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Data analysis methods have been extensively used in active thermography for defect identification. Among them, principal component thermography (PCT) is popular for dimensionality reduction and feature extraction. PCT summarizes the thermal images with a small number of empirical orthogonal functions that better reflect the information of defects. However, PCT does not induce sparsity, which limits the interpretation of PCT results. Recently, sparse PCT (SPCT) has been proposed to provide more interpretable analysis results. However, SPCT does not consider the spatial connectivity between pixels, omitting the fact that a defective region is usually spatially connected. In this article, a novel thermographic data analysis method is proposed to overcome the shortcomings of the existing methods. The proposed method imposes both spatial connectivity and sparsity constraints in PCT. Finally, one case study on an ancient marquetry sample and another on a carbon fiber-reinforced polymer composite illustrate the feasibility of the proposed method.

The conservation of artworks is playing an increasingly important role in society today. Non-destructive investigation can provide the potential to identify deterioration as early as possible. In this research, transmission mid-wave infrared (MWIR) vision and air-coupled ultrasound (ACU) were used to investigate two paintings on canvas made from different textile support materials. An X-ray technique was used in the work for validation. It was found that the transmission mode can probe deeper and the differences in absorption due to the different textile support materials can be distinguished. This paper summarises advantages of the transmission inspection mode and compares and analyses images from the two techniques from a physical point of view.

The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic samples. The time series, including the raw temperature-time series and sequenced signals obtained from the first derivative after thermographic signal reconstruction was used to train and test the models respectively. Quantitative comparisons of testing results showed that the long short-term memory recurrent neural network model was more accurate in handling time dependent information compared to the artificial feed-forward neural network model.

Defects inside cultural heritage objects and composite structures have the potential to cause extensive damage. There is a need to develop 3D non-destructive imaging techniques to determine and visualize the positions of defects. The aim of this research is to develop a new non-destructive imaging technique using infrared-terahertz fusion approach. Recently, time-domain terahertz and infrared tomography proved the capability of providing 3D imaging. To combine the advantages of the two non-destructive imaging techniques, a feature-based fusion algorithm is designed in order to fuse the images generated by the systems. The fusion images show the slices through the thickness, i.e.from the surface to a known depth of the specimens themselves. Finally, a 3D visualized model is reconstructed by combining these through-depth slices

The imaging of structures with a complex material composition and geometry is still a challenge in the field of non-destructive testing (NDT). In this study, a non-invasive imaging technique is proposed for the non-destructive inspection of both cultural heritage and natural fiber composites. The proposed technique combines the surface information provided by infrared thermography (IRT) and the internal structure retrieved with terahertz (THz) time-domain spectroscopy using an unsupervised deep residual fusion network. Experiments show that the fusion results contain more material information than a single modality. In addition, 3D imaging has been achieved using the fusion results on natural fiber composites.

Defect detection in composite materials using active thermography is a well-studied field, and many thermographic data analysis methods have been proposed to facilitate defect visibility enhancement. In this work, we introduce a deep learning method that is constrained by known heat transfer phenomena described by a series of governing equations, also known in the literature as the physics-informed neural network (PINN). The accurate reconstruction of background information based on thermal images facilitates the identification of subsurface defects and reduction in noises caused by an uneven background and heating. The authors illustrate the method’s feasibility through experimental results obtained after pulsed thermography (PT) on a carbon fiber-reinforced polymer (CFRP) specimen.

The use of infrared thermography presents unique perspectives in imaging of artifacts to help interrogate their surface and subsurface characteristics, highlight deviations and detect contrast. This research capitalizes on active and passive thermal imagery along with advanced machine learning-based algorithms for pre- and post-processing of acquired scans. Such codes operate efficiently (compress data) to help link the observed temperature variations and the thermophysical parameters of targeted samples. One such processing modality is dictionary learning, which infers a “frame dictionary” to help represent the scans as linear combinations of a small set of features, thus training data to show a sparse representation. This technique (along factorization and component analysis-based methods) was used in current research on ancient polychrome marquetries aimed at detecting aging anomalies. The presented research is unique in terms of the targeted samples and the applied approaches and should provide specific guidance to similar domains.

In this paper, terahertz time-domain spectroscopy (THz-TDS) is used for the first time to detect fabricated defects in a glass fiber-skinned lightweight honeycomb composite panel. A novel amplitude polynomial regression (APR) algorithm is proposed as a preprocessing method. This method segments the amplitude–frequency curves to simulate the heating and the cooling monotonic behavior as in infrared thermography. Then, the method of empirical orthogonal function (EOF) imaging is applied on the APR preprocessed data as a postprocessing algorithm. Signal-to-noise ratio analysis is performed to verify the image improvement of the proposed APR-EOF modality from a quantitative point of view. Finally, the experimental results and the physical analysis show that THz is more suitable with respect to the detection of defects in glass fiber lightweight honeycomb composites.

Active thermography is an efficient and powerful technique for nondestructive testing of products made of composite materials, which enables rapid inspection of large areas, presents results as easily interpreted high-resolution images, and is easy to operate. In recent years, a number of thermographic data analysis methods were developed to enhance the visibility of subsurface defects, among which principal component thermography (PCT) is recommended because of its capability to enhance the contrast between defective and defect-free areas, compress data, and reduce noise. In this study, a sparse principal component thermography (SPCT) method is proposed, which inherits the advantages of PCT and allows more flexibility by introducing a penalization term. Compared to PCT, SPCT provides more interpretable analysis results owing to its structure sparsity. The feasibility and effectiveness of the proposed method are illustrated by the experimental results of the subsurface defect characterization in a carbon fiber reinforced plastic specimen.

Pulsed thermography data are typically affected by noise and uneven backgrounds, thereby complicating defect identification. Hence, various image analysis methods have been applied to improve defect detectability. However, most of them directly analyze the original images, while the low quality of the data is disregarded. Herein, a thermographic data analysis method named TriMap thermography with convolutional autoencoder (CAE) is proposed to overcome this problem. In this method, a CAE is used to reduce noise and enhance the quality of thermograms. Subsequently, the TriMap algorithm is used to extract features from the enhanced data. Specifically, the TriMap uses triplet information to improve the low-dimensional embedding quality and obtain an abstract representation of high-dimensional data. Finally, defects and uneven backgrounds are effectively distinguished by visualizing the embedding vectors. The test results of a carbon fiber-reinforced polymer specimen validate the effectiveness of the proposed method.

When planning the restoration of an artwork, the good practice involves the evaluation of the item healthiness before starting the common operation of cleaning, consolidation, etc., possibly through non-invasive techniques that supply meaningful information about the whole item. Motivated by this need, a plethora of imaging techniques are used in cultural heritage diagnostic typically borrowed from other applications – e.g. medical diagnostics, nondestructive testing, etc., and then tailored for inspecting cultural heritage objects. In the inspection of a painting, hyper- and multi- spectral techniques are commonly used to analyze the outer layers (varnish, pictorial and drawing) while X-ray, tomography, and many other can be employed to investigate its inner structure. Although highly desirable, a single technique providing all the info about a painting is still not available, thus it is of great interest defining protocols that could optimally exploit the complementarities of a limited number of techniques. To this aim, the present paper shows the combined use of the Hypecolorimetric Multispectral Imaging (HMI) and that of the Pulse-Compression Thermography (PuCT) on a 15th century wall painting attributed to the Italian artist Antonio del Massaro, also known as Pastura, and representing the Madonna with the Child and the Saints Jerome and Francis. In particular, HMI is a multispectral imaging method working from the ultraviolet to the near infrared region, exploiting advanced processing based on artificial intelligence to define hypercolorimetric coordinates. Such approach guarantees a thorough analysis of the outer layers, underlining previous restorations, varnish alterations and allowing the pigments to be classified from a comparison with a large database. The PuCT method adopted here has been tailored for the specific needs of artworks’ inspection and it allows for a safe imaging of the multilayer structure of paintings, and hence the stratigraphy analysis, through a suitable processing of the time-domain thermal response. The capabilities and the complementarities of the two techniques, whose info can also be fused through postprocessing techniques, are illustrated in detail in this paper. A false-color imaging approach is also proposed to improve the readability and analysis of the thermography results.

Imaging-based inspection techniques have practical advantages in the study and/or rehabilitation of artworks. They provide in some cases internal information on the status of the sample to be inspected. On the one hand, techniques based on active infrared thermography (IRT) are advantageous to obtaining complete images of the inspected parts, although a technical interpretation performed by a team of experts in non-destructive testing (NDT) techniques is needed above all when the target is composed, as in our case, by different materials. On the other hand, optical coherence tomography (OCT) is slow when inspecting complete parts, but it has great level of structural detail in subsurface measurements up to 3 mm. The complementary use of these two techniques, and its application to a very ancient marquetry sample with an unusual tessellatum layer, is presented herein. The plan size of the sample is 208×212 mm, while the tessellatum is 1.5 mm thick. Starting from thermal imaging inspections, using step-heating (SH) and pulsed thermography (PT), a defect map has been defined. Structural details of these defects using OCT will help the restorer in charge of the restoration process to perform a satisfactory work. This work was supported in part by the Spanish Economy and Competitiveness Minister under Project TEC2016-76021-C2-2-R; Jose Castillejo Grant (CAS17/00216) by the Spanish Minister of Education, Culture and Sports and Cantabria government postdoc Grant PS-UC-2018-16.

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The continuous deterioration of elements, with high patrimonial value over time, can only be mitigated or annulled through the application of techniques that facilitate the preventative detection of the possible agents of deterioration. InfraRed Thermography (IRT) is one of the most used techniques for this task. However, there are few IRT methodologies, which can automatically monitor the cultural heritage field, and are vitally important in eliminating the subjectivity in interpreting and accelerating the analysis process. In this work, a study is performed on a tessellatum layer of a mosaic to automatically: (i) Detect the first appearance of the thermal footprint of internal water, (ii) delimit the contours of the thermal footprint of internal water from its first appearance, and (iii) classify between harmful and non-harmful internal water. The study is based on the analysis of the temperature distribution of each thermal image. Five thermal images sequences are acquired during the simulation of different real situations, obtaining a set of promising results for the optimization of the thermographic inspection process, while discussing the following recommended steps to be taken in the study for future researches. España. Ministerio de Educación I Ref. FPU16/03950 Iberdrola I Ref. Cátedra Iberdrola VIII Centenario - Universidad de Salamanca

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively.

In thermographic non-destructive evaluation (TNDE), the optimal thermal stimulus to be provided on precious coatings applied on ancient walls confined in indoor environments is a complex problem to solve particularly when low temperatures characterize the room where they were built. In fact, thermal stresses are always the concern of any restorer who is interested in determining both the positions and sizes of superficial and sub-superficial defects. In scientific literature, the use of lamps appears to be dominant, but when large surfaces must be inspected in situ, many problems could arise, such as environmental reflections, emissivity variations, and non-uniform heating, which cause false alarms in defect detections. In this study, the heating phase was carried out by using a wooden tunnel and an air heater. The main physical and geometrical characteristics of these two objects were optimized in order to stimulate the external coating (called “finishing layer” in this study) as homogeneously as possible. In fact, taking into account the very low temperature of the room which contains the inspected aedicule, the energy deposition (heating phase) is enhanced and the energy dispersion (cooling phase) is reduced (thanks to the tunnel) at the same time. This improves the heat transfer phenomenon along the z axis (i.e., the thickness of the wall). The detection of the defects was performed via higher-order statistics thermography (HOST) technique, which processes the thermal images resulting from the heating and cooling phases. Bearing in mind the low emissivity value of the canary grass (i.e., the finishing layer), the use of heating sources in front of it must be minimized; otherwise, spurious reflections can be recorded and then processed. In particular, a computational fluid dynamics (CFD) approach implemented via Comsol Multiphysics® computer program was used to validate the experimental setup. This was applied to a couple of cases in the Baiocco's room subjected to restrictions of the Superintendence for Historical, Architectural, and Environmental Heritage of the Abruzzo region (Italy) owing to its high artistic importance. The exact position of each defect was modelled after a combined visual-acoustical-thermal inspection. An expert restorer helped us in this task. This study is important because, to the best of our knowledge, the problem discussed above has not been solved yet by the scientific community involved in the field of cultural heritage. The results are first thoroughly discussed and second experimentally validated using a combination between thermocouples and ground penetrating radar (GPR) technique, while advantages and disadvantages of the proposed method are highlighted in view of a future perspective of the present work.

The aim of this research is to investigate the suitability of truncated correlation photothermal coherence tomography (TC-PCT) for the non-destructive imaging of a replica of a real inlay to identify subsurface features that often are invisible areas of vulnerability and damage. Defects of inlays involve glue-rich areas, glue-starved areas, termite attack, insect damage, and laminar splitting. These defects have the potential to result in extensive damage to the art design layers of inlays. Therefore, there is a need for an imaging technique to visualize and determine the location of defects within the sample. The recently introduced TC-PCT modality proved capable of providing 3-D images of specimens with high axial resolution, deep subsurface depth profiling capability, and high signal-to-noise ratio (SNR). Therefore, in this study the authors used TC-PCT to image a fabricated inlay sample with various natural and artificial defects in the middle and top layers. The inlay in question reproduces to scale a piece of art preserved in the “Mirror room” of the Castle Laffitte in France. It was built by a professional restorer following the ancient procedure named element by element. Planar TC-PCT images of the inlay were stacked coherently to provide 3-D visualization of areas with known defects in the sample. The experimental results demonstrated the identification of defects such as empty holes, a hole filled with stucco, subsurface delaminations and natural features such as a wood knot and wood grain in different layers of the sample. For this wooden sample that has a very low thermal diffusivity, a depth range of 2 mm was achieved.

The restoration of Cultural Heritage wouldn't be possible without the financial resources to meet Cultural Heritage needs. A smart procedure to reduce in time and funds spent for restoration is linked to a planning of the diagnostic interventions acting to predict incipient defects undetectable to the naked eye. One of the main methods to fulfil this task is infrared thermography (IRT). The aim of this study is to examine the efficiency of various mathematical techniques in thermographic data processing, with respect to the thermal excitation procedure and the type of artificial defect in a panel painting sample. One of the thermographic analyses performed was based on the pixelwise algorithm for time-derivative of temperature (PATDT). With this algorithm, Newton's cooling law was applied pixel per pixel, resulting in the computation of the cooling rate of each pixel. In addition, the capabilities of the multivariate statistical analysis methods, independent component thermography (ICT) and sparse principal component thermography (SPCT) were also investigated. In the present case study, the authors inspected possible pathologies resembling splitting areas (i.e., detachments) in real panel paintings, with the consequent change in the heat transfer coefficient and the heat capacity. The feasibility of the different analysis methods was illustrated with the application results.

The detection of delaminations in composite laminates using automated thermographic scanning is a quite challenging task. The set-up parameters are not only dependent on the equipment, but on the inspected component as well. In this work, a methodology is discussed to use Finite Element (FE) model updating to automatically establish the most suitable inspection parameters for a given combination of the structure and the investigated delamination depths. The optimised results are compared using binary Probability of Detection analysis and are benchmarked with parameter sets retrieved by an expert using the regular trial & error approach. The results show an improvement of the accuracy and scanning speed which significantly increases as the POD decreases and the complexity of the samples increases.

Non-destructive testing (NDT) techniques play an important role in structural health monitoring (SHM) of composite structures, among which infrared thermography (IRT) is popular because it is easy to operate, enables rapid inspection of large areas, and presents results as easily interpreted thermal images. In order to achieve noise reduction, feature extraction, and data compression, principal component thermography (PCT) was developed for thermographic data processing. However, each principal component in PCT is a linear combination of all the original pixel values, making the results difficult to interpret and hence affecting defect identification. In this work, sparse principal component thermography (SPCT) is proposed as an improved version of PCT, which provides more interpretable analysis results owing to its structure sparsity and leads to a better defect detection. The feasibility of SPCT is illustrated with two case studies.

In building construction, it is very important to reduce energy consumption and provide thermal comfort. In this regard, defects in insulating panels can compromise the capability of these panels of reducing the heat flow by conduction with the surroundings. In recent years, both experimental techniques and numerical methods have been used for investigating the effect of defects on the thermal behavior of building panels. The main novelty of this work regards the application of both numerical and experimental approaches based on infrared thermography techniques for studying the effects of defects such as debonding on the insulation properties of cork panels. In particular, the effects of defects were investigated by using the Long Pulse Thermography technique and then by analyzing the thermal behavior of the panel during the cooling phase. Results show the capability of the proposed approaches in describing the effects of defects in cork panels such as detachments and the benefit effect of a shield coating in improving the insulation properties of the panel.

The use of recycled paper in HDPE (High Density PolyEthylene) matrix composites has recently been introduced as an interesting alternative to traditional recycling process for paper. HDPE is also used as double wall greenhouse glazing because panels are easy to install, UV stabilized, and affordable. These type of products must also be strong enough and durable in order to react under tensile loads provided by wind and harsh weather conditions. An interesting idea may be the insertion via injection moulding of chopped basalt and waste paper – i.e. , two natural products – in pure HDPE samples. It completely follows the environmental sustainability concept centred on the triple “ re ”, i.e. , re cycle, re use and re duce. The research presented herein starts with a Differential Scanning Calorimetry (DSC) inspection of pure HDPE and HDPE mixed with 5% by weight of waste paper plus 5% by weight of chopped basalt as fillers in order to obtain an insight related to the temperature at which possible thermal events (endothermic or exothermic) occur. The dog-bone samples were also inspected under UV conditions (380 nm) before and after tensile tests. The latter approach was simulated firstly by Comsol Multiphysics® computer program, and secondly recorded in real time via thermographic inspections. The temperature variation in a region of interest (ROI) selected at the centre of the samples was mapped in the time during the inspection by infrared thermography (IRT) method using a pseudo-static matrix reconstruction algorithm realized in Matlab® environment. Instead, the combined use of thermocouples aimed at emphasizing the knowledge of the heat transfer in the time due to the mechanical stress applied at the borders of the inspected samples. The aim was to understand whether modified HDPE can (or cannot) be a valid competitor of pure HDPE for the production of semi-transparent and robust panels.

Fibre metal laminates are widely implemented in the aerospace industry owing to the merits of fatigue resistance and plastic properties. An effective defect assessment technique needs to be investigated for this type of composite materials. In order to achieve accurate impact-induced damage evaluation, a multi-excitation infrared fusion method is introduced in this study. Optical excitation thermography with high performance on revealing surface and subsurface defects is combined with vibro-thermography to improve the capability of detection on defects. Quantitative analysis is carried out on the temperature curve to assess the impact-induced deformation. A new image fusion framework including feature extraction, feature selection and fusion steps is proposed to fully utilize the information from two excitation modalities. Six fibre metal laminates which contain aluminium-basalt fibre reinforced plastic and aluminium-glass fibre reinforced plastic are investigated. Features from different perspectives are compared and selected via intensity contrast on deformation area for fusion imaging. Both types of defects (i.e., surface and sub-surface) and the internal deformation situation of these six samples are characterized clearly and intuitively.

Inspection of cultural heritage objects plays nowadays paramount importance around the world. Non-destructive inspection is a must and a necessity in order to preserve the integrity of the artwork without losing any precious material composing it. The use of thermal non-destructive inspection is a good idea since, by exploiting the 3D diffusion inside the object, triggered by external radiation, surface and subsurface defects may be revealed. To do this, the long-wave infrared (LWIR) spectrum is usually exploited in combination with a thermal camera. In the cultural heritage field, the main problem to solve to detect as much as possible thermal imprints linked to invisible defects is the minimisation of the impact of emissivity variations caused by pigments composing the colours. A simple, effective, solid, and optimized method for decorated paintings is here applied right after some preliminary results. It is based on active thermography as the modality of inspection, and a thermal stimulus provoked by halogen lamps; thermal images recorded on a panel painting including man-made defects have been analysed and processed in MATLAB® environment. After extraction of de-nosing functions based on the heating and cooling steps, two methods are proposed to enhance the de-noised thermograms. Then, popular methods of Pulsed Phase Thermography (PPT) and the Principal Component Thermography (PCT) are applied. In the end, after minimizing the effect of emissivity variation, an optimization fusion proposal through post-processing the emissivity adjusted thermograms is provided. A brief but exhaustive review introduce and guide the readers towards the problem for which the authors took a step ahead via a proposal based on image fusion.

Distance estimation and pedestrian detection are critical for safe driving operation decision-making and autonomous vehicle intelligent control strategies. This paper proposes a novel multi-task Faster R-CNN detector which simultaneously realizes distance estimation and pedestrian detection using an improved ResNet-50 architecture. Images were acquired using a near-infrared camera with two near-infrared fill-lights devices during real road nighttime scenarios. Ground truth pedestrian distances used for training were obtained using LIDAR. The data used to optimize the multi-task Faster R-CNN detector were approximately 20 k high-quality near-infrared images with marked pedestrians and tagged distance values. The proposed algorithm including the distance estimation runs at a speed exceeding 7 fps. Pedestrian detection accuracy reached nearly 80% with a total average absolute distance estimation error rate of less than 5%.

Thermal and infrared imagery creates considerable developments in Non-Destructive Testing (NDT) area. Here, a thermography method for NDT specimens inspection is addressed by applying a technique for computation of eigen-decomposition which refers as Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). The proposed approach uses a shorter computational alternative to estimate covariance matrix and Singular Value Decomposition (SVD) to obtain the result of Principal Component Thermography (PCT) and ultimately segments the defects in the specimens applying color based K-medoids clustering approach. The problem of computational expenses for high-dimensional thermal image acquisition is also investigated. Three types of specimens (CFRP, Plexiglas and Aluminium) have been used for comparative benchmarking. The results conclusively indicate the promising performance and demonstrate a confirmation for the outlined properties.

The experimental results from infrared thermography surveys over two buildings externally exposed walls are presented. Data acquisition was performed on a static configuration by recording direct and indirect solar loading during several days and was processed using advanced signal processing techniques in order to increase signal-to-noise ratio and signature contrast of the elements of interest. It is demonstrated that it is possible to detect the thermal signature of large internal structures as well as surface features under such thermographic scenarios. Results from a long-wave microbolometer compared favorably to those from a mid-wave cooled infrared camera for the detection of large subsurface features from unprocessed images. In both cases, however, advanced signal processing greatly improved contrast of the internal features.

Because of the growing need for efficient energy production systems, energy policies promoted in recent years have also involved complex structures, like airports. This paper proposes the implementation of an energy management system for a very energy-consuming structure, composed of different power plants and many energy consumers: the Leonardo da Vinci International Airport of Rome. In this study, the examination of historical data related to airport electric power, thermal energy and fuel consumption is discussed, starting with the analysis of the production energy plants, mainly based on a combined heat and power system. Furthermore, pioneering solutions are proposed, not only to cover airport energy requirements, but also to test the safety and reliability of innovative load management systems. For this reason, the choice of the Leonardo da Vinci management company, oriented to install a smart storage in order to manage the bidirectional energy flows by consumers and producers, is justified. Such innovative energy procurement systems are examined, with the goal of achieving greater penetration of renewable sources: mini and micro wind power plants and high concentrator photovoltaic plants.

Nondestructive inspection (NDI) has immensely contributed to the restoration of historic and artistic works. As one of the most common used NDI methods, active thermography is an easy-to-operate and efficient technique. Principal component thermography (PCT) has been widely used to deal with thermographic data for enhancing the visibility of subsurface defects. Unlike PCT, edge-group sparse PCT introduced herein enforces sparsity of principal component (PC) loadings by considering the spatial connectivity of thermographic image pixels. The feasibility and effectiveness of this method is illustrated by the experimental results of the defect characterization in an ancient marquetry sample with a fir wood support.

This paper presents the contextual use of Pulse-Compression Thermography and Hypercolorimetric Multispectral Imaging for the diagnostic study of historical heritage paintings. The comparison and the integration of images provided by the two techniques allows the conservation state of both the painting layers and wooden support to be investigated. Relevant information on the painting technique and figurative scene can be obtained as well. The proposed approach was applied to two Italian Renaissance panel paintings. The first object tested was a 16th century panel painting representing a Crucifixion, exposed in the Museum of Colle del Duomo in Viterbo, Italy, and attributed to the workshop of the master Michelangelo Buonarroti. The second artwork was a late 15th century panel painting, representing The Resurrection of Christ, currently preserved at Museo Carrara in Bergamo, Italy, and recently re-attributed to Andrea Mantegna; it was identified as being the upper half of a whole composition together with the Descent into Limbo painting. HMI acquisitions and digital image processing tools allowed to investigate the upper painting layer, while PuCT imaging data gave relevant information on the structure of the wooden support proving to be an innovative stratigraphic investigation method. The combination of HMI and PuCT imaging techniques supplied information on the whole structure of the artworks, identifying surface degradation, different layers, wood defects and their position in the inner layers of the object. The integration of the above-mentioned techniques might stand as a new reference diagnostic method to evaluate conservative needs and support decisions for restoration.

Using an infrared image sequence, how can one make the inner structure of a sample more visible without human supervision nor understanding of the context? This task is well known as a challenging task. One of the reasons is due to the great number of external events and factors that can influence the acquisition. This paper introduces a solution to this question. The sequence of infrared images is processed using the monogenic signal theory in order to extract the phase congruency. The Fourier Transform must respect the Hermitian property and it does thank to the Hilbert Transform in the 1D case, however this property is not respected in 2D. It does thanks to some approximation made in the analytic signal. The monogenic signal theory consists in reprocessing the Fourier Transform by replacing the Hilbert Transform by a Riesz Transform in order to maintain the Hermitian symmetry. In other words the phase congruence can be described as a feature detection approach. Using the assumption that the symmetry, or asymmetry of the phase does represent the similarity of the features at one scale, then the phase congruency represents how similar the phase values are at different scales. The proposed approach is invariant to image contrast which makes it suitable for applications. It can also give valuable results even with very noisy sequences. The proposed approach has been evaluated by using referenced Carbon Fiber Reinforced Plastic sample.

This work presents a robotized line-scan thermography (LST) modality for the inspection of two paintings on canvas, which are the mock-ups of a famous oil painting, titled Portrait of the Painter’s Mother. Two widely used image post-processing methods were applied on the acquired LST thermal sequences. X-ray radiography was used to anticipate the thermal inspection results. It was concluded that LST is an effective technique for artwork inspection, and it can additionally provide a higher image contrast if compared to classical flash thermography technique. Consequently, the corresponding physical analysis was conducted. Overall, this work appears useful to investigate the feasibility of LST technique for large-scale artwork in-line inspection.

The addition of insulating layers on vertical walls of buildings is a common practice for providing a higher thermal insulation of the envelope. Workmanship defects, however, might influence the effectiveness of such insulation strategy. Damaged materials, incorrect installation, use of aged or weathered materials might alter the capability of reducing heat transfer through the envelope, whether vertical or sloped. In this work, drawbacks caused by the wrong installation of insulating material and by damaged material are assessed. A specimen wall was investigated by experimental and numerical approaches, the latter carried out by using COMSOL Multiphysics®. Results are compared and discussed.

Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.

This contribution focuses the attention on an innovative approach in diagnostics of paintings, based on the combine use of two imaging techniques named Hypecolorimetric Multispectral Imaging (HMI) and Pulse Compression Thermography (PuCT) applied to a 15th century wall painting, attributed to the Italian artist Antonio del Massaro, also known as Pastura. HMI technique is based on the simultaneous exploitation of the electromagnetic spectrum from the ultraviolet to the near infrared region. The acquisition, made under a standard metric, allows for characterizing the investigated surfaces in a more detailed way than the standard colorimetry. The system transforms any spectra in the range 300-1000nm into sevenfold hypecolorimetric coordinates. HMI guarantees very high radiometric (better than 95%) and colorimetric precision (better than ΔE = 2). PuCT is a thermography technique based on the use of coded modulated heating stimuli in combination with the pulse-compression technique. A PuCT scheme, based on coded LED excitation capable of optimizing the estimation of the impulse responses compared to the state-of-the-art PuCT literature has also been proposed. The combined use of HMI and PuCT recently revealed its potentiality in the investigation of important panel paintings by highlighting hidden details, mapping the conservation status, characterizing painting materials, etc. in a completely non-invasive way. Their combined capabilities are here tested on a wall painting representing the Madonna with the Child and the Saints Jerome and Francis, which was investigated during the restoration in the Laboratory in order to supply information about the materials and techniques.

The thermal transmittance ( U -value) of the vertical opaque building envelope plays a key role for the evaluation of the thermal performance of a structure. In order to speed up the assessment procedure and to investigate wider portions of buildings directly in situ, new methods based on the use of quantitative infrared thermography have been proposed during the last years. Although the studies agree about the influence of the operative conditions, a detailed report, based on measurements effected on a large wall in a controlled environment and at different boundary conditions, is still missing in literature. The aim of this work is to assess the validity of thermographic methods by using different operative conditions in a controlled environment. The results obtained by different IR methods in a Guarded Hot Box have been compared with heat flow meter measurements and theoretical values.

The object of the work is a character of the Madonna con Bambino (XIII–XV century) mural painting (Fontecchio – L’Aquila, Italy). It was analyzed by different nondestructive testing (NDT) techniques: electronic speckle pattern interferometry (ESPI), ultraviolet (UV) imaging and infrared vision. In addition, three micro-samplings were collected on suspected areas after examination of the signal strength variations over the raw thermograms. On the latter, the images’ quality was enhanced by applying advanced processing techniques. Micro-samplings were also analyzed by scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared (FTIR) and μ-Raman spectroscopy. Splitting, subsurface cracks and under-/over-paintings were detected by this integrated method.