Image Components Research Articles

Efficient simultaneous migration of primary and free-surface related multiples using reformulated two-way wave-equation depth extrapolation scheme

The migration of free-surface related multiples can enhance subsurface illumination and improve overall imaging quality. However, this process encounters two main challenges: crosstalk artefacts resulting from the cross-correlation of non-reflection-related wavefields, and the increased computational burden of imaging different orders of multiples. We propose a novel method that simultaneously and efficiently migrates both primary and multiple reflections while mitigating crosstalk artefacts. The method employs a reformulated two-way wave-equation depth extrapolation scheme that simplifies up/down wavefield separation through straightforward summation and subtraction operations at each depth step. Two innovative algorithms are integrated into this scheme: a generalized up/down separation algorithm, and a simultaneous migration algorithm of primary and free-surface-related multiples. The up/down separation algorithm efficiently separates the up- and down-going wavefields into primary wavefield and multiple reflections of various orders at the measurement surface. The simultaneous migration algorithm then pairs these components as two-way quantities, allowing for efficient depth extrapolation using a unified propagator, followed by effective decomposition into corresponding one-way components for imaging. Numerical experiments conducted on synthetic models, including a two-dimensional two-layer model and the Sigsbee 2B model, as well as on real seismic data from a gas hydrates bearing zone, demonstrate that the proposed method simultaneously migrate both primary and multiple reflections with reduced crosstalk artefacts and limited computational overhead.

A theoretical analysis of the logging-while-drilling dipole acoustic reflection measurement

Post-drilling wireline acoustic single-well imaging technology can now detect geological structures tens of meters away from boreholes. Further development of this single-well imaging technology in the logging-while-drilling (LWD) environment will have significant values in real-time applications such as geosteering and reservoir navigation. Based on the wireline imaging application, we propose a new method for the LWD application. In wireline imaging, the four-component (4C) dipole acoustic data are azimuthally rotated to scan the reflectors around the borehole. In LWD, azimuthal scanning is achieved by drilling rotation such that the 4C dipole system in the wireline is replaced by a one-dipole-source and two-receiver LWD system, where the two receivers are mounted on opposite sides of the drill collar. For the LWD application, we first developed the theory for LWD dipole shear-wave reflection imaging and validated the theory using 3D finite-difference waveform modeling. Using the analytical solution, we analyzed the far-field radiation directivity of an acoustic LWD dipole source and the effect of drilling rotation on the shear-wave reflection imaging using the LWD acoustic system. The LWD analysis results show that, for fast formations, the SH-wave is the dominant component for imaging, whereas for slow formations, the P-wave becomes important and can be used for imaging. Our results also indicate that the reflection data acquired by the system are affected by the speed of drilling rotation. The take-off azimuth at the wave radiation may be different from the incident azimuth at the wave reception. Knowing the rotation speed, this azimuth difference can be corrected. A further advantage of using the oppositely mounted receivers is that the reflected wave arrives earlier (later) at the front (back)-side receiver; thus, the arrival time difference between the receivers can be used to eliminate the 180°-azimuth ambiguity of dipole acoustic imaging. For reflection imaging, using the proposed LWD system configuration, we tested its azimuth sensitivity and validated its 180°-ambiguity solution using synthetic LWD and field wireline dipole data. The results of this work, therefore, provide a theoretical foundation for the development of the LWD acoustic reflection imaging system.

WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.

Nitric Oxide-Photodelivering Materials with Multiple Functionalities: From Rational Design to Therapeutic Applications.

The achievement of materials that are able to release therapeutic agents under the control of light stimuli to improve therapeutic efficacy is a significant challenge in health care. Nitric oxide (NO) is one of the most studied molecules in the fascinating realm of biomedical sciences, not only for its crucial role as a gaseous signaling molecule in the human body but also for its great potential as an unconventional therapeutic in a variety of diseases including cancer, bacterial and viral infections, and neurodegeneration. Handling difficulties due to its gaseous nature, reduced region of action due to its short half-life, and strict dependence of the biological effects on its concentration and generation site are critical questions to be solved for appropriate therapeutic uses of NO. Light-activatable NO precursors, namely, NO photodonors (NOPDs), address the above issues since they are stable in the dark and permit in a noninvasive fashion the remote-controlled delivery of NO on demand with great spatiotemporal precision. Engineering biocompatible materials with NOPDs and their combination with additional imaging, therapeutic, and phototherapeutic components leads to intriguing light-responsive multifunctional constructs exhibiting promising potential for biomedical applications. This contribution illustrates the most significant progress made over the last five years in achieving engineered materials including nanoparticles, gels, and thin films, sharing the common feature to deliver NO under the exclusive control of the biocompatible visible/near infrared light inputs. We will highlight the logical design behind the fabrication of these systems, illustrating the potential therapeutic applications with particular emphasis on cancer and bacterial infections.

Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.

Tunable Imaging Distance Focusing Module Directly Driven by a Thin‐plate Piezoelectric Actuator

AbstractWith the advancement of imaging technology, the performance of optical focusing modules becomes crucial for determining imaging quality. Conventional focusing modules frequently face challenges of excessive bulk and weight, complicating the integrated design and limiting application fields. Herein, this study presents a piezo‐actuated optical focusing module (POFM) and the construction strategy to alleviate these issues. A novel radial‐bending resonant‐type piezoelectric actuator (RB‐RPA) is developed to directly drive the POFM. The RB‐RPA, which features a hollow structure (27 × 27 × 7 mm3), utilizes both radial and bending vibration modes of a thin metal plate. It achieves a speed of 122.93 mm s−1, a thrust force of 0.14 N, and a displacement resolution of 0.52 µm. Employing the proposed construction strategy, RB‐RPA is integrated with imaging components to fabricate a POFM prototype. Furthermore, the micrometer‐level displacement resolution of POFM facilitates optical focusing on imaging targets, allowing the acquisition of sharp images within 70 ms. It achieves an image resolution of 119.57 lp mm−1 within a 42 mm segment of the captured image. Finally, a QT‐based interface for the POFM, enables real‐time image capture and sharpness evaluation, thereby enhancing imaging efficiency and integration. This work provides a potential candidate for next‐generation imaging devices.

Decoupling Deep Learning for Enhanced Image Recognition Interpretability

The quest for enhancing the interpretability of neural networks has become a prominent focus in recent research endeavors. Prototype-based neural networks have emerged as a promising avenue for imbuing models with interpretability by gauging the similarity between image components and category prototypes to inform decision-making. However, these networks face challenges as they share similarity activations during both the inference and explanation processes, creating a tradeoff between accuracy and interpretability. To address this issue and ensure that a network achieves high accuracy and robust interpretability in the classification process, this article introduces a groundbreaking prototype-based neural network termed the “Decoupling Prototypical Network” (DProtoNet). This novel architecture comprises encoder, inference, and interpretation modules. In the encoder module, we introduce decoupling feature masks to facilitate the generation of feature vectors and prototypes, enhancing the generalization capabilities of the model. The inference module leverages these feature vectors and prototypes to make predictions based on similarity comparisons, thereby preserving an interpretable inference structure. Meanwhile, the interpretation module advances the field by presenting a novel approach: a “multiple dynamic masks decoder” that replaces conventional upsampling similarity activations. This decoder operates by perturbing images with mask vectors of varying sizes and learning saliency maps through consistent activation. This methodology offers a precise and innovative means of interpreting prototype-based networks. DProtoNet effectively separates the inference and explanation components within prototype-based networks. By eliminating the constraints imposed by shared similarity activations during the inference and explanation phases, our approach concurrently elevates accuracy and interpretability. Experimental evaluations on diverse public natural datasets, including CUB-200-2011, Stanford Cars, and medical datasets like RSNA and iChallenge-PM, corroborate the substantial enhancements achieved by our method compared to previous state-of-the-art approaches. Furthermore, ablation studies are conducted to provide additional evidence of the effectiveness of our proposed components.

Effects of brand visual identity on consumer attitude ：A systematic literature review

Brand visual identity is a critical component of brand image and impacts consumers' brand perception and attitude. Although scholars and practitioners have increasingly researched brand visual identity, most focus on brand communication and corporate management perspectives, necessitating a systematic review of consumer attitudes based on brand visual identity elements. This study aims to review the impact of brand visual identity on consumer attitudes and provide directions for future research. We conducted a systematic search using PRISMA guidelines, reviewed relevant articles published in four databases (Web of Sciences, ProQuest, Scopus, and Elsevier) in the past two decades (2004-2024), and obtained 559 articles in May 2024. We conducted a thorough peer-review of both theoretical and empirical journal articles, followed by a scoping review. Thirty-four studies were eligible, analysing consumers' perceptions of various core elements of brand visual identity (BVI) (such as brand logo, colour, name, typography, and font), as well as the impact of brand visual identity on consumer attitudes and the practical implications of brand management. The results show that the consumption of visual identification elements affects consumers' perceived brand quality, brand personality, brand satisfaction, loyalty, favourability, and so on., as well as purchase intentions and social attitudes. In summary, the findings indicate that brand visual identity elements impact consumer perceptions and attitudes. However, despite some progress, further research is necessary to arrive at more definitive and robust conclusions.

Consumer-based brand equity of US domestic and global wine tourism destinations from the American market perspective

Purpose This study aims to measure and compare consumer-based brand equity (CBBE) of several US and global wine tourism destinations from an American market point of view. Design/methodology/approach An online randomized experimental design was used to assign respondents to different wine tourism destination contexts and measure their perceptions and intentions. A structured survey was designed with CBBE scale items related to the most commonly studied components of CBBE, namely, familiarity, perceived quality, image, consumer value, brand value and brand loyalty. Findings The study revealed that respondents were unfamiliar with multi-regional and global wine-growing destinations and their wines, implying that wine tourists belong to different market segments that seek different experiences. Among the US wine tourism destinations, Napa Valley is the destination with the strongest CBBE, with the highest overall ratings in all five CBBE components, on average. In terms of country-level wine tourism destinations, the US has the strongest CBBE, with the highest overall ratings in familiarity and brand loyalty. Research limitations/implications Wine tourism destination CBBE is shown to include components that may not relate exclusively to wine and variations in perceptions concerning global wine-growing regions may be associated with consumer familiarity. Future studies may include respondents from different countries to determine the generalizability of the current findings. Originality/value To the best of the authors’ knowledge, the current study is the first to assess consumer-based brand equity of wine tourism destinations in the US and globally from a US resident perspective.

Complex-valued image reconstruction for compressed sensing MRI using hierarchical constraint

In Magnetic Resonance Imaging (MRI), the sequential acquisition of raw complex-valued image data in Fourier space, also known as k-space, results in extended examination times. To speed up the MRI scans, k-space data are usually undersampled and processed using numerical techniques such as compressed sensing (CS). While the majority of CS-MRI algorithms primarily focus on magnitude images due to their significant diagnostic value, the phase components of complex-valued MRI images also hold substantial importance for clinical diagnosis, including neurodegenerative diseases. In this work, complex-valued MRI reconstruction is studied with a focus on the simultaneous reconstruction of both magnitude and phase images. The proposed algorithm is based on the nonsubsampled contourlet transform (NSCT) technique, which offers shift invariance in images. Instead of directly transforming the complex-valued image into the NSCT domain, we introduce a wavelet transform within the NSCT domain, reducing the size of the sparsity of coefficients. This two-level hierarchical constraint (HC) enforces sparse representation of complex-valued images for CS-MRI implementation. The proposed HC is seamlessly integrated into a proximal algorithm simultaneously. Additionally, to effectively minimize the artifacts caused by sub-sampling, thresholds related to different sub-bands in the HC are applied through an alternating optimization process. Experimental results show that the novel method outperforms existing CS-MRI techniques in phase-regularized complex-valued image reconstructions.

Zirconium- 89 Labeled Antibody K1-70 for PET Imaging of Thyroid-stimulating Hormone Receptor Expression in Thyroid Cancer.

Thyroid-stimulating hormone receptor (TSHR) is a G-protein coupled receptor that is highly expressed on benign and malignant thyroid tissues. TSHR binding and activation has long been a component of thyroid cancer molecular imaging and radiotherapy, by promoting expression of the sodium-iodide symporter (NIS) and incorporation of I-131 into thyroid hormones. Here, we report the radiosynthesis and preclinical evaluation of a Zirconium-89 (89Zr) labeled TSHR antibody to serve as a positron emission tomography (PET) diagnostic correlate for therapeutic agents targeting TSHR without reliance on NIS. TSHR human monoclonal antibody K1-70 was conjugated to chelator desferrioxamine-p-benzyl-isothiocyanate, followed by labeling with Zr-89, yielding the radiotracer 89Zr-DFO-TSHR-Ab. The in vitro cellar uptake and binding affinity of 89Zr-DFO-TSHR-Ab were analyzed in three new TSHR stable overexpressing tumor cell lines and their corresponding wild types (WT) with low or no TSHR expression. 89Zr-DFO-TSHR-Ab PET/CT imaging of TSHR expression was evaluated in tumor mouse models bearing one TSHR-positive tumor and other negative control with or without the coinjection of antibody K1-70, and then verified by radiotracer biodistribution study and tumor immunohistochemistry (IHC). The conjugate DFO-TSHR-Ab was labeled with Zr-89 at 37°C for 60min and purified by PD-10 column in radiochemical yields of 68.8 ± 9.9%, radiochemical purities of 98.7 ± 0.8%, and specific activities of 19.1 ± 2.7mCi/mg (n = 5). In vitro cell studies showed 89Zr-DFO-TSHR-Ab had significantly high uptake on TSHR expressing tumor cells with nanomolar affinity and high potency. Preclinical PET/CT imaging revealed that 89Zr-DFO-TSHR-Ab selectively detected TSHR expressing thyroid tumors and displayed improved in vivo performance with the coinjection of unlabeled TSHR antibody K1-70 leading to higher uptake in TSHR expressing tumors than parental WT tumors and physiologic tissues; this observation was confirmed by the biodistribution and immunostaining analyses. We synthesized 89Zr-labeled antibody K1-70 as a new radiopharmaceutical for PET imaging of TSHR. 89Zr-DFO-TSHR-Ab has high radioactive uptake and retention in TSHR expressing tumors and cleared quickly from most background tissues in mouse models. Our study demonstrated that 89Zr-DFO-TSHR-Ab has the potential for PET imaging of TSHR-positive thyroid cancer and monitoring TSHR-targeted therapy.

Identification of weeds in cotton fields at various growth stages using color feature techniques

Weeds affect the growth and yield of cotton (Gossypium hirsutum), which is the most prevalent commercially viable non-food crop. Accurate differentiation between weeds and cotton plants is crucial for implementing precise weed-control strategies, optimizing crop yields, and minimizing herbicide usage. In this study, we aimed to investigate the effectiveness of using color feature techniques combined with Otsu's method (OTSU) to distinguish weeds from cotton plants at various growth stages. The results revealed that the image segmentation approach achieved a recognition rate of 74.17% during the second growth stage. In the third growth stage, the weed identification process primarily targeted lambsquarters (Chenopodium album). The standard deviation (SD) difference between the red (R), green (G), and blue (B) components of the plant images revealed that the difference between the SD values of R and B was <5, which could serve as an effective threshold for identifying lambsquarters. Additionally, the recognition rates for cotton and lambsquarters were 71.40% and 92.90%, respectively, and the overall recognition rate reached 82.10% during the identification process. The results of this study have practical implications for weed management practices, such as targeted herbicide application, mitigation of environmental impacts, and contribution to higher crop yields. Future research will focus on refining these methods by exploring advanced image processing techniques that integrate deep learning for automatic feature extraction, which will be evaluated for handling complex agronomic scenarios, such as leaf occlusion, varying growth stages, and image noise.

The JWST UNCOVER Treasury Survey: Ultradeep NIRSpec and NIRCam Observations before the Epoch of Reionization

In this paper we describe the survey design for the Ultradeep NIRSpec and NIRCam Observations before the Epoch of Reionization (UNCOVER) Cycle 1 JWST Treasury program, which executed its early imaging component in 2022 November. The UNCOVER survey includes ultradeep (∼29–30AB) imaging of ∼45 arcmin2 on and around the well-studied A2744 galaxy cluster at z = 0.308 and will follow up ∼500 galaxies with extremely deep low-resolution spectroscopy with the NIRSpec/PRISM during the summer of 2023, with repeat visits in summer 2024. We describe the science goals, survey design, target selection, and planned data releases. We also present and characterize the depths of the first NIRCam imaging mosaic, highlighting previously unparalleled resolved and ultradeep 2–4 μm imaging of known objects in the field. The UNCOVER primary NIRCam mosaic spans 28.8 arcmin2 in seven filters (F115W, F150W, F200W, F277W, F356W, F410M, and F444W) and 16.8 arcmin2 in our NIRISS parallel (F115W, F150W, F200W, F356W, and F444W). To maximize early community use of the Treasury data set, we publicly release the full reduced mosaics of public JWST imaging including 45 arcmin2 NIRCam and 17 arcmin2 NIRISS mosaics on and around the A2744 cluster, including the Hubble Frontier Field primary and parallel footprints.

Weakly‐supervised structural component segmentation via scribble annotations

AbstractSegmentation of structural components in infrastructure inspection images is crucial for automated and accurate condition assessment. While deep neural networks hold great potential for this task, existing methods typically require fully annotated ground truth masks, which are time‐consuming and labor‐intensive to create. This paper introduces Scribble‐supervised Structural Component Segmentation Network (ScribCompNet), the first weakly‐supervised method requiring only scribble annotations for multiclass structural component segmentation. ScribCompNet features a dual‐branch architecture with higher‐resolution refinement to enhance fine detail detection. It extends supervision from labeled to unlabeled pixels through a combined objective function, incorporating scribble annotation, dynamic pseudo label, semantic context enhancement, and scale‐adaptive harmony losses. Experimental results show that ScribCompNet outperforms other scribble‐supervised methods and most fully‐supervised counterparts, achieving 90.19% mean intersection over union (mIoU) with an 80% reduction in labeling time. Further evaluations confirm the effectiveness of the novel designs and robust performance, even with lower‐quality scribble annotations.

Spinal Schwannoma Classification Based on the Presumed Origin With Preoperative Magnetic Resonance Images.

Classification guides the surgical approach and predicts prognosis. However, existing classifications of spinal schwannomas often result in a high 'unclassified' rate. Here, we aim to develop a new comprehensive classification for spinal schwannomas based on their presumed origin. We compared the new classification with the existing classifications regarding the rate of 'unclassified'. Finally, we assessed the surgical strategies, outcomes, and complications according to each type of the new classification. A new classification with 9 types was created by analyzing the anatomy of spinal nerves and the origin of significant tumor portions and cystic components in preoperative magnetic resonance images. A total of 482 patients with spinal schwannomas were analyzed to compare our new classification with the existing classifications. We defined 'unclassified' as the inability to classify a patient with spinal schwannoma using the classification criteria. Surgical approaches and outcomes were also aligned with our new classification. Our classification uniquely reported no 'unclassified' cases, indicating full applicability. Also, the classification has demonstrated usefulness in predicting the surgical outcome with the approach planned. Gross total removal rates reached 88.0% overall, with type 1 and type 2 tumors at 95.3% and 96.0% respectively. The approach varied with tumor type, with laminectomy predominantly used for types 1, 2, and 9, and facetectomy with posterior fixation used for type 3 tumors. The new classification for spinal schwannomas based on presumed origin is applicable to all spinal schwannomas. It could help plan a surgical approach and predict its outcome, compared with existing classifications.

Replicating biological 3D root and hyphal networks in transparent glass chips

Replicating the complex 3D microvascular architectures found in biological systems is a critical challenge in tissue engineering and other fields requiring efficient mass transport. Conventional microfabrication techniques often face limitations in creating extensive hierarchical networks, especially within bulk materials. Here, we report a versatile bioinspired approach to generate optimized 3D microvascular networks within transparent glass matrix by transcribing the natural growth patterns of plants and fungi. Plant seeds or fungal spores are first cultivated on nanoparticle-based culture media. Subsequent heat treatment removes the biological species while sintering the surrounding compound into a solidified chip with replica root/hyphal architectures as open microchannels. A diverse range of architectures, including the hierarchical branching of plant roots and the intricate networks formed by fungal hyphae, can be faithfully replicated. The resultant glass microvascular networks exhibit high chemical and thermal stability, enabling applications under harsh conditions. Fluid flow experiments validate the functionalities of the fabricated channels. By co-cultivating plants and fungi, hierarchical multi-scale architectures mimicking natural vascular systems are achieved. This bioinspired manufacturing technique leverages autonomous biological growth for architectural optimization, offering a complementary approach to existing microfabrication methods. The transparent nature of the glass chips allows for direct optical inspection, potentially facilitating integration with imaging components. This versatile platform holds promise for various engineering applications, such as microreactors, heat exchangers, and advanced filtration systems.

A robust color image encryption scheme with complex whirl wind spiral chaotic system and quadrant-wise pixel permutation

This paper presents a novel encryption technique that uses a unique chaotic circuit design called as 3D Complex Whirl Wind Spiral chaotic system (CWWS). The major goal of this novel approach is to create an efficient 3D chaotic systems with increased randomness and multistability, specifically designed to encrypt multimedia data. The design incorporates the sine function sin(x) to introduce complexity and unpredictability in the chaotic circuit. The dynamic behaviour of the proposed scheme’s chaotic system is thoroughly evaluated using a variety of analyses, including KY dimension, dissipativity, Lyapunov exponent spectra, and bifurcation diagrams. There are two key stages to the encryption process: diffusion and confusion. The diffusion process is strengthened by the smooth integration of quadrant-wise pixel permutation (QWPP) algorithms, which eliminate correlations between neighbouring pixels. Following that, the image components are concealed using the chaotic sequence that was generated from the 3D CWWS chaotic system. The complete encrypted image is then created by combining these encrypted components. The simulation results of the proposed strategy are thoroughly investigated using statistical analysis, differential analysis, and brute force attacks. The system has optimal key space, entropy, UACI, and NPCR metric values of 2400, 7.99, 0.334, and 0.996, respectively. Furthermore, the experimental findings show robust resistance to statistical, differential, and brute force attacks for a single round of iteration.

Face shape transfer via semantic warping

Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful, which has many potential applications. Existing methods 1) operate on the pre-defined facial landmarks, leading to artifacts and distortions due to the limited number of landmarks, 2) synthesize new faces based on segmentation masks or sketches, causing generated faces to look dissatisfied due to the losses of skin details and difficulties in dealing with hair and background blurring, and 3) project the positions of the deformed feature points from the 3D face model to the 2D image, making the results unrealistic because of the misalignment between feature points. In this paper, we propose a novel method named face shape transfer (FST) via semantic warping, which can transfer both the overall face and individual components (e.g., eyes, nose, and mouth) of a reference image to the source image. To achieve controllability at the component level, we introduce five encoding networks, which are designed to learn feature embedding specific to different face components. To effectively exploit the features obtained from semantic parsing maps at different scales, we employ a straightforward method of directly connecting all layers within the global dense network. This direct connection facilitates maximum information flow between layers, efficiently utilizing diverse scale semantic parsing information. To avoid deformation artifacts, we introduce a spatial transformer network, allowing the network to handle different types of semantic warping effectively. To facilitate extensive evaluation, we construct a large-scale high-resolution face dataset, which contains 14,000 images with a resolution of 1024 × 1024. Superior performance of our method is demonstrated by qualitative and quantitative experiments on the benchmark dataset.

A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress

A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress

High-Resolution Power Doppler Using Null Subtraction Imaging.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.