2] vs 19.9 [19.2] likes; aIRR, 1.48; 95% CI, 1.07-2.06; P = .02). In this cross-sectional study of synthetic nicotine brand Instagram accounts, 87% of sampled posts did not adhere to FDA health warning requirements in tobacco promotions. Enforcement of FDA compliant health warnings on social media may reduce youth engagement with tobacco marketing.

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Abstract Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

Asymptotic multilayer pooled transformer based strategy for medical assistance in developing countries

With the continuous progress of medical imaging technology, pathology images have become an important basis for doctors to judge the condition. However, pathology images have problems such as large numbers, large sizes, and complex backgrounds, which make the task of manual recognition exceptionally difficult. The use of computer-aided diagnosis technology can improve the diagnostic accuracy of medical image recognition. Transformer model-based methods have made great progress in the field of medical image recognition, but the self-attention mechanism in them has high computational complexity. Although single-layer pooling can reduce the computational cost, the strategy is prone to feature loss, and too many self-attention operations can exacerbate the distraction problem. Based on this, this study proposes an asymptotic multilayer pooling transformer-based pathology image assistance strategy in medical decision-making systems. First, we pre-process the pathology images with denoising and enhancement to accurately capture the detailed features of the lesion region. Then the asymptotic multilayer pooling transformer pyramid structure (PMPSNet) is used to identify cell nuclei. In the self-focused module, a multilayer pooling operation is employed to simplify the sequence and efficiently capture contextual features. In addition, the strategy utilizes a pyramid encoder to obtain multi-scale feature maps and a novel multilayer perceptual structure to limit the attention interference problem. The results show that our proposed PMPSNet-L model achieves a DSC value of 0.822, which is about 1.4 % better than the second-ranked model, and the IoU value reaches up to 0.702. In addition, our PMPSNet-S maintains a relatively lightweight parameter size of about 14.49 million, which is considerably lower than the existing segmentation models. Our proposed method not only achieves higher segmentation accuracy but also has fewer model parameters and lower computational complexity.

Leveraging historical hospitality in the national parks: Fred Harvey, Mary Colter, and the heritage of the American Southwest

ABSTRACT This conceptual paper examines the Fred Harvey Company, a key entity that helped develop tourism at Southwestern national parks, in particular Grand Canyon National Park and Petrified Forest National Park. The article describes some of the influences of this organization on the image of tourism in the Southwest, from the thematic design of spaces to the commercializing of Native American cultural heritage. After examining these impacts, the contemporary state of interpretation in these national parks is highlighted. Drawing on not only the natural and cultural resources of the region, organizations such as the National Park Service or Xanterra Travel Collection employ the historical hospitality foundations of the sites to create a multi-layered image of the national park as well as connect to tourist identities.

Sparse-angle optical projection tomography based on multi-layer sparsity and deep image priors

Optical projection tomography (OPT) is a computational imaging technique to acquire the volumetric images of biological samples ranging from millimeters to centimeters. For in-vivo OPT, it is essential to minimize the inspection time to reduce the adverse impacts on organisms, including the anesthetic side effect and phototoxicity. It can be achieved by projecting the samples from equally spaced sparse angles, but this method will induce radial artifacts in the reconstructed tomographic images. This paper develops a high-quality reconstruction method for sparse-angle OPT by jointly exploiting the multi-layer sparsity prior and deep image prior (DIP) on the volumetric images. The DIP module works in an unsupervised manner without requirement on a training dataset. This method can also address the inter-layer correlation within the samples, and process multi-layer images in parallel to improve the reconstruction accuracy and efficiency. Simulations and experiments demonstrate the superiority of the proposed method over some widely used reconstruction algorithms for sparse-angle OPT.

Enabling accessibility to the CellScape spatial biology workflow for researchers using standard histology protocols and pre-slided specimens.

e15185 Background: Solid tumor specimen collection during clinical trials often follows a standard histology protocol, ending in the generation of FFPE sections mounted onto 1 mm histology slides. Though a common sample format for researchers developing tissue-based biomarkers, slides present a data quality challenge for multiplexed spatial biology workflows. Here we present data describing an innovative flow cell design fully compatible with pre-slided tissue specimens. We collected spatial biology data using this slide-compatible flow cell and compared it to the gold standard direct mounting on coverglass. Several rounds of marker staining and imaging demonstrate equivalency in method outcomes including image resolution, signal to noise, and re-interrogation capacity. Methods: FFPE biopsy tissue specimens (tumor punch biopsies and a tonsil section) were mounted on standard histology slides. These specimens were incorporated into flow cells using a new glass coverslip and porting device, CellScape™ Slide, that pairs with a standard histology slide to form a microfluidic chamber. After mounting, each sample was probed for consensus CD markers (CD45, CD3, CD4,CD8,CD20, etc.) through several rounds of iterative immunofluorescence staining using CellScape™ Precise Spatial Multiplexing. The resulting multi-layered whole slide image dataset was then interrogated for spatial resolution and signal-to-noise using digital image analysis. Results: Spatial resolution and signal-to-noise ratio (SNR) were assessed through comparison to data collected from similar samples using the existing CellScape microfluidic chip. The lowest intensity signal collected with at least a 30:1 SNR (acceptable for image analysis) was equivalent or lower in the CellScape Slide preparation (equivalent sensitivity). The highest intensity signal collected was equivalent between the two flow cell designs. Comparing stain quality (specificity, sensitivity) among markers stained through multiple rounds of iterative staining, imaging, and signal remove via photoinactivation showed that the CellScape Slide flow cell is on par with or better than the existing CellScape chip. Further, the usable clear window of the CellScape Slide flow chamber, measuring 16 mm x 44 mm, was found to be greater than twice that of the existing chip. Literature review determined this to also be the largest imaging window available among commercially available spatial biology platforms. Conclusions: Assessment of the CellScape Slide flow cell, innovative in providing access to the CellScape spatial biology workflow for pre-mounted FFPE tissue, determined that the optical performance of the new flow cell is at least on par with the existing flow cell. Further, the usable window is over twice the area, and the new flow cell is fully capable of supporting re-interrogation of the sample.

A Novel Small Form-Factor Handheld Optical Coherence Tomography Probe for Oral Soft Tissue Imaging.

Tissue imaging is crucial in oral cancer diagnostics. Imaging techniques such as X-ray imaging, magnetic resonance imaging, optical coherence tomography (OCT) and computed tomography (CT) enable the visualization and analysis of tissues, aiding in the detection and diagnosis of cancers. A significant amount of research has been conducted on designing OCT probes for tissue imaging, but most probes are either heavy, bulky and require external mounting or are lightweight but straight. This study addresses these challenges, resulting in a curved lightweight, low-voltage and compact handheld imaging probe for oral soft tissue examination. To the best of our knowledge, this is the first curved handheld OCT probe with its shape optimized for oral applications. This probe features highly compact all-fiber optics with a diameter of 125 μm and utilizes innovative central deflection magnetic actuation for controlled beam scanning. To ensure vertical stability while scanning oral soft tissues, the fiber was secured through multiple narrow slits at the probe's distal end. This apparatus was encased in a 3D-printed angular cylinder tube (15 mm outer diameter, 12 mm inner diameter and 160 mm in length, weighing < 20 g). An angle of 115° makes the probe easy to hold and suitable for scanning in space-limited locations. To validate the feasibility of this probe, we conducted assessments on a multi-layered imaging phantom and human tissues, visualizing microstructural features with high contrast.

Improved deep-learning rotor fault diagnosis based on multi vibration sensors and recurrence plots

Deep learning techniques are increasingly applied to time series data, offering promising results in various fields. Deep learning techniques can handle data from multiple sensors to detect anomalies in an industrial environment. This paper proposes a new method of anomaly detection based on a multilayer image representation of different vibration sensors’ recurrence plots. Each sensor’s recurrence plot forms a layer. The performance and reliability of our method were assessed using an experimental database collected under different load conditions and with different types of rotor anomalies. Experiment results demonstrate the effectiveness of GoogLeNet using individual and multi-layered recurrence plots to find rotor faults in an induction motors.

Impact of residual stress on coronary plaque stress/strain calculations using optical coherence tomography image-based multi-layer models.

Mechanical stress and strain conditions play an important role in atherosclerosis plaque progression, remodeling and potential rupture and may be used in plaque vulnerability assessment for better clinical diagnosis and treatment decisions. Single layer plaque models without residual stress have been widely used due to unavailability of multi-layer image segmentation method and residual stress data. However, vessel layered structure and residual stress have large impact on stress/strain calculations and should be included in the models. In this study, intravascular optical coherence tomography (OCT) data of coronary plaques from 10 patients were acquired and segmented to obtain the three-layer vessel structure using an in-house automatic segmentation algorithm. Multi- and single-layer 3D thin-slice biomechanical plaque models with and without residual stress were constructed to assess the impact of residual stress on stress/strain calculations. Our results showed that residual stress led to a more uniform stress distribution across the vessel wall, with considerable plaque stress/strain decrease on inner wall and increase on vessel out-wall. Multi-layer model with residual stress inclusion reduced inner wall maximum and mean plaque stresses by 38.57% and 59.70%, and increased out-wall maximum and mean plaque stresses by 572.84% and 432.03%. These findings demonstrated the importance of multi-layer modeling with residual stress for more accurate plaque stress/strain calculations, which will have great impact in plaque cap stress calculation and plaque rupture risk assessment. Further large-scale studies are needed to validate our findings.

Product quality recognition and its industrial application based on lightweight machine learning

Quality management plays a crucial role in manufacturing industry as it directly impacts product quality and customer satisfaction. Traditional manufacturing industries have begun incorporating deep learning models to manage production quality. However, existing studies often focus on algorithmic models that require high computing power, while practical industrial applications often suffer from a lack of labelled data. This article proposes a novel and lightweight machine-learning-based quality detection model to address this issue. The proposed model tackles quality detection by efficiently detecting the local maxima of the scale space in input images using a Hessian matrix detection method, eliminating the need for continuous computation of multilayer Gaussian difference images. Additionally, image processing techniques are employed to augment the training data, enabling the model to achieve high accuracy even with limited datasets. Experimental results demonstrate that the proposed model outperforms alternative methods in terms of both accuracy and efficiency.

Enhancing the Efficacy of Radiomics-Based Prediction of Fuhrman Pathological Grading in Renal Clear Cell Carcinoma Using Multilayer Spiral CT Imaging.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma (RCC). Conventional pathological methods of Fuhrman pathological grading system have limitations. This study aims to investigate the efficacy of radiomics-based multilayer spiral computed tomography (CT) imaging of Fuhrman pathological grading in ccRCC. A retrospective analysis was conducted on the clinical data of ccRCC patients admitted in our hospital from March 2023 to March 2024. The patients were classified as low-grade (Fuhrman pathological grades I and II) or high-grade (Fuhrman pathological grades III and IV). Statistical methods, including correlation analysis, receiver operating characteristic (ROC) curves and construction of a joint predictive model, were utilised to assess the predictive value of these imaging omics indicators for Fuhrman pathological grading in ccRCC. The primary outcome assessment parameter in this study was the predictive value of these imaging omics indicators for Fuhrman pathological grading in ccRCC. The clinical data from 101 ccRCC patients were examined, with 56 cases classified as low-grade and 45 cases as high-grade. The grey-level co-occurrence matrix (GLCM) features between low and high Fuhrman grading groups, including contrast (0.24 ± 0.08 vs. 0.33 ± 0.09), energy (0.73 ± 0.05 vs. 0.67 ± 0.06) and homogeneity (0.63 ± 0.05 vs. 0.57 ± 0.05), showed notable distinctions (p < 0.001). The CT imaging characteristics between low and high Fuhrman grading groups, including enhancement homogeneity (0.34 ± 0.08 vs. 0.26 ± 0.08) and washout half-time (28.57 ± 4.35 vs. 34.72 ± 5.62) demonstrated a substantial variation between the groups (p < 0.001). The enhancement homogeneity (r = 0.476), washout half-time (r = -0.519), contrast (r = 0.454), energy (r = -0.453) and homogeneity (r = -0.541) showed significant correlations with Fuhrman pathological grading. The predictive value of these features was evident, with a combined imaging genomics model exhibiting an area under the curve of 0.929. This study demonstrated the potential of radiomics-based prediction using multilayer spiral CT imaging for accurately predicting Fuhrman pathological grading in ccRCC.

An Accurate Calibration Method for Multilayer Refractive Imaging in Underwater 3-D Scanning

An Accurate Calibration Method for Multilayer Refractive Imaging in Underwater 3-D Scanning

Depth of field extended integral imaging based on multi-depth fitting fusion

The traditional lens-based imaging display suffers from narrow depth of field (DoF), which is one of the primary drawbacks to restrict the commercial application of integral imaging (InIm). The limitation of DoF exists because locations of the reference plane and reconstructed 3D imagery are not identical. Herein, a DoF-extended method of rendering the elemental image array (EIA) based on multi-depth fitting fusion is proposed. In this method, depth stratification across the desired viewing depth range is implemented with depth maps for accurate extracting depth information and realizing high registration accuracy for image fusion, and then the multilayer parallax images (PIs) of different depth layers are obtained. Furthermore, an adaptive depth-fusion coding is proposed to establish effective iteration of depth-feature fitting for rendering the fusion EIA with the multilayer PIs, which uses the adaptive Gaussian-based weight matrix and the least squares method. As a result, the fusion EIA with multi-depth information, namely the iteratively weighted least squares solution of the EIA, is rendered to overcome the DoF limitation for InIm. The simulation and experimental presented 3D images with extended viewing depth range are demonstrated, verifying the feasibility of the proposed method.

Flow-induced oscillations of pitching swept wings: stability boundary, vortex dynamics and force partitioning

We study experimentally the aeroelastic instability boundaries and three-dimensional vortex dynamics of pitching swept wings, with the sweep angle ranging from 0 $^\circ$ to 25 $^\circ$ . The structural dynamics of the wings are simulated using a cyber-physical control system. With a constant flow speed, a prescribed high inertia and a small structural damping, we show that the system undergoes a subcritical Hopf bifurcation to large-amplitude limit-cycle oscillations (LCOs) for all the sweep angles. The onset of LCOs depends largely on the static characteristics of the wing. The saddle-node point is found to change non-monotonically with the sweep angle, which we attribute to the non-monotonic power transfer between the ambient fluid and the elastic mount. An optimal sweep angle is observed to enhance the power extraction performance and thus promote LCOs and destabilize the aeroelastic system. The frequency response of the system reveals a structural-hydrodynamic oscillation mode for wings with relatively high sweep angles. Force, moment and three-dimensional flow structures measured using multi-layer stereoscopic particle image velocimetry are analysed to explain the differences in power extraction for different swept wings. Finally, we employ a physics-based force and moment partitioning method to correlate quantitatively the three-dimensional vortex dynamics with the resultant unsteady aerodynamic moment.

Deep Learning-Based Metasurface Design for Smart Cooling of Spacecraft

A reconfigurable metasurface constitutes an important block of future adaptive and smart nanophotonic applications, such as adaptive cooling in spacecraft. In this paper, we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using a convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model material properties as image maps. We avoid the dimensionality mismatch problem using the operating wavelength as an input to the network. As a case study, we model the response of a reconfigurable absorber that employs the phase transition of vanadium dioxide in the mid-infrared spectrum. The feed-forward model is used as a surrogate model and is subsequently employed within a pattern search optimization process to design a passive adaptive cooling surface leveraging the phase transition of vanadium dioxide. The results indicate that our model delivers an accurate prediction of the metasurface response using a relatively small training dataset. The proposed patterned vanadium dioxide metasurface achieved a 28% saving in coating thickness compared to the literature while maintaining reasonable emissivity contrast at 0.43. Moreover, our design approach was able to overcome the non-uniqueness problem by generating multiple patterns that satisfy the design objectives. The proposed adaptive metasurface can potentially serve as a core block for passive spacecraft cooling applications. We also believe that our design approach can be extended to cover a wider range of applications.

A Web-Based Platform for 3D Visualization of Multimodal Imaging Data in Cultural Heritage Asset Documentation

Complex demands in the field of cultural heritage preservation often require a multidisciplinary approach and substantial volumes of multimodal data integration and management. The conventional approach to tackling these issues revolves around using different H-BIM (historical building information model) solutions. This paper presents a prototype for a web platform that moves closer to the idea of a digital twin for physical cultural assets. Based on a light development framework, it is designed for online open access and features a versatile custom 3D viewer for intuitive interaction with the presented data. The concept requires a workflow similar to the video-game industry’s 3D asset optimization to generate highly detailed 3D models and to facilitate the display of multilayered imaging data. The technological stack features a minimal MVC architecture framework and front-end stylesheets. It is designed to be independent of specific databases, enhancing portability for potential future open-source releases. Moreover, the platform employs WebGL libraries to create a dynamic 3D environment interaction. The capabilities of the web platform were tested in a case study regarding the documentation of an important 17th-century church in Romania. Further developments and current limitations of the platform are also discussed.