arXiv subjects

Stefano Sfarra

Publications and source records attributed to Stefano Sfarra.

12 recordsLinked to original sources

arXiv2026

Quantitative Infrared Thermographic Assessment of Hand Cooling Dynamics During Controlled Contact with Metal Plates

This study investigates the spatiotemporal thermal response of human hands during controlled contact cooling using short wave infrared (SWIR), mid wave infrared (MWIR), and long wave infrared (LWIR) thermography. Three participants simultaneously placed one hand on a cooling metal plate and the contralateral hand on a reference plate maintained near room temperature. Temperature evolution was analyzed in five anatomical regions, including the distal finger, proximal finger, vessel associated region, non vessel region, and forearm. Quantitative metrics, including temperature variation, bilateral temperature difference, initial cooling rate, and frequency-domain amplitude, were extracted from the thermal image sequences. The results showed that the finger regions exhibited the largest temperature reductions and highest cooling rates, indicating greater sensitivity to thermal stimulation than the dorsal hand and forearm. MWIR and LWIR measurements revealed highly consistent cooling dynamics, while LWIR imaging provided enhanced thermal contrast and sensitivity. Frequency-domain analysis demonstrated that the dominant thermal response was concentrated in the low frequency range below 0.05 Hz. Furthermore, pixel-wise cooling rate maps highlighted substantial spatial heterogeneity across the hand surface. Numerical bioheat simulations confirmed that blood perfusion and skin plate contact conductance are key factors governing the cooling response. These findings demonstrate the potential of dynamic infrared thermography as a non-contact tool for assessing peripheral thermoregulation and vascular function during controlled cooling experiments.

physics.app-ph↗

arXiv2026

Vision-Language Based Expert Reporting for Painting Authentication and Defect Detection

Authenticity and condition assessment are central to conservation decision-making, yet interpretation and reporting of thermographic output remain largely bespoke and expert-dependent, complicating comparison across collections and limiting systematic integration into conservation documentation. Pulsed Active Infrared Thermography (AIRT) is sensitive to subsurface features such as material heterogeneity, voids, and past interventions; however, its broader adoption is constrained by artifact misinterpretation, inter-laboratory variability, and the absence of standardized, explainable reporting frameworks. Although multi-modal thermographic processing techniques are established, their integration with structured natural-language interpretation has not been explored in cultural heritage. A fully automated thermography-vision-language model (VLM) framework is presented. It combines multi-modal AIRT analysis with modality-aware textual reporting, without human intervention during inference. Thermal sequences are processed using Principal Component Thermography (PCT), Thermographic Signal Reconstruction (TSR), and Pulsed Phase Thermography (PPT), and the resulting anomaly masks are fused into a consensus segmentation that emphasizes regions supported by multiple thermal indicators while mitigating boundary artifacts. The fused evidence is provided to a VLM, which generates structured reports describing the location of the anomaly, thermal behavior, and plausible physical interpretations while explicitly acknowledging the uncertainty and diagnostic limitations. Evaluation on two marquetries demonstrates consistent anomaly detection and stable structured interpretations, indicating reproducibility and generalizability across samples.

cs.CV↗

arXiv2026

Towards Cognitive Defect Analysis in Active Infrared Thermography with Vision-Text Cues

Active infrared thermography (AIRT) is currently witnessing a surge of artificial intelligence (AI) methodologies being deployed for automated subsurface defect analysis of high performance carbon fiber-reinforced polymers (CFRP). Deploying AI-based AIRT methodologies for inspecting CFRPs requires the creation of time consuming and expensive datasets of CFRP inspection sequences to train neural networks. To address this challenge, this work introduces a novel language-guided framework for cognitive defect analysis in CFRPs using AIRT and vision-language models (VLMs). Unlike conventional learning-based approaches, the proposed framework does not require developing training datasets for extensive training of defect detectors, instead it relies solely on pretrained multimodal VLM encoders coupled with a lightweight adapter to enable generative zero-shot understanding and localization of subsurface defects. By leveraging pretrained multimodal encoders, the proposed system enables generative zero-shot understanding of thermographic patterns and automatic detection of subsurface defects. Given the domain gap between thermographic data and natural images used to train VLMs, an AIRT-VLM Adapter is proposed to enhance the visibility of defects while aligning the thermographic domain with the learned representations of VLMs. The proposed framework is validated using three representative VLMs; specifically, GroundingDINO, Qwen-VL-Chat, and CogVLM. Validation is performed on 25 CFRP inspection sequences with impacts introduced at different energy levels, reflecting realistic defects encountered in industrial scenarios. Experimental results demonstrate that the AIRT-VLM adapter achieves signal-to-noise ratio (SNR) gains exceeding 10 dB compared with conventional thermographic dimensionality-reduction methods, while enabling zero-shot defect detection with intersection-over-union values reaching 70%.

cs.CV↗

arXiv2026

Field-material coupled neural network: A novel prior-free and data-free inverse problem solver for extracting complex dielectric constant in terahertz band

Accurate extraction of the complex dielectric constant in the terahertz (THz) band is essential for material characterization and non-destructive evaluation yet remains challenging due to the ill-posed nature of electromagnetic inverse problems and the limited availability of reliable reference data. In this work, a field-material couple neural network (FMCNN) is proposed to retrieve the complex dielectric constant directly from THz measurements. The FMCNN consists of a field neural network and a material neural network that are strongly coupled through the frequency-domain Maxwell equations in the form of a Helmholtz equation, with the governing physics enforced by partial differential equation (PDE) and boundary condition constraints. This formulation enables prior-free and data-free inversion, requiring only measured test data as input. The extracted dielectric constants are validated by comparison with results from a one-dimensional normal-incidence model and the Drude-Lorentz model, showing good agreement over a broad frequency range, particularly above 0.2 THz. These results demonstrate that the FMCNN provides a physics-consistent and data-efficient approach for material parameter extraction in the THz band, offering an alternative to conventional model-based methods.

physics.app-ph↗

arXiv2026

Principal Component Analysis-Based Terahertz Self-Supervised Denoising and Deblurring Deep Neural Networks

Terahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional image processing techniques cannot simultaneously address both issues, and manual intervention is often required due to the unknown boundary between denoising and deblurring. To tackle this challenge, we propose a principal component analysis (PCA)-based THz self-supervised denoising and deblurring network (THz-SSDD). The network employs a Recorrupted-to-Recorrupted self-supervised learning strategy to capture the intrinsic features of noise by exploiting invariance under repeated corruption. PCA decomposition and reconstruction are then applied to restore images across both low and high frequencies. The performance of the THz-SSDD network was evaluated on four types of samples. Training requires only a small set of unlabeled noisy images, and testing across samples with different material properties and measurement modes demonstrates effective denoising and deblurring. Quantitative analysis further validates the network feasibility, showing improvements in image quality while preserving the physical characteristics of the original signals.

cs.CV↗

arXiv2025

Masked Sequence Autoencoding for Enhanced Defect Visualization in Active Infrared Thermography

Active infrared thermography (AIRT) became a crucial tool in aerospace non-destructive testing (NDT), enabling the detection of hidden defects and anomalies in materials by capturing thermal responses over time. In AIRT, autoencoders are widely used to enhance defect detection by reducing the dimensionality of thermal data and improving the signal-to-noise ratio. However, traditional AIRT autoencoders often struggle to disentangle subtle defect features from dominant background responses, leading to suboptimal defect analysis under varying material and inspection conditions. To overcome this challenge, this work proposes a Masked CNN-Attention Autoencoder (AIRT-Masked-CAAE) that integrates convolutional feature extraction with attention mechanisms to capture both local thermal patterns and global contextual dependencies. The AIRT-Masked-CAAE introduces a masked sequence autoencoding strategy, where the network learns to infer missing thermal responses from surrounding contextual cues, while suppressing background redundancy. In addition, the proposed masked sequence autoencoding approach enables training on only a subset of the thermal sequence, while providing generalizable latent representations and reducing training time by a factor of 30. The AIRT-Masked-CAAE framework was evaluated using specimens made of PVC, CFRP, and PLA. The results demonstrate that the AIRT-Masked-CAAE surpasses state-of-the-art AIRT autoencoders in terms of contrast, signal-to-noise ratio (SNR), and metrics based on neural networks.

eess.SP↗

arXiv2025

A review of cultural heritage inspection: Toward terahertz from mid-infrared region

This review explores non-invasive imaging (NII) methods covering the mid- and far-infrared to the terahertz spectral regions (up to approximately 1000 um) for the detection and analysis of cultural heritage artifacts. In the thermal infrared domain, where radiation follows Planck's law, the self-emission of materials reveals intrinsic properties and internal degradation. By contrast, in the near-infrared range, external illumination enhances surface details and pigment differentiation. Far-infrared and terahertz techniques, operating in both transmission and reflection modes, provide complementary insights by penetrating surface layers to uncover subsurface structures and concealed features. Integrating visible and infrared imaging further enriches diagnostic capabilities by correlating conventional visual assessments with spectral information. Beyond reviewing the wide applications of these NII techniques in cultural heritage research, this work also summarizes recent advances in signal processing, encompassing both hardware and software developments. In particular, deep learning has revolutionized the field by enabling automated classification, feature extraction, defect detection, and super-resolution imaging. Through supervised and unsupervised learning strategies, neural networks can reliably identify subtle anomalies and material variations indicative of past restorations or early stages of deterioration. In conclusion, the convergence of advanced spectral imaging, sophisticated signal processing, and deep neural networks offers a transformative pathway toward more accurate, efficient, and data-driven cultural heritage analysis, ultimately supporting more informed conservation and restoration decisions.

physics.optics↗

arXiv2025

Modeling Terahertz Propagation via Frequency-Domain Physics-Informed Neural Networks

Terahertz time-domain spectroscopy (THz-TDS) provides a non-invasive and label-free method for probing the internal structure and electromagnetic response of materials. Numerical simulation of THz-TDS can help understanding wave-matter interactions, guiding experimental design, and interpreting complex measurement data. However, existing simulation techniques face challenges in accurately modeling THz wave propagation with low computational cost. Additionally, conventional simulation solvers often require dense spatial-temporal discretization, which limits their applicability to large-scale and real-time scenarios. Simplified analytical models may neglect dispersion, multiple scattering, and boundary effects. To address these limitations, we establish a novel computational framework that integrates frequency-domain physics-informed neural networks (FD-PINNs) with less data-driven. To validate our proposed FD-PINNs, simulation results from finite-difference time-domain (FDTD) and time-domain (TD)-PINNs were used to compare with FD-PINNs. Finally, experimental results from THz-TDS systems were employed to further exhibit accurate reconstruction ability of FD-PINNs.

physics.optics↗

arXiv2025

Thermal diffusivity measurement based on evaporative cryocooling excitation: Theory and experiments

Photo-thermal methods for measuring thermal diffusivity inherently pose an ill-posed inverse problem, affected by factors such as sample thickness, heating or cooling time, and excitation energy. Measurement accuracy becomes particularly challenging under non-impulsive pulsed excitation when the observation timescale is comparable to the pulse duration. This is often due to poorly defined pulse shapes, broadened thermal responses, and the absence of clear boundary conditions, especially under significant interfacial temperature gradients where natural convection dominates. The classic Parker solution, while widely used, is physically unrealistic as it assumes adiabatic heat flux and shallow-region heat absorption. In this study, we prove that Parker's assumption is equivalent to the Dirac pulse boundary condition in mathematics. Then, we present comprehensive analytical solutions for thermal/cooling responses under Dirac and rectangular pulse excitations. By comparing with eigenfunction-based solutions for well-posed boundary conditions, we show that Parker's solution is only valid before the thermal peak. Through dimensionless processing, we further demonstrate that Parker's solution can be regarded as a limiting case of the rectangular pulse solution as the heating duration approaches zero. Furthermore, we propose a novel excitation approach, evaporative cryocooling, for thermal diffusivity measurement. This method offers a compact, low-cost, and easy-to-implement alternative to conventional excitation schemes. The theoretical model was further validated through comparison with experimental results.

physics.app-ph↗

arXiv2025

A novel IR-SRGAN assisted super-resolution evaluation of photothermal coherence tomography for impact damage in toughened thermoplastic CFRP laminates under room temperature and low temperature

Evaluating impact-induced damage in composite materials under varying temperature conditions is essential for ensuring structural integrity and reliable performance in aerospace, polar, and other extreme-environment applications. As matrix brittleness increases at low temperatures, damage mechanisms shift: impact events that produce only minor delaminations at ambient conditions can trigger extensive matrix cracking, fiber/matrix debonding, or interfacial failure under severe cold loads, thereby degrading residual strength and fatigue life. Precision detection and quantification of subsurface damage features (e.g., delamination area, crack morphology, interface separation) are critical for subsequent mechanical characterization and life prediction. In this study, infrared thermography (IRT) coupled with a newly developed frequency multiplexed photothermal correlation tomography (FM-PCT) is employed to capture three-dimensional subsurface damage signatures with depth resolution approaching that of X-ray micro-computed tomography. However, the inherent limitations of IRT, including restricted frame rate and lateral thermal diffusion, reduce spatial resolution and thus the accuracy of damage size measurement. To address this, we develop a new transfer learning-based infrared super-resolution generative adversarial network (IR-SRGAN) that enhances both lateral and depth-resolved imaging fidelity based on limited thermographic datasets.

physics.app-ph↗

arXiv2025

Thermal diffusivity characterization of impacted composites using evaporative cryocooling excitation and inverse physics-informed neural networks

The thermal diffusivity measurement of impacted composites using pulsed methods presents an ill-posed inverse problem influenced by multiple factors such as sample thickness, cooling duration, and excitation energy. In this study, a novel excitation method, evaporative cryocooling, was introduced for measuring the thermal diffusivity of tested samples. Compared to conventional excitation modalities, evaporative cryocooling excitation is compact, portable, and low cost. However, evaporative cryocooling cannot be considered a pulsed method due to its prolonged excitation duration. In general, it is difficult to measure thermal diffusivity based on non-impulsive pulsed excitation at times commensurate with the pulse duration, often due to ill-defined pulse shape and width and the subsequent potentially complicated thermal response which may be subject to diffusive broadening. To address this challenge, inverse physics-informed neural networks (IPINNs) were introduced in this work and integrated with an evaporative cryocooling method. The Parker method combined with a photothermal method was employed as a reference. To improve the accuracy of both IPINNs and Parker methods, terahertz time-domain spectroscopy (THz-TDS) was employed for measuring the thickness of impacted composites. Simulations and experimental results demonstrated the feasibility and accuracy of the IPINN-based approach.

physics.app-ph↗

arXiv2025

PCA-Guided Autoencoding for Structured Dimensionality Reduction in Active Infrared Thermography

Active Infrared thermography (AIRT) is a widely adopted non-destructive testing (NDT) technique for detecting subsurface anomalies in industrial components. Due to the high dimensionality of AIRT data, current approaches employ non-linear autoencoders (AEs) for dimensionality reduction. However, the latent space learned by AIRT AEs lacks structure, limiting their effectiveness in downstream defect characterization tasks. To address this limitation, this paper proposes a principal component analysis guided (PCA-guided) autoencoding framework for structured dimensionality reduction to capture intricate, non-linear features in thermographic signals while enforcing a structured latent space. A novel loss function, PCA distillation loss, is introduced to guide AIRT AEs to align the latent representation with structured PCA components while capturing the intricate, non-linear patterns in thermographic signals. To evaluate the utility of the learned, structured latent space, we propose a neural network-based evaluation metric that assesses its suitability for defect characterization. Experimental results show that the proposed PCA-guided AE outperforms state-of-the-art dimensionality reduction methods on PVC, CFRP, and PLA samples in terms of contrast, signal-to-noise ratio (SNR), and neural network-based metrics.

eess.IV↗