Pub Date : 2025-01-03DOI: 10.1016/j.infrared.2025.105712
Mei Ming , Zhibin Ren , Wangyang Yu , Haifeng Zhang , Mengdan Liu
A tunable peak shift narrow band metamaterial perfect absorber (MPA) with a high fill factor VO2 phase transition grating layer operating at near-infrared waveband is studied theoretically and experimentally. The theoretical absorption peaks of the proposed MPA are located at 1080 nm (at 20 °C) and 946 nm (at 80 °C) respectively for normal incidence. The theoretical results show that the absorption spectra of the MPA are insensitive to the incident angles. The MPA exhibits a slight blue shift with the increase of surrounding media refractive index. Finally, the theoretical results are verified by the MPA sample fabrication and spectral measurements. The novel MPA can be better applied in microbolometers, pixel imaging, biochemical sensing and absorption filter et al.
{"title":"Peak shift near-infrared metamaterial absorber with VO2 phase transition grating layer","authors":"Mei Ming , Zhibin Ren , Wangyang Yu , Haifeng Zhang , Mengdan Liu","doi":"10.1016/j.infrared.2025.105712","DOIUrl":"10.1016/j.infrared.2025.105712","url":null,"abstract":"<div><div>A tunable peak shift narrow band metamaterial perfect absorber (MPA) with a high fill factor <span>VO<sub>2</sub></span> phase transition grating layer operating at near-infrared waveband is studied theoretically and experimentally. The theoretical absorption peaks of the proposed MPA are located at 1080 nm (at 20 °C) and 946 nm (at 80 °C) respectively for normal incidence. The theoretical results show that the absorption spectra of the MPA are insensitive to the incident angles. The MPA exhibits a slight blue shift with the increase of surrounding media refractive index. Finally, the theoretical results are verified by the MPA sample fabrication and spectral measurements. The novel MPA can be better applied in microbolometers, pixel imaging, biochemical sensing and absorption filter et al.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105712"},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The pedestrian detection network utilizing a combination of infrared and visible image pairs can improve detection accuracy by fusing their complementary information, especially in challenging illumination conditions. However, most existing dual-modality methods only focus on the effectiveness of feature maps between different modalities while neglecting the issue of redundant information in the modalities. This oversight often affects the detection performance in low illumination conditions. This paper proposes an efficient attention feature fusion network (EAFF-Net), which suppresses redundant information and enhances the fusion of features from dual-modality images. Firstly, we design a dual-backbone network based on CSPDarknet53 and combine with an efficient partial spatial pyramid pooling module (EPSPPM), improving the efficiency of feature extraction in different modalities. Secondly, a feature attention fusion module (FAFM) is built to adaptively weaken modal redundant information to improve the fusion effect of features. Finally, a deep attention pyramid module (DAPM) is proposed to cascade multi-scale feature information and obtain more detailed features of small targets. The effectiveness of EAFF-Net in pedestrian detection has been demonstrated through experiments conducted on two public datasets.
{"title":"EAFF-Net: Efficient attention feature fusion network for dual-modality pedestrian detection","authors":"Ying Shen, Xiaoyang Xie, Jing Wu, Liqiong Chen, Feng Huang","doi":"10.1016/j.infrared.2024.105696","DOIUrl":"10.1016/j.infrared.2024.105696","url":null,"abstract":"<div><div>The pedestrian detection network utilizing a combination of infrared and visible image pairs can improve detection accuracy by fusing their complementary information, especially in challenging illumination conditions. However, most existing dual-modality methods only focus on the effectiveness of feature maps between different modalities while neglecting the issue of redundant information in the modalities. This oversight often affects the detection performance in low illumination conditions. This paper proposes an efficient attention feature fusion network (EAFF-Net), which suppresses redundant information and enhances the fusion of features from dual-modality images. Firstly, we design a dual-backbone network based on CSPDarknet53 and combine with an efficient partial spatial pyramid pooling module (EPSPPM), improving the efficiency of feature extraction in different modalities. Secondly, a feature attention fusion module (FAFM) is built to adaptively weaken modal redundant information to improve the fusion effect of features. Finally, a deep attention pyramid module (DAPM) is proposed to cascade multi-scale feature information and obtain more detailed features of small targets. The effectiveness of EAFF-Net in pedestrian detection has been demonstrated through experiments conducted on two public datasets.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105696"},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.infrared.2025.105713
Long Liu , Bin Wang , Xiaoxuan Xu , Jing Xu
Tea is one of the most popular and widely consumed beverages worldwide, and accurately identifying its type is important for consumers. NIRS, a technology that uses near-infrared light for material analysis, is often employed for this purpose. Traditionally, automatic identification of NIRS has relied on classical machine learning methods. However, these conventional algorithms tend to lack accuracy when dealing with complex spectra. This article proposes a tea classification method based on a 1-dimensional residual network(1DResNet) model combined with transfer learning. The method is implemented in several steps. First, the 1DResNet model is pre-trained using a pre-training dataset. Then, the parameters of the feature extraction layers are frozen, and the model is fine-tuned using a fine-tuning dataset. Finally, the fine-tuned 1DResNet model is tested on a separate test dataset. Compared to traditional machine learning algorithms like Partial Least Squares Discriminant Analysis (PLS-DA), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP), the fine-tuned 1DResNet model demonstrates significantly improved classification accuracy (by more than 4.32%). Furthermore, compared to a 1DResNet model without fine-tuning, accuracy improves by 4.96%. When compared to a fine-tuned 1-dimensional Convolutional Neural Network (1DCNN), the accuracy increases by 4%.This notable improvement highlights the potential of the fine-tuned 1DResNet model in handling complex spectral data. The method also performs well in transfer learning tasks; both black tea and green tea classification results demonstrate that the 1DResNet model with fine-tuning has strong potential for migration tasks. Overall, this classification method offers broader application prospects.
{"title":"A tea classification method based on near infrared spectroscopy (NIRS) and transfer learning","authors":"Long Liu , Bin Wang , Xiaoxuan Xu , Jing Xu","doi":"10.1016/j.infrared.2025.105713","DOIUrl":"10.1016/j.infrared.2025.105713","url":null,"abstract":"<div><div>Tea is one of the most popular and widely consumed beverages worldwide, and accurately identifying its type is important for consumers. NIRS, a technology that uses near-infrared light for material analysis, is often employed for this purpose. Traditionally, automatic identification of NIRS has relied on classical machine learning methods. However, these conventional algorithms tend to lack accuracy when dealing with complex spectra. This article proposes a tea classification method based on a 1-dimensional residual network(1DResNet) model combined with transfer learning. The method is implemented in several steps. First, the 1DResNet model is pre-trained using a pre-training dataset. Then, the parameters of the feature extraction layers are frozen, and the model is fine-tuned using a fine-tuning dataset. Finally, the fine-tuned 1DResNet model is tested on a separate test dataset. Compared to traditional machine learning algorithms like Partial Least Squares Discriminant Analysis (PLS-DA), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP), the fine-tuned 1DResNet model demonstrates significantly improved classification accuracy (by more than 4.32%). Furthermore, compared to a 1DResNet model without fine-tuning, accuracy improves by 4.96%. When compared to a fine-tuned 1-dimensional Convolutional Neural Network (1DCNN), the accuracy increases by 4%.This notable improvement highlights the potential of the fine-tuned 1DResNet model in handling complex spectral data. The method also performs well in transfer learning tasks; both black tea and green tea classification results demonstrate that the 1DResNet model with fine-tuning has strong potential for migration tasks. Overall, this classification method offers broader application prospects.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105713"},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.infrared.2024.105697
Shaoquan Zhang , Yuyang Liu , Fan Li , Jiajun Zheng , Pengfei Lai , Chengzhi Deng , Mengxiong Tang , Shengqian Wang
With the widespread use of endmember spectral libraries, sparse regression techniques have become crucial in hyperspectral image unmixing. Recently, considering spatial information in sparse unmixing frameworks has become increasingly important, as it enhances the accuracy of mixed pixel decomposition. However, challenges such as spectral mismatch due to high correlation among endmember spectra and susceptibility to complex noise, like Gaussian and sparse noise, affect unmixing accuracy. To address these issues, this paper introduces the robust spatially regularized sparse unmixing algorithm with spectral library pruning (RSUSLP). The algorithm decomposes the unmixing process into multiple layers and prunes the spectral library at each layer to alleviate spectral mismatch. It models sparse noise within the unmixing framework and utilizes spectral weighting in conjunction with spatial weighting to increase row sparsity and spatial correlation, thus improving robustness. The optimization problem described in the algorithm is solved using the alternating direction method of multipliers (ADMM). As illustrated by experimental results from both simulated and real hyperspectral data, RSUSLP significantly surpasses current sparse unmixing methods by reducing spectral library interference and effectively handling noise, thereby enhancing the accuracy and performance of mixed pixel decomposition.
{"title":"Robust spatially regularized sparse unmixing of hyperspectral remote sensing images with spectral library pruning","authors":"Shaoquan Zhang , Yuyang Liu , Fan Li , Jiajun Zheng , Pengfei Lai , Chengzhi Deng , Mengxiong Tang , Shengqian Wang","doi":"10.1016/j.infrared.2024.105697","DOIUrl":"10.1016/j.infrared.2024.105697","url":null,"abstract":"<div><div>With the widespread use of endmember spectral libraries, sparse regression techniques have become crucial in hyperspectral image unmixing. Recently, considering spatial information in sparse unmixing frameworks has become increasingly important, as it enhances the accuracy of mixed pixel decomposition. However, challenges such as spectral mismatch due to high correlation among endmember spectra and susceptibility to complex noise, like Gaussian and sparse noise, affect unmixing accuracy. To address these issues, this paper introduces the robust spatially regularized sparse unmixing algorithm with spectral library pruning (RSUSLP). The algorithm decomposes the unmixing process into multiple layers and prunes the spectral library at each layer to alleviate spectral mismatch. It models sparse noise within the unmixing framework and utilizes spectral weighting in conjunction with spatial weighting to increase row sparsity and spatial correlation, thus improving robustness. The optimization problem described in the algorithm is solved using the alternating direction method of multipliers (ADMM). As illustrated by experimental results from both simulated and real hyperspectral data, RSUSLP significantly surpasses current sparse unmixing methods by reducing spectral library interference and effectively handling noise, thereby enhancing the accuracy and performance of mixed pixel decomposition.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105697"},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.infrared.2025.105711
Jiachen Sun , Fupeng Wang , Lin Zhang , Jiankun Shao
In this study, a neural network-assisted photoacoustic gas sensor is proposed that enables dual-component identification and concentration analysis of methane and ethylene, effectively addressing the issue of cross-interference in photoacoustic spectroscopy (PAS) technology. This sensor identifies the unknown photoacoustic second harmonic signal using a self-built photoacoustic deep neural network-component identification model, and then determines the composition of the gas sample. The traditional concentration fitting equation method and the self-built Photoacoustic Deep Neural Network-Concentration Regression Model are integrated to analyze the gas samples composed of single- and dual-component. The sensor demonstrates exceptionally high linearity, accuracy and robustness. Additionally, the minimum detection limits (MDLs) for a single-component are determined to be 0.28 ppm for methane and 1.56 ppm for ethylene. For dual-component detection, the MDLs are 8.86 ppm for methane and 4.55 ppm for ethylene. The promising results of the present study demonstrate that deep learning algorithm provides a more effective, accurate, and stable solution for elimination of cross-interference in photoacoustic gas sensor.
{"title":"A novel photoacoustic gas sensor for dual-component identification and concentration analysis","authors":"Jiachen Sun , Fupeng Wang , Lin Zhang , Jiankun Shao","doi":"10.1016/j.infrared.2025.105711","DOIUrl":"10.1016/j.infrared.2025.105711","url":null,"abstract":"<div><div>In this study, a neural network-assisted photoacoustic gas sensor is proposed that enables dual-component identification and concentration analysis of methane and ethylene, effectively addressing the issue of cross-interference in photoacoustic spectroscopy (PAS) technology. This sensor identifies the unknown photoacoustic second harmonic signal using a self-built photoacoustic deep neural network-component identification model, and then determines the composition of the gas sample. The traditional concentration fitting equation method and the self-built Photoacoustic Deep Neural Network-Concentration Regression Model are integrated to analyze the gas samples composed of single- and dual-component. The sensor demonstrates exceptionally high linearity, accuracy and robustness. Additionally, the minimum detection limits (MDLs) for a single-component are determined to be 0.28 ppm for methane and 1.56 ppm for ethylene. For dual-component detection, the MDLs are 8.86 ppm for methane and 4.55 ppm for ethylene. The promising results of the present study demonstrate that deep learning algorithm provides a more effective, accurate, and stable solution for elimination of cross-interference in photoacoustic gas sensor.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105711"},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1016/j.infrared.2025.105708
Wenya Huang , Shuai Yang , Yilun Yu , Beituo Liu , Rui Ge , Changsheng Xia , Fangyu Yue
The short-mid-/mid-wave infrared (SMWIR/MWIR) detectors can capture target information in two bands simultaneously, enhancing the recognition accuracy in challenging interference scenarios by suppressing complex background. Here, we explore the optoelectrical properties of InAs/InAsSb-superlattice-based dual-band niBin detectors consisting of SMWIR/MWIR absorbers separated by an AlGaAsSb unipolar barrier. The experimental and simulated results show that: i) The bandgaps of both SMWIR and MWIR absorbers can be consistently determined via transmission and photocurrent spectra, giving cutoff wavelengths of ∼ 4.16 µm and ∼ 5.21 µm (77 K), respectively, well in line with the design values; ii) The device structure shows n-type conductivity by Hall measurements, based on which the conduction and scattering mechanisms at various temperatures can be clarified; iii) Dark current density analysis reveals the temperature dependent dominant current mechanisms, i.e., the generation-recombination current in 150 K − 210 K and the diffusion current above 210 K; and iv) The Burstein-Moss effect can make the determined optical bandgap slightly redshifted (∼34 meV), as compared to that of electrical techniques. This work provides new insights into bandgap engineering and structural design for MWIR dual-color detectors based on InAs/InAsSb superlattices.
{"title":"Exploring the optoelectronic properties of medium-wave dual-color detectors based on asymmetric InAs/InAsSb superlattice niBin structure","authors":"Wenya Huang , Shuai Yang , Yilun Yu , Beituo Liu , Rui Ge , Changsheng Xia , Fangyu Yue","doi":"10.1016/j.infrared.2025.105708","DOIUrl":"10.1016/j.infrared.2025.105708","url":null,"abstract":"<div><div>The short-mid-/mid-wave infrared (SMWIR/MWIR) detectors can capture target information in two bands simultaneously, enhancing the recognition accuracy in challenging interference scenarios by suppressing complex background. Here, we explore the optoelectrical properties of InAs/InAsSb-superlattice-based dual-band niBin detectors consisting of SMWIR/MWIR absorbers separated by an AlGaAsSb unipolar barrier. The experimental and simulated results show that: i) The bandgaps of both SMWIR and MWIR absorbers can be consistently determined via transmission and photocurrent spectra, giving cutoff wavelengths of ∼ 4.16 µm and ∼ 5.21 µm (77 K), respectively, well in line with the design values; ii) The device structure shows n-type conductivity by Hall measurements, based on which the conduction and scattering mechanisms at various temperatures can be clarified; iii) Dark current density analysis reveals the temperature dependent dominant current mechanisms, i.e., the generation-recombination current in 150 K − 210 K and the diffusion current above 210 K; and iv) The Burstein-Moss effect can make the determined optical bandgap slightly redshifted (∼34 meV), as compared to that of electrical techniques. This work provides new insights into bandgap engineering and structural design for MWIR dual-color detectors based on InAs/InAsSb superlattices.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105708"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1016/j.infrared.2024.105706
Junyue Ke , Xiaowei Xu , Feng Qian , Xiong Bao , Zhengxiang Tian , Mingzhao Wang , Chao Wang , Xuan Yang , Zunhua Zhang , Xiaofeng Guo
In the field of accurate measurement of gas concentration by Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, the fluctuation of environmental pressure becomes a constraint, which affects the gas absorption line and measurement accuracy. Therefore, it is very important to implement pressure compensation for the test system. This study takes water as the research object and innovatively designs a water vapor concentration sensor, which integrates multiple key components, including a laser emission module using direct absorption method and precision temperature control, an open Herriott gas absorption cell, and a high-precision signal acquisition and processing module. Under standard atmospheric conditions (normal temperature and pressure), a comprehensive performance test of the sensor was first carried out. The results showed that the detection limit was 0.01% and the rise time of the detection system was about 12s (30–1000 ppm), and the fall time was about 10s (1000–30 ppm). Secondly, the influence of pressure change on the absorption line shape is discussed. A series of water vapor concentration measurement experiments with variable pressure are carried out, and a pressure compensation method is proposed to effectively control the measurement error within 2%. Finally, in order to ensure the reliability and accuracy of the sensor in practical applications, this study tested the sensor from low pressure to high pressure, and successfully verified its stability and accuracy during long-term operation.
{"title":"Research on low-pressure water vapor measurement based on TDLAS technology","authors":"Junyue Ke , Xiaowei Xu , Feng Qian , Xiong Bao , Zhengxiang Tian , Mingzhao Wang , Chao Wang , Xuan Yang , Zunhua Zhang , Xiaofeng Guo","doi":"10.1016/j.infrared.2024.105706","DOIUrl":"10.1016/j.infrared.2024.105706","url":null,"abstract":"<div><div>In the field of accurate measurement of gas concentration by Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, the fluctuation of environmental pressure becomes a constraint, which affects the gas absorption line and measurement accuracy. Therefore, it is very important to implement pressure compensation for the test system. This study takes water as the research object and innovatively designs a water vapor concentration sensor, which integrates multiple key components, including a laser emission module using direct absorption method and precision temperature control, an open Herriott gas absorption cell, and a high-precision signal acquisition and processing module. Under standard atmospheric conditions (normal temperature and pressure), a comprehensive performance test of the sensor was first carried out. The results showed that the detection limit was 0.01% and the rise time of the detection system was about 12s (30–1000 ppm), and the fall time was about 10s (1000–30 ppm). Secondly, the influence of pressure change on the absorption line shape is discussed. A series of water vapor concentration measurement experiments with variable pressure are carried out, and a pressure compensation method is proposed to effectively control the measurement error within 2%. Finally, in order to ensure the reliability and accuracy of the sensor in practical applications, this study tested the sensor from low pressure to high pressure, and successfully verified its stability and accuracy during long-term operation.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105706"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1016/j.infrared.2024.105705
Antimo Di Meo , Barbara Balbi , Marco Casciello , Maria Rosaria Vigorito , Pasquale Mormile , Massimo Rippa
Today, digital imaging techniques are extensively used as non-invasive tools for studying and analyzing artworks in the field of cultural heritage These methods provide critical structural information that supports conservation efforts or the development of the most appropriate restoration strategies. Among the case studies, the analysis of panel paintings represents a challenging task, requiring the use of suitable and complementary diagnostic approaches to achieve a comprehensive understanding of the artwork’s condition and its technical characteristics. Imaging techniques operating in the infrared spectrum are reliable, non-invasive, and non-contact methods for performing in situ analyses of artworks. In this study, we examined a 16th-century panel painting by Marco Cardisco, titled Adoration of the Magi, using both infrared reflectography (IRR) and active thermography (AT) techniques. For the AT approach, we applied a low-power pulsed thermal stimulation and analyzed the acquired thermal images in both spatial and temporal domains by combining the results achieved through Principal Component Thermography (PCT) and Thermal Recovery Trend (TRT) methods. The data collected from the two infrared imaging techniques, IRR and AT, were compared and evaluated across different areas of the painting, discussing and demonstrating their complementarity. This combined approach provided valuable insights into the technical and structural features of the artwork, thereby enhancing our understanding of its condition and state of conservation.
{"title":"Complementary infrared imaging methods for the structural and technical analysis of a panel painting: Adoration of the Magi by Marco Cardisco","authors":"Antimo Di Meo , Barbara Balbi , Marco Casciello , Maria Rosaria Vigorito , Pasquale Mormile , Massimo Rippa","doi":"10.1016/j.infrared.2024.105705","DOIUrl":"10.1016/j.infrared.2024.105705","url":null,"abstract":"<div><div>Today, digital imaging techniques are extensively used as non-invasive tools for studying and analyzing artworks in the field of cultural heritage These methods provide critical structural information that supports conservation efforts or the development of the most appropriate restoration strategies. Among the case studies, the analysis of panel paintings represents a challenging task, requiring the use of suitable and complementary diagnostic approaches to achieve a comprehensive understanding of the artwork’s condition and its technical characteristics. Imaging techniques operating in the infrared spectrum are reliable, non-invasive, and non-contact methods for performing in situ analyses of artworks. In this study, we examined a 16th-century panel painting by Marco Cardisco, titled Adoration of the Magi, using both infrared reflectography (IRR) and active thermography (AT) techniques. For the AT approach, we applied a low-power pulsed thermal stimulation and analyzed the acquired thermal images in both spatial and temporal domains by combining the results achieved through Principal Component Thermography (PCT) and Thermal Recovery Trend (TRT) methods. The data collected from the two infrared imaging techniques, IRR and AT, were compared and evaluated across different areas of the painting, discussing and demonstrating their complementarity. This combined approach provided valuable insights into the technical and structural features of the artwork, thereby enhancing our understanding of its condition and state of conservation.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105705"},"PeriodicalIF":3.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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