Infrared Physics & Technology最新文献_第10页

An infrared DoLP calculation model with parameters instantiation for high-temp rough surfaces

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

Infrared Physics & Technology

Pub Date : 2024-12-26 DOI: 10.1016/j.infrared.2024.105686

Kunna Yan, Haizheng Liu, Zelin Shi, Mingqi Pang, Yunge Cui, Qiunan Tong

Infrared polarimetric imaging has gained more attention in recent years in various defense applications. As the temperature of an object rises, the infrared polarization on its surface becomes stronger in room-temp environments. This polarization mainly depends on the thermal emission and is slightly affected by the reflection of ambient radiation. Due to the difficulty in solving the relevant sample parameters in the infrared band, the complexity of infrared polarization modeling for high-temp surfaces exceeds that of visible and near-infrared. Based on the polarized bidirectional reflectance distribution function (pBRDF) and Kirchhoff’s law, this work first derives the emissivity vector. Then, a hybrid infrared degree of linear polarization (DoLP) calculation model framework for high-temp rough surfaces is proposed. This framework consists of thermal emission and the reflection of ambient radiation. The reflection model component is simplified by uniformizing the ambient incidence radiation on surfaces. An infrared DoLP measurement experiment is conducted on three sample surfaces at multiple high-tempe points. Considering the insufficiency of measurement data at a single high-temp point, model parameters are inverted using data from two high-temp points. The genetic algorithm is selected as the inversion method, and an instantiated hybrid model is obtained based on the optimal solution of parameters. At multiple high-temp points, the DoLP curve predicted by the hybrid model is consistent with the measured curve. When the observation angle is less than

75 °

, the absolute residual between the predicted and measured DoLP is less than 0.005. The experimental results demonstrate that the proposed hybrid infrared DoLP calculation model can be applied to predict and simulate the polarization characteristics of high-temp objects in room-temp environments. Moreover, the proposed inversion approach can accurately estimate the material surface parameters.

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引用次数: 0

Accumulation of holes at the mesa sidewall surface of InGaAsSb extended-short wavelength infrared photodetectors

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

Infrared Physics & Technology

Pub Date : 2024-12-26 DOI: 10.1016/j.infrared.2024.105695

Nong Li , Dongwei Jiang , Guowei Wang , Donghai Wu , Wenguang Zhou , Xiangyu Zhang , Faran Chang , Ruoyu Xie , Ye Zhang , Yifan Shan , Yan Liang , Lingze Yao , Qiuyao Pang , Chen Li , Hongyue Hao , Yingqiang Xu , Zhichuan Niu

This work investigates leakage mechanisms in InGaAsSb e-SWIR detectors by examining the surface states of the mesa sidewall and the longevity of passivation layers. The passivation efficacy of SU8, SiO₂ and S-SiO₂ films is evaluated, using an un-passivated photodetector as a reference. The S-SiO₂ composite film passivated InGaAsSb mesa sidewall exhibits the highest surface resistivity. To investigate the factors contributing to this high surface resistivity, we employed gate-controlled photodetectors. Our findings indicate that the neutralization of dangling bonds at the InGaAsSb surface by sulfide is not the main factor. Instead, by examining the longevity of these passivation films, we determined that the high resistivity of the S-SiO₂ film is related to its airtightness. Additionally, the GCPD results indicate an accumulation of holes at the InGaAsSb surface. These findings suggest that a pBp structure may provide advantages in reducing the leakage current of InGaAsSb e-SWIR photodetectors.

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引用次数: 0

Enhanced surface adhesion and LWIR paint effect on the low vacuum radio-frequency argon plasma (LVRFAP) treated composite laminates for aerospace applications

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

Infrared Physics & Technology

Pub Date : 2024-12-25 DOI: 10.1016/j.infrared.2024.105693

Murat Tanışlı , Mete Bakır , Adem Can Uşak , Suat Pat , Neslihan Şahin

The aim of this study is to investigate the effect of low vacuum radio frequency argon plasma treatment on paint coatings of composite materials and derivative materials used in the aviation industry, and to examine the morphological structure of composite materials after plasma application to the surface, especially to determine their suitability for paint adhesion. Therefore, the atomic interactions on plasma treated surfaces are studied through various analysis and test methods such as atomic force microscopy (AFM), scratch test and contact angle measurement. The motivation of this paper is to present the emissivity changes of plasma treated five harness satin weave carbon fiber reinforced polyphenylene sulfide (PPS) matrix composite material after the application of long wave infrared (LWIR) paint coating. Intense temperature changes can cause undesirable effects in many materials. Increased temperature can cause expansion of these material. When low vacuum radio-frequency argon plasma (LVRFAP) is applied to the composite laminates, their mechanical properties doesn't change. In also, thermoset and thermoplastic materials used in the study have similar mechanical property. The results of the study show that plasma application to composites is a simple, fast and reliable solution to change the adhesion properties of paint to composite materials and a very useful technology to improve the surface properties. Thermal images photos of LWIR-painted untreated, and LWIR-painted plasma treated sample indicate that their emissivity measurements are close to each other, but in plasma treated samples, emissivity values were decreased to a lower value.

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引用次数: 0

RDAGAN: Residual Dense Module and Attention-Guided Generative Adversarial Network for infrared image generation

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

Infrared Physics & Technology

Pub Date : 2024-12-24 DOI: 10.1016/j.infrared.2024.105685

Tianwei Zhou , Yanfeng Tang , Weida Zhan , Yu Chen , Yueyi Han , Deng Han

Visible-to-Infrared image Translation (V2I) is fundamentally an ill-defined problem, since RGB images do not have any information about the thermal characteristics of different objects. In recent years, with the development of deep learning, infrared image generation has been widely studied, however, existing methods often suffer from the problems of incomplete structure and blurred details in the generated infrared images. For this reason, this paper proposes Residual Dense Module and Attention-Guided Generative Adversarial Networks (RDAGAN) to improve the generation quality of infrared images. RDAGAN incorporates several modules, firstly, we adopt Residual Dense Module (RDM), which improves the model feature extraction capability by enhancing the depth and width of the model. Second, in order to guide the model to focus on the key parts of the image, we designed Attention-Guided Module (AGM), which enable the model to learn and generate the key features of the infrared image more efficiently, thus generating a pseudo-image that is closer to the real infrared image. To further optimize the generated infrared images, we also propose a composite loss function combining the Adversarial loss, L1 loss, Perceptual loss, and SSIM loss, where the Perceptual loss significantly reduces the LPIPS value and improves the visual perceptual quality of the generated images, and the SSIM loss strengthens the edge texture details of the generated images and significantly improves the SSIM value. Experimental results on KAIST, FLIR and LLVIP datasets show that RDAGAN outperforms the existing methods in terms of performance metrics and visual quality, and generates clearer and more realistic infrared images.

{"title":"RDAGAN: Residual Dense Module and Attention-Guided Generative Adversarial Network for infrared image generation","authors":"Tianwei Zhou , Yanfeng Tang , Weida Zhan , Yu Chen , Yueyi Han , Deng Han","doi":"10.1016/j.infrared.2024.105685","DOIUrl":"10.1016/j.infrared.2024.105685","url":null,"abstract":"<div><div>Visible-to-Infrared image Translation (V2I) is fundamentally an ill-defined problem, since RGB images do not have any information about the thermal characteristics of different objects. In recent years, with the development of deep learning, infrared image generation has been widely studied, however, existing methods often suffer from the problems of incomplete structure and blurred details in the generated infrared images. For this reason, this paper proposes Residual Dense Module and Attention-Guided Generative Adversarial Networks (RDAGAN) to improve the generation quality of infrared images. RDAGAN incorporates several modules, firstly, we adopt Residual Dense Module (RDM), which improves the model feature extraction capability by enhancing the depth and width of the model. Second, in order to guide the model to focus on the key parts of the image, we designed Attention-Guided Module (AGM), which enable the model to learn and generate the key features of the infrared image more efficiently, thus generating a pseudo-image that is closer to the real infrared image. To further optimize the generated infrared images, we also propose a composite loss function combining the Adversarial loss, L1 loss, Perceptual loss, and SSIM loss, where the Perceptual loss significantly reduces the LPIPS value and improves the visual perceptual quality of the generated images, and the SSIM loss strengthens the edge texture details of the generated images and significantly improves the SSIM value. Experimental results on KAIST, FLIR and LLVIP datasets show that RDAGAN outperforms the existing methods in terms of performance metrics and visual quality, and generates clearer and more realistic infrared images.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105685"},"PeriodicalIF":3.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101561","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

Beam quality improvement of mid-infrared laser diode with monolithically integrated sawtooth waveguide structures

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

Infrared Physics & Technology

Pub Date : 2024-12-24 DOI: 10.1016/j.infrared.2024.105694

Jianmei Shi , Chengao Yang , Yihang Chen , Tianfang Wang , Hongguang Yu , Juntian Cao , Zhengqi Geng , Zhiyuan Wang , Haoran Wen , Enquan Zhang , Yu Zhang , Hao Tan , Donghai Wu , Yingqiang Xu , Haiqiao Ni , Zhichuan Niu

High-power and high beam-quality semiconductor diode lasers emitting around 2 μm have sparked considerable interest owing to their potential applications across various industrial and medical fields. Here, we demonstrate a sawtooth waveguide (SW) structure to achieve enhanced lateral beam quality and power performance based on GaSb. A valid lateral mode discrimination capability is guaranteed by the integrated SW design and triple confirmed by simulation, near field and far field experimental measurement. The resulting SW laser exhibits an enhanced continuous-wave output power of 1.392 W around 2 μm with an increased power conversion efficiency. Moreover, a more concentrated and narrower beam profile is obtained across its whole measurement range, with lateral beam parameter product notably improved by up to 48 % compared to the conventional broad area laser. These results show significant promise for enhancing the performance of existing systems and enabling new applications.

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引用次数: 0

Rapid non-destructive identification of blueberry origin based on near infrared spectroscopy combined with wavelength selection

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

Infrared Physics & Technology

Pub Date : 2024-12-24 DOI: 10.1016/j.infrared.2024.105688

Guannan Wang , Na Wang , Ying Dong , Jinming Liu , Peng Gao , Rui Hou

To realize the nondestructive identification of blueberry origin, near-infrared spectroscopy was used to obtain the original spectral data of blueberry. Given the problems of spectral bandwidth, severe overlap, and complicated information analysis in the collection of near-infrared spectral data, we integrated successive projection algorithm (SPA) and sparrow search algorithm (SSA) with partial least squares regression (PLS) and support vector machine (SVM), respectively, resulting in the construction of two wavelength selection (WS) models: SPA-PLS and SSA-SVM for WS from blueberry spectral data, 30 and 148 wavelength variables were selected respectively. To further enhance the accuracy of blueberry origin identification, we incorporated SSA into both Optimal Latin hypercube idea and Osprey algorithm, creating a multi-strategy hybrid sparrow search algorithm (ZOSSA). This approach reduced the number of selected wavelengths from 148 to 36. Using wavelengths selected from three different techniques as input subsets, a blueberry origin recognition model is constructed by placing them separately into a support vector machine. The experimental results prove that the performance of the wavelength-optimized model is higher than that of the full spectra performance, and the wavelength variables screened by ZOSSA have the best effect. The wavelength variables identified by ZOSSA exhibit superior performance with an accuracy rate of 96.21%, precision rate of 95.12 %, recall rate of 94.78 %, and F1 score of 94.94 % on the test set; surpassing those obtained using SPA (89.39 %, 87.43 %, 88.72 %, and 88.08 %) as well as SSA (90.15 %, 87.90 %, 88.16 %, and 88.02 %). The method strikes a balance between selecting an appropriate number of wavelengths while maintaining high model performance levels; thus meeting requirements for fast, accurate, nondestructive origin identification not only for blueberries but also providing novel insights for identifying origins in other agricultural products.

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引用次数: 0

Performance comparison of initialization representations for functional data analysis based hyperspectral image classification

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

Infrared Physics & Technology

Pub Date : 2024-12-22 DOI: 10.1016/j.infrared.2024.105691

Yaqiu Zhang, Quanhua Zhao, Yu Li, Xueliang Gong

In functional data analysis (FDA) based hyperspectral image (HSI) classification, the optimizing initialization representations of high dimension spectral vectors for individual pixels in the HSI is crucial for obtaining the high-precision classification results. In FDA, basis functions are commonly used to represent a given function as its initialization representations in terms of root mean square error (RMSE) scheme. Unfortunately, RMSE based basis function fittings for initialization representations of HSI spectral vector seems not be optimal from HSI classification perspective. As a result, this study compares five types of basis functions to obtain the optimal initialization representations from a classification perspective and explores their essential characteristics. The research results suggest that the basis functions can in nature express low-frequency and high-frequency features, where the low-frequency features are more clustering properties and these features are more useful for HSI classification. The Gaussian function, in particular, attenuates high-frequency features while amplifying low-frequency features, promoting intra-class aggregatability and inter-class separability. Thus, despite yielding relatively higher RMSE compared to the classical FDA approach, it achieves better classification accuracy. Consequently, RMSE should not be the sole criterion for evaluating the optimal initialization representations in HSI classification. Additionally, this study introduces regularized basis weighted local least squares penalty (RBWLP) strategy that better handles non-stationary HSI data, contributing to the further extension of FDA methods in the context of HSI.

{"title":"Performance comparison of initialization representations for functional data analysis based hyperspectral image classification","authors":"Yaqiu Zhang, Quanhua Zhao, Yu Li, Xueliang Gong","doi":"10.1016/j.infrared.2024.105691","DOIUrl":"10.1016/j.infrared.2024.105691","url":null,"abstract":"<div><div>In functional data analysis (FDA) based hyperspectral image (HSI) classification, the optimizing initialization representations of high dimension spectral vectors for individual pixels in the HSI is crucial for obtaining the high-precision classification results. In FDA, basis functions are commonly used to represent a given function as its initialization representations in terms of root mean square error (RMSE) scheme. Unfortunately, RMSE based basis function fittings for initialization representations of HSI spectral vector seems not be optimal from HSI classification perspective. As a result, this study compares five types of basis functions to obtain the optimal initialization representations from a classification perspective and explores their essential characteristics. The research results suggest that the basis functions can in nature express low-frequency and high-frequency features, where the low-frequency features are more clustering properties and these features are more useful for HSI classification. The Gaussian function, in particular, attenuates high-frequency features while amplifying low-frequency features, promoting intra-class aggregatability and inter-class separability. Thus, despite yielding relatively higher RMSE compared to the classical FDA approach, it achieves better classification accuracy. Consequently, RMSE should not be the sole criterion for evaluating the optimal initialization representations in HSI classification. Additionally, this study introduces regularized basis weighted local least squares penalty (RBWLP) strategy that better handles non-stationary HSI data, contributing to the further extension of FDA methods in the context of HSI.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105691"},"PeriodicalIF":3.1,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102040","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 review of recent trends, advancements, and future directions in near-infrared spectroscopy applications in biofuel production and analysis

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

Infrared Physics & Technology

Pub Date : 2024-12-21 DOI: 10.1016/j.infrared.2024.105692

Flavio Odoi-Yorke , Sandra Ama Kaburi , Rita Elsie Sanful , Gifty Serwaa Otoo , Francis Padi Lamptey , Agnes Abeley Abbey , Ephraim Bonah Agyekum , Ransford Opoku Darko

The growing demand for sustainable energy solutions has intensified research into biofuel production and analysis techniques. Near-infrared spectroscopy (NIRS) has emerged as a promising tool in this field, yet a comprehensive understanding of its applications and impact remains lacking. This study aims to systematically review and analyse the applications of NIRS in biofuel production and analysis, providing insights into research trends, key contributors, and future directions. A bibliometric analysis was conducted using the Scopus database, covering publications from 1996 to 2023. The methodology included quantitative analysis, thematic mapping, factorial analysis, and citation analysis using the Bibliometrix package in R. The findings reveal a significant growth in NIRS biofuel applications, with an 11.85% annual increase in publications. The USA, China, and Brazil emerged as leading contributors, with strong international collaborations. Key applications include real-time monitoring of biodiesel production, biomass characterisation, and biogas production analysis. The integration of machine learning with NIRS data analysis represents a notable trend, enhancing prediction accuracy and model robustness. Thematic analysis identifies emerging research clusters in process monitoring, quality control, and feedstock analysis. These findings have important implications for both research and industry. The versatility of NIRS across various biofuel types and production stages suggests its potential for improving process efficiency and product quality. The identified research trends provide direction for future studies, particularly in standardising methodologies and developing more sophisticated data analysis techniques. This review highlights NIRS as a key technology that is enabling the advancement of sustainable biofuel production.

{"title":"A review of recent trends, advancements, and future directions in near-infrared spectroscopy applications in biofuel production and analysis","authors":"Flavio Odoi-Yorke , Sandra Ama Kaburi , Rita Elsie Sanful , Gifty Serwaa Otoo , Francis Padi Lamptey , Agnes Abeley Abbey , Ephraim Bonah Agyekum , Ransford Opoku Darko","doi":"10.1016/j.infrared.2024.105692","DOIUrl":"10.1016/j.infrared.2024.105692","url":null,"abstract":"<div><div>The growing demand for sustainable energy solutions has intensified research into biofuel production and analysis techniques. Near-infrared spectroscopy (NIRS) has emerged as a promising tool in this field, yet a comprehensive understanding of its applications and impact remains lacking. This study aims to systematically review and analyse the applications of NIRS in biofuel production and analysis, providing insights into research trends, key contributors, and future directions. A bibliometric analysis was conducted using the Scopus database, covering publications from 1996 to 2023. The methodology included quantitative analysis, thematic mapping, factorial analysis, and citation analysis using the Bibliometrix package in R. The findings reveal a significant growth in NIRS biofuel applications, with an 11.85% annual increase in publications. The USA, China, and Brazil emerged as leading contributors, with strong international collaborations. Key applications include real-time monitoring of biodiesel production, biomass characterisation, and biogas production analysis. The integration of machine learning with NIRS data analysis represents a notable trend, enhancing prediction accuracy and model robustness. Thematic analysis identifies emerging research clusters in process monitoring, quality control, and feedstock analysis. These findings have important implications for both research and industry. The versatility of NIRS across various biofuel types and production stages suggests its potential for improving process efficiency and product quality. The identified research trends provide direction for future studies, particularly in standardising methodologies and developing more sophisticated data analysis techniques. This review highlights NIRS as a key technology that is enabling the advancement of sustainable biofuel production.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105692"},"PeriodicalIF":3.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143098119","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

Accreditations of the optimal origins of Boletus bainiugan using Fourier transform near-infrared spectroscopy in combination with environmental variables and heavy metal element determinations

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

Infrared Physics & Technology

Pub Date : 2024-12-21 DOI: 10.1016/j.infrared.2024.105690

Shuai Liu , Honggao Liu , Jieqing Li , Yuanzhong Wang

Boletus bainiugan is a wild resource and favorite food for people from Yunnan, China. At present, it is not capable of being cultivated. Therefore, it is predicted that the changes in the area of habitable zone and the degree of heavy metal enrichment of Boletus bainiugan from different origins under the current and future climatic conditions. This will help consumers to avoid purchasing products from the areas where heavy metals are enriched in excess of the standard and provide a reference for the better-protected areas of origin of Boletus bainiugan. The different origins were ranked in order of preference based on the size of the suitable area, the degree of heavy metal enrichment, and the level of chemical composition. The results show that there was a positive correlation between the absorbance values of the samples and the degree of concentration of the highly suitable habitat in Anning, Kunming, Chuxiong, Baoshan and Wenshan. However, there is an over-accumulation of heavy metal elements in individual production areas. It can be seen that Boletus bainiugan does vary considerably from one origin to another, and the identification of origin is of practical significance. The support vector machine (SVM) model is built to solve the problem of different origin traceability of Boletus bainiugan. The SVM model using normalization and second-order derivative method is found to be the fitting origin traceability model.

{"title":"Accreditations of the optimal origins of Boletus bainiugan using Fourier transform near-infrared spectroscopy in combination with environmental variables and heavy metal element determinations","authors":"Shuai Liu , Honggao Liu , Jieqing Li , Yuanzhong Wang","doi":"10.1016/j.infrared.2024.105690","DOIUrl":"10.1016/j.infrared.2024.105690","url":null,"abstract":"<div><div><em>Boletus bainiugan</em> is a wild resource and favorite food for people from Yunnan, China. At present, it is not capable of being cultivated. Therefore, it is predicted that the changes in the area of habitable zone and the degree of heavy metal enrichment of <em>Boletus bainiugan</em> from different origins under the current and future climatic conditions. This will help consumers to avoid purchasing products from the areas where heavy metals are enriched in excess of the standard and provide a reference for the better-protected areas of origin of <em>Boletus bainiugan</em>. The different origins were ranked in order of preference based on the size of the suitable area, the degree of heavy metal enrichment, and the level of chemical composition. The results show that there was a positive correlation between the absorbance values of the samples and the degree of concentration of the highly suitable habitat in Anning, Kunming, Chuxiong, Baoshan and Wenshan. However, there is an over-accumulation of heavy metal elements in individual production areas. It can be seen that <em>Boletus bainiugan</em> does vary considerably from one origin to another, and the identification of origin is of practical significance. The support vector machine (SVM) model is built to solve the problem of different origin traceability of <em>Boletus bainiugan</em>. The SVM model using normalization and second-order derivative method is found to be the fitting origin traceability model.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"145 ","pages":"Article 105690"},"PeriodicalIF":3.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097743","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

Enhancing thermal infrared image colorization through reference-driven and contrastive learning approaches

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

Infrared Physics & Technology

Pub Date : 2024-12-21 DOI: 10.1016/j.infrared.2024.105675

Weida Zhan , Mingkai Shi , Yu Chen , Jingwen Zhang , Cong Zhang , Deng Han

Thermal infrared image colorization remains challenging due to limitations in existing methods, such as insufficient detail preservation and inaccurate color rendering. This paper presents a novel colorization approach that leverages reference images and contrastive learning to address these issues. Our model employs a dual-encoder generator architecture, allowing for detailed feature extraction from both infrared and reference images to enable precise color transfer. Key modules, including the Multi-Receptive Field Feature Integration Module (MFIM) and Channel–Spatial Feature Enhancement Module (CSFEM), enhance feature extraction and integration, while the Improved Stop-Gradient Attention Module (ISGA) ensures accurate feature alignment. A composite loss function combining adversarial, perceptual, and contrastive losses further refines the model’s output. Experimental results on benchmark datasets show that this method significantly improves colorization quality, generating visually realistic and detailed images, thus advancing applications in post-processing, object detection, and scene analysis within the infrared domain.

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引用次数: 0