Infrared Physics & Technology最新文献

英文中文

Infrared normal emissivity measurement of graphite by integrated blackbody method incorporating modified flight time variation

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-07 DOI: 10.1016/j.infrared.2025.105750

Xuyao Song , Chengzhi Yang , Baolin An , Luge Sun , Gui Lu , Yunlong Zhao , Qiwen Wang , Wei Dong , Zundong Yuan

The integrated blackbody method is an emissivity measurement method that does not require independent measurement of the true temperature of the sample surface. In this method, the sample flies rapidly from the bottom of the cavity to the mouth of the cavity, and this rapid flight will cause a temperature drop on the sample surface, which will produce a non-negligible negative deviation of the emissivity measurement. At present, the temperature drop and emissivity of sample 0 flight time cannot be measured by experiments. Therefore, to study the influence of surface temperature drop caused by sample flight, an experiment design that can change sample flight time is proposed in this paper, and a corresponding experimental system is established. By adjusting the pulse value of the linear motor, the sample flight time can be adjusted to (147–1604)ms when the flight distance is 180 mm. Then, taking graphite material as an example, the experiment of sample flight temperature drop was carried out with the wavelength of 0.65 μm and temperature of 1573 K. The result with 0 flight time was obtained by extrapolation, and the correction factor of sample temperature drop was calculated for different flight times, with the wavelength region of (0.65–13.00) μm. Finally, the emittance measurement experiment from 3.00 μm to 13.00 μm was carried out, and the temperature drop correction factor was applied to correct the results.

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引用次数: 0

Corrigendum to “Ultrathin 2D graphdiyne for nonlinear optical modulation in the mid-infrared regime” [Infrared Phys. Technol. 145 (2025) 105699]

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-07 DOI: 10.1016/j.infrared.2025.105742

Bingbing Tu , Tiantian Zhou , Yuanyuan Gou , Danqin Long , Tianlong Bai , Weichun Huang , Man Wu

引用次数: 0

Interface debonding defect detection of thermal barrier coatings with laser lock-in thermography based on edge sharpness optimization

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-07 DOI: 10.1016/j.infrared.2025.105744

Huipeng Wang , guoqing Zhang , Weisheng Li , Lihong Dong , Dongwei Cai , Haidou Wang , Huizhong Liu

Micro-defects created during the manufacturing of coatings would develop into interface defects during the service process and eventually the coating would fall off, it is crucial to detect and visualize minor debonding defects in coatings at an early stage. Lock-in thermography (LIT) is a non-destructive evaluation method capable of inspecting defects in composites by calculating differences of the thermal amplitude and phase between sound and defective regions. For debonding defects of different diameters, different optimal excitation frequencies need to be used to obtain phase characteristics. In order to adapt to the detection of debonding defects of multiple diameters and to improve the edge definition of the defects, a multi-frequency fusion LIT method is proposed in this paper. Multiple frequencies were used to excite the specimen to obtain optimal excitation frequencies for different diameters, and the effects of thermal diffusion length with amplitude and phase difference at different excitation frequencies were also discussed; then, multiple optimal excitation frequencies were used to extract phase features. Finally, fusion images comprising debonding defects of different diameters were constructed using principal component analysis. The method was also compared with other conventional methods, and the experimental results showed that the method could implement high signal-to-noise ratio and sharpened debonding defect extraction. In addition, the method realizes the extraction of debonding defects with a minimum diameter of 1 mm, which surpasses the detection limit of minute debonding defects at the coating interface.

{"title":"Interface debonding defect detection of thermal barrier coatings with laser lock-in thermography based on edge sharpness optimization","authors":"Huipeng Wang , guoqing Zhang , Weisheng Li , Lihong Dong , Dongwei Cai , Haidou Wang , Huizhong Liu","doi":"10.1016/j.infrared.2025.105744","DOIUrl":"10.1016/j.infrared.2025.105744","url":null,"abstract":"<div><div>Micro-defects created during the manufacturing of coatings would develop into interface defects during the service process and eventually the coating would fall off, it is crucial to detect and visualize minor debonding defects in coatings at an early stage. Lock-in thermography (LIT) is a non-destructive evaluation method capable of inspecting defects in composites by calculating differences of the thermal amplitude and phase between sound and defective regions. For debonding defects of different diameters, different optimal excitation frequencies need to be used to obtain phase characteristics. In order to adapt to the detection of debonding defects of multiple diameters and to improve the edge definition of the defects, a multi-frequency fusion LIT method is proposed in this paper. Multiple frequencies were used to excite the specimen to obtain optimal excitation frequencies for different diameters, and the effects of thermal diffusion length with amplitude and phase difference at different excitation frequencies were also discussed; then, multiple optimal excitation frequencies were used to extract phase features. Finally, fusion images comprising debonding defects of different diameters were constructed using principal component analysis. The method was also compared with other conventional methods, and the experimental results showed that the method could implement high signal-to-noise ratio and sharpened debonding defect extraction. In addition, the method realizes the extraction of debonding defects with a minimum diameter of 1 mm, which surpasses the detection limit of minute debonding defects at the coating interface.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"146 ","pages":"Article 105744"},"PeriodicalIF":3.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436801","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}

引用次数: 0

Taste evaluation by NIR spectroscopy and blending strategy of black tea based on GDE3 algorithm

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-05 DOI: 10.1016/j.infrared.2025.105739

Caijin Ling , Lizhu Huang , Yun Bian , Xiaolong Lu , Yiqing Lin , Qiaoyi Zhou , Feihu Song , Zhenfeng Li , Jinbiao Teng , Chunfang Song

Blending is an important part of tea production and processing. Taking Ying Hong No. 9 black tea as the object, a strategy for the taste quality evaluation and blending of black tea based on spectral information was proposed by combining quantitative analysis of near-infrared spectroscopy with multi-objective optimization algorithm. The effects of different spectral pre-processing methods and feature selection algorithms on the performance of the prediction model were compared. The quantitative prediction models of tea polyphenol, amino acid, aqueous extract and taste score of black tea were developed by the partial least squares regression method. PLSR results showed that the characteristic wavelength screening results of taste score could effectively reflect the chemical information of main taste substances in black tea. The SNV pretreatment method combined with SPA feature wavelength selecting algorithm was determined to be the most efficient combination for the taste quality prediction model, of which

R_{P}^{2}

and RMSEP reached 0.863 and 1.307, respectively. With the blending of eight raw material teas as an example, the tea blending problem was converted into a multi-objective optimization model based on the taste quality prediction results, and the blending recipes were globally optimizing by the GDE3 algorithm, and the optimized blending recipes achieved satisfactory performance in the manual sensory evaluation. The proposed approach can realize the digitization of the whole process from sensory evaluation to tea blending and can be considered as an effective aid in the blending process.

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引用次数: 0

A multi-scale infrared polarization image fusion method based on polarization-forming

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-04 DOI: 10.1016/j.infrared.2025.105735

Jin Duan , Yue Zheng , Guangqiu Chen , Ju Liu , Hao Zhang , Jingyuan Song

In traditional infrared polarization image fusion, the degree of linear polarization (DoLP) image is frequently fused with the intensity image (

S_{0}

). However, it is easily interfered by background noise, which degrades the quality of the fused image. To address this problem, a multi-scale infrared polarization image fusion method based on polarization-forming (PF) is proposed. Aiming at the sensitivity of the DoLP image to background noise, the approach is proposed to strip the information of the input image. The decomposition of the DoLP image is guided by controlling the inputs to the Stokes model. Subsequently, the ratio of local entropy (RoLEN) is employed to obtain an objective polarization vector image (

O P V

) for noise suppression. For the blurred detail, a fusion framework based on multi-scale hybrid window filtering (MSHWF) is constructed to fuse

O P V

with

S_{0}

. The framework adopts an improved filtered differential structure to separate edge and weak texture information. Then fusion strategies based on adaptive fractional saliency detection and adaptive energy are applied to highlight the target and its detailed information, respectively. Finally, the extracted sub-layer signatures are fused to obtain enhanced images with clear objectives and rich details. Qualitative and quantitative experimental results demonstrate the superiority of our method over other infrared polarization image fusion algorithms in several metrics such as information entropy, average gradient, and spatial frequency.

{"title":"A multi-scale infrared polarization image fusion method based on polarization-forming","authors":"Jin Duan , Yue Zheng , Guangqiu Chen , Ju Liu , Hao Zhang , Jingyuan Song","doi":"10.1016/j.infrared.2025.105735","DOIUrl":"10.1016/j.infrared.2025.105735","url":null,"abstract":"<div><div>In traditional infrared polarization image fusion, the degree of linear polarization (DoLP) image is frequently fused with the intensity image (<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>). However, it is easily interfered by background noise, which degrades the quality of the fused image. To address this problem, a multi-scale infrared polarization image fusion method based on polarization-forming (PF) is proposed. Aiming at the sensitivity of the DoLP image to background noise, the approach is proposed to strip the information of the input image. The decomposition of the DoLP image is guided by controlling the inputs to the Stokes model. Subsequently, the ratio of local entropy (RoLEN) is employed to obtain an objective polarization vector image (<span><math><mrow><mi>O</mi><mspace></mspace><mi>P</mi><mi>V</mi></mrow></math></span>) for noise suppression. For the blurred detail, a fusion framework based on multi-scale hybrid window filtering (MSHWF) is constructed to fuse <span><math><mrow><mi>O</mi><mspace></mspace><mi>P</mi><mi>V</mi></mrow></math></span> with <span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. The framework adopts an improved filtered differential structure to separate edge and weak texture information. Then fusion strategies based on adaptive fractional saliency detection and adaptive energy are applied to highlight the target and its detailed information, respectively. Finally, the extracted sub-layer signatures are fused to obtain enhanced images with clear objectives and rich details. Qualitative and quantitative experimental results demonstrate the superiority of our method over other infrared polarization image fusion algorithms in several metrics such as information entropy, average gradient, and spatial frequency.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"146 ","pages":"Article 105735"},"PeriodicalIF":3.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388479","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}

引用次数: 0

An intelligent model integrating multi-scale features and end-to-end classifier for accurate events recognition along fiber optic fence

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.infrared.2025.105740

Zhenshi Sun , Qian Yang , Haokun Yang , Kang Xue , Peizhou Fang

Pattern recognition is crucial for event detection and analysis in diverse optical fiber-based perimeter security systems. Although numerous methods and schemes have been investigated in this field, rapid and accurate pattern recognition still poses challenges for promptly identifying multiple events in practical application scenarios. For this reason, in this article, we propose and design an accurate model that integrates multi-scale features with an end-to-end classifier, enabling instantaneous and precise recognition of sensing patterns. Firstly, the original acquired time-series sensing signals are converted into two-dimensional time–frequency spectrum using a Stockwell transform, thus enabling accurate representation of the time–frequency features and phase variation characteristics. Subsequently, these acquired two-dimensional spectrum images collectively constitute the final sample dataset, which is then utilized in an end-to-end classifier that integrates a convolutional neural network with a gated recurrent unit neural network, for identifying and classifying the extent of events on the fiber optic fence. Finally, to establish the cogency and acceptability of our approach, a series of rigorous field tests have been conducted in a practical perimeter security system spanning a total sensing length of 21 km. In particular, nine types of sensing events are collected as data samples, acquired through an asymmetric dual Mach-Zehnder interferometer-based optical fiber perimeter security system. The results demonstrate that the proposed scheme outperforms previously reported schemes used for similar purposes. Verification has shown that the mean accuracy of the given nine sensing patterns achieved 98.96 %, while the mean processing time required was only 0.31 s. Thus, we believe that the proposed model holds significant promise for multiple event recognition in practical application scenarios.

{"title":"An intelligent model integrating multi-scale features and end-to-end classifier for accurate events recognition along fiber optic fence","authors":"Zhenshi Sun , Qian Yang , Haokun Yang , Kang Xue , Peizhou Fang","doi":"10.1016/j.infrared.2025.105740","DOIUrl":"10.1016/j.infrared.2025.105740","url":null,"abstract":"<div><div>Pattern recognition is crucial for event detection and analysis in diverse optical fiber-based perimeter security systems. Although numerous methods and schemes have been investigated in this field, rapid and accurate pattern recognition still poses challenges for promptly identifying multiple events in practical application scenarios. For this reason, in this article, we propose and design an accurate model that integrates multi-scale features with an end-to-end classifier, enabling instantaneous and precise recognition of sensing patterns. Firstly, the original acquired time-series sensing signals are converted into two-dimensional time–frequency spectrum using a Stockwell transform, thus enabling accurate representation of the time–frequency features and phase variation characteristics. Subsequently, these acquired two-dimensional spectrum images collectively constitute the final sample dataset, which is then utilized in an end-to-end classifier that integrates a convolutional neural network with a gated recurrent unit neural network, for identifying and classifying the extent of events on the fiber optic fence. Finally, to establish the cogency and acceptability of our approach, a series of rigorous field tests have been conducted in a practical perimeter security system spanning a total sensing length of 21 km. In particular, nine types of sensing events are collected as data samples, acquired through an asymmetric dual Mach-Zehnder interferometer-based optical fiber perimeter security system. The results demonstrate that the proposed scheme outperforms previously reported schemes used for similar purposes. Verification has shown that the mean accuracy of the given nine sensing patterns achieved 98.96 %, while the mean processing time required was only 0.31 s. Thus, we believe that the proposed model holds significant promise for multiple event recognition in practical application scenarios.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105740"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143272875","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}

引用次数: 0

Simultaneous restoration of a non-smooth strain distribution and temperature of an optical fiber Bragg grating based on its intensity spectrum

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.infrared.2025.105738

Małgorzata Detka , Cezary Kaczmarek

This paper presents the implementation and results of simultaneous restoration of a non-smooth strain distribution and temperature acting on a uniform fiber Bragg grating (FBG) based on its intensity spectrum. It is to be noted that only a part of the grating is subject to strain, preferably not less than half of its length. To accomplish this task, the transfer matrix method and the Nelder-Mead nonlinear optimization algorithm were used. At a test stand, measurements were taken of the intensity spectrum of the beam reflected by the FBG, which was subject to the simultaneous effects of the two above-mentioned measurands. Based on the measured FBG spectrum, calculations were carried out for the simultaneous restoration of the strain distribution and the temperature value. The obtained restoration results correspond well with the set temperatures and strain gradients. The relative error of reproducing the temperature value and the strain gradient does not exceed 4.4 % and 5.6 %, respectively.

引用次数: 0

Cluster optimized batch mode active learning sample selection method

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.infrared.2025.105746

Zhonghai He , Zhichao Xia , Yinzhi Du , Xiaofang Zhang

Active learning for selecting representative samples submitted to labeling can save model development costs. However, the performance of single-sample selection in each iteration is compromised by a heavy computational burden and low efficiency in reference measurements, issues that can be addressed through batch mode active learning. The sample redundancy in batch mode active learning has long been a challenge. To overcome the shortcomings, a batch mode sample selection method that takes representativeness, diversity, and informativeness into account is proposed, called Gaussian Process Cluster Optimized Active Learning (GPCOAL). Firstly, the Gaussian process is utilized to obtain the variance (information) of each sample. Subsequently, K-means clustering is performed to ensure diversity, and the sample with largest silhouettes is selected from each cluster to ensure representativeness. Finally, the Gaussian process variance and the silhouettes of each sample are integrated to select the most suitable samples within each cluster. Experimental validation is conducted on spectroscopic datasets to illustrate the effectiveness of the GPCOAL sample selection method.

{"title":"Cluster optimized batch mode active learning sample selection method","authors":"Zhonghai He , Zhichao Xia , Yinzhi Du , Xiaofang Zhang","doi":"10.1016/j.infrared.2025.105746","DOIUrl":"10.1016/j.infrared.2025.105746","url":null,"abstract":"<div><div>Active learning for selecting representative samples submitted to labeling can save model development costs. However, the performance of single-sample selection in each iteration is compromised by a heavy computational burden and low efficiency in reference measurements, issues that can be addressed through batch mode active learning. The sample redundancy in batch mode active learning has long been a challenge. To overcome the shortcomings, a batch mode sample selection method that takes representativeness, diversity, and informativeness into account is proposed, called Gaussian Process Cluster Optimized Active Learning (GPCOAL). Firstly, the Gaussian process is utilized to obtain the variance (information) of each sample. Subsequently, K-means clustering is performed to ensure diversity, and the sample with largest silhouettes is selected from each cluster to ensure representativeness. Finally, the Gaussian process variance and the silhouettes of each sample are integrated to select the most suitable samples within each cluster. Experimental validation is conducted on spectroscopic datasets to illustrate the effectiveness of the GPCOAL sample selection method.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105746"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143272874","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}

引用次数: 0

Research on gas concentration field reconstruction method based on laser SLAM and TDLAS

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-31 DOI: 10.1016/j.infrared.2025.105741

Pengxiang Cui , Jiuying Chen , Chuncheng Zhou , Xiaoya Yu , Huijing Zhang , Ping Wang , Ning Wang , Lingling Ma

A method for gas concentration field reconstruction is proposed, which integrates laser Simultaneous Localization and Mapping (SLAM) with Tunable Diode Laser Absorption Spectroscopy (TDLAS). This approach aims to address the reconstruction limitations of TDLAS in complex scenarios due to the lack of depth information. By introducing the spatial perception and autonomous localization capabilities of SLAM, a single TDLAS telemetry device mounted on a mobile robotic platform is utilized to achieve scanning and reconstruction of gas concentration fields over a large area (approximately 500 square meters). During the experimental validation conducted in an underground garage, the distribution positions of high-concentration gas bags were accurately reconstructed with an error margin within 0.6 m. Furthermore, the distribution and differences between methane and carbon dioxide gas bags of varying concentrations were successfully reflected, demonstrating the effectiveness and feasibility of the proposed method. In the future, this method holds promise for widespread applications in pipeline inspection, personnel detection, and other fields.

{"title":"Research on gas concentration field reconstruction method based on laser SLAM and TDLAS","authors":"Pengxiang Cui , Jiuying Chen , Chuncheng Zhou , Xiaoya Yu , Huijing Zhang , Ping Wang , Ning Wang , Lingling Ma","doi":"10.1016/j.infrared.2025.105741","DOIUrl":"10.1016/j.infrared.2025.105741","url":null,"abstract":"<div><div>A method for gas concentration field reconstruction is proposed, which integrates laser Simultaneous Localization and Mapping (SLAM) with Tunable Diode Laser Absorption Spectroscopy (TDLAS). This approach aims to address the reconstruction limitations of TDLAS in complex scenarios due to the lack of depth information. By introducing the spatial perception and autonomous localization capabilities of SLAM, a single TDLAS telemetry device mounted on a mobile robotic platform is utilized to achieve scanning and reconstruction of gas concentration fields over a large area (approximately 500 square meters). During the experimental validation conducted in an underground garage, the distribution positions of high-concentration gas bags were accurately reconstructed with an error margin within 0.6 m. Furthermore, the distribution and differences between methane and carbon dioxide gas bags of varying concentrations were successfully reflected, demonstrating the effectiveness and feasibility of the proposed method. In the future, this method holds promise for widespread applications in pipeline inspection, personnel detection, and other fields.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105741"},"PeriodicalIF":3.1,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372684","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}

引用次数: 0

A method of combining frame accumulation technique and dual temperature calibration for improving temperature measurement accuracy of an infrared thermal imager

IF 3.1 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2025-01-30 DOI: 10.1016/j.infrared.2025.105729

Honghui Zeng , Gang Li , Tian Lan , Hongshan Zhen , Fan Yang , Ling Lin

Infrared thermal imager has gradually become the basis for diagnosing many diseases. For diagnostic accuracy, we need to measure with high temperature resolution to improve the accuracy of body surface temperature field gradient measurements. In this paper, a combination of frame accumulation and dual temperature calibration is used to improve the temperature accuracy of an infrared imager. The frame accumulation effectively suppresses random noise within the image. The dual temperature calibration method adds temperature calibration for each pixel. This method reduces the influence of spatial distribution on temperature measurement and greatly improves the accuracy of temperature difference measurement between different points. The experiments show that the division value of temperature measurement can be improved to 0.01 °C. Moreover, the temperature measurement accuracy was improved by 3.85 times compared to the accuracy of only single calibration. In the edema case experiment, the dual calibration effectively increased the temperature difference between the edematous and surrounding areas from 1.37 °C to 1.49 °C. Therefore, this method can support the clinical application of infrared imaging for body surface temperature measurement.

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