Pub Date : 2026-03-01Epub Date: 2025-12-29DOI: 10.1016/j.infrared.2025.106360
Wenfang Lin , Conghui Huang , Shulong Zhang , Min Xu , Siliang Tao , Shanming Li , Chengchun Zhao , Qiannan Fang , Xisheng Ye , Yin Hang
The self-Q-switching (SQS) laser performance on Nd-doped crystal at 1.3 μm has been reported for the first time, as far as is known. On Sr0.7Nd0.05La0.25Mg0.3Al11.7O19 (Nd:ASL) disorder crystal, a SQS dual-wavelength laser at 1339.9 and 1370.3 nm with output power up to 1.65 W was obtained under an absorbed pump power of 10.13 W with slope and optical-to-optical efficiencies of 22.3 % and 16.3 %, respectively. Furthermore, an on-surface optical axis quartz birefringent filter (BRF) was inserted in the V-folded cavity to tune the laser wavelength. Lasers at 1306.4, and approximately 1340, 1370, or 1391 nm were obtained. The experimental results indicated that σ polarization direction Nd:ASL is capable of producing dual-wavelength lasers at 1339.9 and 1370.3 nm, which was potential to be employed as the source of THz radiation. Besides, Nd:ASL crystals are enable to generate tunable lasers near 1370 and 1391 nm.
{"title":"Self-Q-switching laser performance of Nd:ASL crystals at 1.3 μm","authors":"Wenfang Lin , Conghui Huang , Shulong Zhang , Min Xu , Siliang Tao , Shanming Li , Chengchun Zhao , Qiannan Fang , Xisheng Ye , Yin Hang","doi":"10.1016/j.infrared.2025.106360","DOIUrl":"10.1016/j.infrared.2025.106360","url":null,"abstract":"<div><div>The self-Q-switching (SQS) laser performance on Nd-doped crystal at 1.3 μm has been reported for the first time, as far as is known. On Sr<sub>0.7</sub>Nd<sub>0.05</sub>La<sub>0.25</sub>Mg<sub>0.3</sub>Al<sub>11.7</sub>O<sub>19</sub> (Nd:ASL) disorder crystal, a SQS dual-wavelength laser at 1339.9 and 1370.3 nm with output power up to 1.65 W was obtained under an absorbed pump power of 10.13 W with slope and optical-to-optical efficiencies of 22.3 % and 16.3 %, respectively. Furthermore, an on-surface optical axis quartz birefringent filter (BRF) was inserted in the V-folded cavity to tune the laser wavelength. Lasers at 1306.4, and approximately 1340, 1370, or 1391 nm were obtained. The experimental results indicated that σ polarization direction Nd:ASL is capable of producing dual-wavelength lasers at 1339.9 and 1370.3 nm, which was potential to be employed as the source of THz radiation. Besides, Nd:ASL crystals are enable to generate tunable lasers near 1370 and 1391 nm.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106360"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923448","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 : 2026-03-01Epub Date: 2026-01-23DOI: 10.1016/j.infrared.2026.106421
Xue Li , Hongying Zhang , Lijun Yang , Xi Yang , Song Liu
Infrared images are often severely degraded by stripe noise, which significantly hinders subsequent image analysis and applications. To address the limitations of existing destriping methods in distinguishing noise from image details and modeling cross-scale feature correlations, this paper proposes a dual-path sampling and hybrid attention-based approach for infrared image destriping. The method implicitly splits feature branches through the designed residual dual-path downsampling module. One branch uses adaptive pooling to suppress stripe noise, while the other retains image edge details via grouped strided convolution. These two branches are fused using dynamic weights. Additionally, a hybrid attention module is proposed to separately capture noise patterns and structural features via 1 × 3 convolution and vertical strip attention, respectively, with a self-calibration branch adaptively modulating feature responses to suppress stripe noise while enhancing target integrity. Experiments demonstrate that the proposed method outperforms existing approaches on the INFRARED, ICSRN, CVC09, BSD68, and SIDD benchmark datasets, as well as real data. Specifically, it achieves an average Peak Signal-to-Noise Ratio of 37.96 dB across four typical stripe noise scenarios, surpassing the state-of-the-art method by 0.34 dB while effectively suppressing stripe noise.
{"title":"DSHANet: Dual-path sampling and hybrid attention network for infrared image destriping","authors":"Xue Li , Hongying Zhang , Lijun Yang , Xi Yang , Song Liu","doi":"10.1016/j.infrared.2026.106421","DOIUrl":"10.1016/j.infrared.2026.106421","url":null,"abstract":"<div><div>Infrared images are often severely degraded by stripe noise, which significantly hinders subsequent image analysis and applications. To address the limitations of existing destriping methods in distinguishing noise from image details and modeling cross-scale feature correlations, this paper proposes a dual-path sampling and hybrid attention-based approach for infrared image destriping. The method implicitly splits feature branches through the designed residual dual-path downsampling module. One branch uses adaptive pooling to suppress stripe noise, while the other retains image edge details via grouped strided convolution. These two branches are fused using dynamic weights. Additionally, a hybrid attention module is proposed to separately capture noise patterns and structural features via 1 × 3 convolution and vertical strip attention, respectively, with a self-calibration branch adaptively modulating feature responses to suppress stripe noise while enhancing target integrity. Experiments demonstrate that the proposed method outperforms existing approaches on the INFRARED, ICSRN, CVC09, BSD68, and SIDD benchmark datasets, as well as real data. Specifically, it achieves an average Peak Signal-to-Noise Ratio of 37.96 dB across four typical stripe noise scenarios, surpassing the state-of-the-art method by 0.34 dB while effectively suppressing stripe noise.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106421"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073973","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 : 2026-03-01Epub Date: 2026-01-14DOI: 10.1016/j.infrared.2026.106400
Huawei Jiang , Yiduo Zhu , Wanbao Sheng , Ruomeng Hu , Wenqiang Pi , Zhen Yang , Like Zhao
As one of the most important food crops worldwide, the accurate quality detection of wheat is a key link in safeguarding food security and food safety. Hyperspectral technology, as an effective method for quality detection, however, faces challenges in accurately determining critical quality indicators such as wheat deterioration degree due to the presence of massive redundant information. To address this issue, this study proposes a Spectral Clustering Dimensionality Reduction (SCDR) algorithm that integrates spectral angle similarity and spatial distance. First, the differences and similarities among various spectral features are quantitatively analyzed to construct the feature relationships between different bands. Second, based on these feature relationships, high-dimensional features are partitioned via clustering to generate feature clusters with dimensions far lower than those of the original data. Finally, weights are assigned according to the intra-cluster feature differences and similarities to calculate the representative feature values, thereby achieving dimensionality reduction. The experimental results demonstrate that the wheat quality detection model established based on the SCDR algorithm achieves an accuracy, precision, recall and F1-score of 0.9821, 0.9818, 0.9822 and 0.9818, respectively, on the test set, and its performance is significantly superior to that of other comparative models.
{"title":"Spectral clustering dimensionality reduction in wheat quality detection based on hyperspectral data","authors":"Huawei Jiang , Yiduo Zhu , Wanbao Sheng , Ruomeng Hu , Wenqiang Pi , Zhen Yang , Like Zhao","doi":"10.1016/j.infrared.2026.106400","DOIUrl":"10.1016/j.infrared.2026.106400","url":null,"abstract":"<div><div>As one of the most important food crops worldwide, the accurate quality detection of wheat is a key link in safeguarding food security and food safety. Hyperspectral technology, as an effective method for quality detection, however, faces challenges in accurately determining critical quality indicators such as wheat deterioration degree due to the presence of massive redundant information. To address this issue, this study proposes a Spectral Clustering Dimensionality Reduction (SCDR) algorithm that integrates spectral angle similarity and spatial distance. First, the differences and similarities among various spectral features are quantitatively analyzed to construct the feature relationships between different bands. Second, based on these feature relationships, high-dimensional features are partitioned via clustering to generate feature clusters with dimensions far lower than those of the original data. Finally, weights are assigned according to the intra-cluster feature differences and similarities to calculate the representative feature values, thereby achieving dimensionality reduction. The experimental results demonstrate that the wheat quality detection model established based on the SCDR algorithm achieves an accuracy, precision, recall and F1-score of 0.9821, 0.9818, 0.9822 and 0.9818, respectively, on the test set, and its performance is significantly superior to that of other comparative models.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106400"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023733","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 : 2026-03-01Epub Date: 2026-01-21DOI: 10.1016/j.infrared.2026.106416
Wei Cui , Shasha Liang , Yixuan Wang , Zhihui He
To realize a terahertz/infrared absorber with a simple architecture, high absorption efficiency, and multi-frequency tunability, a compact bilayer graphene metasurface absorber is proposed. The design integrates etched rectangular graphene strips on the SiO2 surface with a second continuous graphene layer embedded inside the dielectric, forming a five-frequency absorption metasurface, where the first four peaks exceed 98% (with two approaching 99%). Finite-difference time-domain (FDTD) simulations are used to examine the dependence of the absorption response on the polarization angle (PA) and the graphene Fermi level (Ef). Moreover, the interlayer spacing h is explored as a coupling parameter affecting the cavity confinement, while the resonance frequencies remain almost unchanged for incident angles up to 40°, showing excellent robustness to spacing variation and oblique illumination. Importantly, tuning PA and Ef enables five-channel switching with modulation depths (MD) above 99%, a minimum insertion loss (IL) of 0.0019 dB, and extinction ratios (ER) exceeding 20 dB, demonstrating outstanding multi-channel switching performance and strong application potential.
{"title":"Five-channel terahertz switching enabled by a bilayer graphene metasurface with dual tuning mechanisms","authors":"Wei Cui , Shasha Liang , Yixuan Wang , Zhihui He","doi":"10.1016/j.infrared.2026.106416","DOIUrl":"10.1016/j.infrared.2026.106416","url":null,"abstract":"<div><div>To realize a terahertz/infrared absorber with a simple architecture, high absorption efficiency, and multi-frequency tunability, a compact bilayer graphene metasurface absorber is proposed. The design integrates etched rectangular graphene strips on the SiO<sub>2</sub> surface with a second continuous graphene layer embedded inside the dielectric, forming a five-frequency absorption metasurface, where the first four peaks exceed 98% (with two approaching 99%). Finite-difference time-domain (FDTD) simulations are used to examine the dependence of the absorption response on the polarization angle (PA) and the graphene Fermi level (<em>E<sub>f</sub></em>). Moreover, the interlayer spacing <em>h</em> is explored as a coupling parameter affecting the cavity confinement, while the resonance frequencies remain almost unchanged for incident angles up to 40°, showing excellent robustness to spacing variation and oblique illumination. Importantly, tuning PA and <em>E<sub>f</sub></em> enables five-channel switching with modulation depths (<em>MD</em>) above 99%, a minimum insertion loss (<em>IL</em>) of 0.0019 dB, and extinction ratios (<em>ER</em>) exceeding 20 dB, demonstrating outstanding multi-channel switching performance and strong application potential.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106416"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023684","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 : 2026-03-01Epub Date: 2026-01-19DOI: 10.1016/j.infrared.2026.106405
Tianchen Ji , Haonan Liu , Jianguo Liu , ChunXiang Liu , FengFeng Jiang , Yibin Xie , Huaiying Fang , Jianhong Yang
Hyperspectral images (HSIs) and RGB multimodal information have proven effective for solid waste recognition. However, optimizing spectral band selection and feature extraction remains a challenge. This paper proposes an End-to-End Adaptive Fusion Network (E2E-AFNet) that integrates Dueling Double Deep Q Network (D3QN) with Near Infrared-RGB (NIR-RGB) feature extraction to achieve unified band selection and feature fusion. Using plastic waste as a case study, we design the Mask-D3QN SBS module to guide spectral input, which is processed by a multispectral feature extraction backbone. This backbone consists of a Multi-Scale Spectral Correlation Unit (MSC Unit) and a Multi-Scale Contour Feature Extraction Unit (MCF Unit), forming a dual-branch structure for feature decoupling. Additionally, the Mutual Attention Feature Interaction Module (MAFIM) efficiently fuses NIR-RGB features for object detection. A reward mechanism based on multimodal detection loss optimizes spectral input selection, enabling end-to-end adaptive fusion. Ablation results show that introducing the MSC and MCF modules improves the F1 score by 6.34 % and 6.45 %, respectively. Their joint use provides an additional ∼ 0.4 % gain, and incorporating the MAFIM module further increases the F1 score by 0.58 %. Further experiments show that the unified band-selection and fusion framework E2E-AFNet outperforms traditional methods, achieving an mAP of 90.48 % and an mAR of 90.87 %. By effectively combining band selection with multi-modal fusion, this approach enhances feature completeness and improves detection performance.
{"title":"E2E-AFNet: An End-to-End adaptive NIR-RGB fusion network applied to solid waste recognition","authors":"Tianchen Ji , Haonan Liu , Jianguo Liu , ChunXiang Liu , FengFeng Jiang , Yibin Xie , Huaiying Fang , Jianhong Yang","doi":"10.1016/j.infrared.2026.106405","DOIUrl":"10.1016/j.infrared.2026.106405","url":null,"abstract":"<div><div>Hyperspectral images (HSIs) and RGB multimodal information have proven effective for solid waste recognition. However, optimizing spectral band selection and feature extraction remains a challenge. This paper proposes an End-to-End Adaptive Fusion Network (E2E-AFNet) that integrates Dueling Double Deep Q Network (D3QN) with Near Infrared-RGB (NIR-RGB) feature extraction to achieve unified band selection and feature fusion. Using plastic waste as a case study, we design the Mask-D3QN SBS module to guide spectral input, which is processed by a multispectral feature extraction backbone. This backbone consists of a Multi-Scale Spectral Correlation Unit (MSC Unit) and a Multi-Scale Contour Feature Extraction Unit (MCF Unit), forming a dual-branch structure for feature decoupling. Additionally, the Mutual Attention Feature Interaction Module (MAFIM) efficiently fuses NIR-RGB features for object detection. A reward mechanism based on multimodal detection loss optimizes spectral input selection, enabling end-to-end adaptive fusion. Ablation results show that introducing the MSC and MCF modules improves the F1 score by 6.34 % and 6.45 %, respectively. Their joint use provides an additional ∼ 0.4 % gain, and incorporating the MAFIM module further increases the F1 score by 0.58 %. Further experiments show that the unified band-selection and fusion framework E2E-AFNet outperforms traditional methods, achieving an mAP of 90.48 % and an mAR of 90.87 %. By effectively combining band selection with multi-modal fusion, this approach enhances feature completeness and improves detection performance.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106405"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023685","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 : 2026-03-01Epub Date: 2026-01-01DOI: 10.1016/j.infrared.2025.106364
Jin Yang , Xiutao Yang , Jun Gou , Hang Yu , Zexu Wang , Yuchao Wei , Laijiang Wei , Chunyu Li , He Yu , Hongxi Zhou , Yun Zhou , Jun Wang
Research on photoconductive devices based on SeTe alloy remains limited, especially in compositional optimization, interface engineering, and scalable manufacturing techniques. Here, we present a Se0.3Te0.7 photoconductive detector optimized for 1550 nm wavelength through pre-metal annealing-enabled TeOx passivation. Annealing the Se0.3Te0.7 alloy prior to electrode deposition forms a TeOx interfacial layer that passivates surface states and reduces dark current by nearly an order of magnitude. The optimized device achieves an enhanced responsivity of 57.7 mA W−1 at −1 V bias with a 10 μm channel length, representing a 58.6 % improvement compared to traditional methods. Device performance is further tunable via channel length and bias voltage, with shorter channels demonstrating superior speed. This work presents a scalable, low-cost fabrication strategy for SeTe-based photodetectors, bridging the gap between material innovation and practical C-band applications in short-wave infrared (SWIR) detection.
基于SeTe合金的光导器件的研究仍然有限，特别是在成分优化，界面工程和可扩展的制造技术方面。在这里，我们提出了一个Se0.3Te0.7光导探测器，通过金属前退火使TeOx钝化，优化为1550 nm波长。在电极沉积之前，对Se0.3Te0.7合金进行退火，形成TeOx界面层，钝化表面状态并将暗电流降低近一个数量级。优化后的器件在- 1 V偏置和10 μm通道长度下的响应度提高到57.7 mA W−1，比传统方法提高了58.6%。器件性能通过通道长度和偏置电压进一步可调，更短的通道显示出更高的速度。这项工作提出了一种可扩展的、低成本的基于set的光电探测器制造策略，弥合了材料创新与短波红外（SWIR）探测中实际c波段应用之间的差距。
{"title":"TeOx interfacial passivation for Se0.3Te0.7 photoconductive detectors at 1550 nm","authors":"Jin Yang , Xiutao Yang , Jun Gou , Hang Yu , Zexu Wang , Yuchao Wei , Laijiang Wei , Chunyu Li , He Yu , Hongxi Zhou , Yun Zhou , Jun Wang","doi":"10.1016/j.infrared.2025.106364","DOIUrl":"10.1016/j.infrared.2025.106364","url":null,"abstract":"<div><div>Research on photoconductive devices based on SeTe alloy remains limited, especially in compositional optimization, interface engineering, and scalable manufacturing techniques. Here, we present a Se<sub>0.3</sub>Te<sub>0.7</sub> photoconductive detector optimized for 1550 nm wavelength through pre-metal annealing-enabled TeO<sub>x</sub> passivation. Annealing the Se<sub>0.3</sub>Te<sub>0.7</sub> alloy prior to electrode deposition forms a TeO<sub>x</sub> interfacial layer that passivates surface states and reduces dark current by nearly an order of magnitude. The optimized device achieves an enhanced responsivity of 57.7 mA W<sup>−1</sup> at −1 V bias with a 10 μm channel length, representing a 58.6 % improvement compared to traditional methods. Device performance is further tunable via channel length and bias voltage, with shorter channels demonstrating superior speed. This work presents a scalable, low-cost fabrication strategy for SeTe-based photodetectors, bridging the gap between material innovation and practical C-band applications in short-wave infrared (SWIR) detection.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106364"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923446","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 : 2026-03-01Epub Date: 2026-01-15DOI: 10.1016/j.infrared.2026.106402
Zhen Guo , Yifei Qin , Xijun Shao , Fernando A. Auat-Cheein , Lianming Xia , Yemin Guo , Xia Sun , Fangling Du
Peanut kernels are highly susceptible to Aspergillus flavus contamination, posing significant food safety risks due to aflatoxin B1 accumulation. This study applied infrared hyperspectral imaging, specifically covering visible-near infrared (VNIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm), to investigate the micro-interaction mechanisms between Aspergillus flavus and peanut kernels, focusing on spatio-temporal nutrient consumption and toxin accumulation. Infrared-based generalized two-dimensional correlation spectroscopy revealed a phased nutrient utilization strategy employed by Aspergillus flavus, identifying critical contamination phases at day 3 and day 5. An innovative attentive spatial-spectral synergy network (AS3Net) integrated with a novel bi-dimensional focus ripple module (BFRM), and significantly enhanced the prediction accuracy of moisture, protein, and oil contents in peanut kernels, achieving coefficient of determination of validation values of 0.932, 0.859, and 0.786, respectively. Ablation experiments highlighted that the combined use of spatial discovery, spectral insight modules, and dual fusion strategies improved model robustness, especially in predicting moisture content. Additionally, the AS3Net-BFRM framework provided a rapid, accurate classification of fungal-contaminated kernels with 100% accuracy. This advanced infrared hyperspectral imaging and deep learning approach presents a scalable, non-destructive, and efficient solution for real-time fungal contamination detection, which is crucial for enhancing food safety and managing aflatoxin risks in agricultural products.
{"title":"Infrared hyperspectral imaging integrated with an attentive spatial-spectral neural network for precise postharvest detection of Aspergillus flavus contamination and nutrient variations in peanut kernels","authors":"Zhen Guo , Yifei Qin , Xijun Shao , Fernando A. Auat-Cheein , Lianming Xia , Yemin Guo , Xia Sun , Fangling Du","doi":"10.1016/j.infrared.2026.106402","DOIUrl":"10.1016/j.infrared.2026.106402","url":null,"abstract":"<div><div>Peanut kernels are highly susceptible to <em>Aspergillus flavus</em> contamination, posing significant food safety risks due to aflatoxin B<sub>1</sub> accumulation. This study applied infrared hyperspectral imaging, specifically covering visible-near infrared (VNIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm), to investigate the micro-interaction mechanisms between <em>Aspergillus flavus</em> and peanut kernels, focusing on spatio-temporal nutrient consumption and toxin accumulation. Infrared-based generalized two-dimensional correlation spectroscopy revealed a phased nutrient utilization strategy employed by <em>Aspergillus flavus</em>, identifying critical contamination phases at day 3 and day 5. An innovative attentive spatial-spectral synergy network (AS3Net) integrated with a novel bi-dimensional focus ripple module (BFRM), and significantly enhanced the prediction accuracy of moisture, protein, and oil contents in peanut kernels, achieving coefficient of determination of validation values of 0.932, 0.859, and 0.786, respectively. Ablation experiments highlighted that the combined use of spatial discovery, spectral insight modules, and dual fusion strategies improved model robustness, especially in predicting moisture content. Additionally, the AS3Net-BFRM framework provided a rapid, accurate classification of fungal-contaminated kernels with 100% accuracy. This advanced infrared hyperspectral imaging and deep learning approach presents a scalable, non-destructive, and efficient solution for real-time fungal contamination detection, which is crucial for enhancing food safety and managing aflatoxin risks in agricultural products.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106402"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023728","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 : 2026-03-01Epub Date: 2026-01-05DOI: 10.1016/j.infrared.2026.106373
Wenjie Zhao , Sen Yang , Haoyu Wu , Jingmin Dai
In the process of high-temperature kiln ironmaking, molten iron temperature is an important parameter affecting yield and safety, and real-time accurate monitoring is crucial. However, the complexity of kiln conditions and serious environmental interference lead to the low accuracy of traditional temperature measurement, which is difficult to meet the demand for accurate control. To solve this problem, this paper proposes a data fitting method using the Gray Wolf Optimization (GWO) algorithm to optimize the Radial Basis Neural Network (RBFNN) based on the sapphire fiber optic temperature measurement system in order to improve the temperature measurement accuracy. The gray wolf algorithm optimizes the radial basis neural network center, width and weights by simulating the hunting behavior of gray wolves, and adaptively adjusts the parameters with the goal of minimizing the prediction error, which avoids the overfitting of the neural network and improves the model accuracy. The experimental results show that compared with the traditional algorithm, the proposed GWO-RBFNN method reduces the RMSE of iron water temperature prediction by 64%, MAE by 73%, and R2 is improved to 0.9992, which further improves the prediction accuracy and training stability.
{"title":"Application of the GWO-RBFNN algorithm for data fitting in a sapphire fiber temperature measurement system","authors":"Wenjie Zhao , Sen Yang , Haoyu Wu , Jingmin Dai","doi":"10.1016/j.infrared.2026.106373","DOIUrl":"10.1016/j.infrared.2026.106373","url":null,"abstract":"<div><div>In the process of high-temperature kiln ironmaking, molten iron temperature is an important parameter affecting yield and safety, and real-time accurate monitoring is crucial. However, the complexity of kiln conditions and serious environmental interference lead to the low accuracy of traditional temperature measurement, which is difficult to meet the demand for accurate control. To solve this problem, this paper proposes a data fitting method using the Gray Wolf Optimization (GWO) algorithm to optimize the Radial Basis Neural Network (RBFNN) based on the sapphire fiber optic temperature measurement system in order to improve the temperature measurement accuracy. The gray wolf algorithm optimizes the radial basis neural network center, width and weights by simulating the hunting behavior of gray wolves, and adaptively adjusts the parameters with the goal of minimizing the prediction error, which avoids the overfitting of the neural network and improves the model accuracy. The experimental results show that compared with the traditional algorithm, the proposed GWO-RBFNN method reduces the RMSE of iron water temperature prediction by 64%, MAE by 73%, and R<sup>2</sup> is improved to 0.9992, which further improves the prediction accuracy and training stability.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106373"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023729","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 : 2026-03-01Epub Date: 2026-01-19DOI: 10.1016/j.infrared.2026.106413
Disheng Wei , Fei Wang , Lulu Gao , Zhuang Jin , Wenlong Li , Jun Meng , Gaoyou Liu , Zhaojun Liu
In this paper, we demonstrated a high-efficiency, high-energy, and widely tunable mid-infrared optical vortex parametric oscillator based on a ZnGeP2 (ZGP) crystal, pumped by a 2.05 μm first-order vortex laser operating at a repetition rate of 1 kHz. The oscillator generated a signal beam that consistently carried orbital angular momentum (OAM) across a continuous tuning range from 3.57 to 4.02 μm, while the corresponding idler beam remained OAM-free and exhibited a Gaussian-like intensity profile. At a pump pulse energy of 6.4 mJ, the signal vortex beam achieved a maximum output energy of 2.7 mJ at a central wavelength of 3.86 μm, corresponding to an optical‑to‑optical conversion efficiency (OOCE) exceeding 42 %, which clearly demonstrated efficient OAM transfer from the pump to the signal beam. At maximum output, the signal vortex beam exhibited beam quality factors M2 of 2.7 and 2.6 in the x and y directions, respectively.
{"title":"High-efficiency multi-millijoule mid-infrared optical vortex parametric oscillator based on a ZGP crystal","authors":"Disheng Wei , Fei Wang , Lulu Gao , Zhuang Jin , Wenlong Li , Jun Meng , Gaoyou Liu , Zhaojun Liu","doi":"10.1016/j.infrared.2026.106413","DOIUrl":"10.1016/j.infrared.2026.106413","url":null,"abstract":"<div><div>In this paper, we demonstrated a high-efficiency, high-energy, and widely tunable mid-infrared optical vortex parametric oscillator based on a ZnGeP<sub>2</sub> (ZGP) crystal, pumped by a 2.05 μm first-order vortex laser operating at a repetition rate of 1<!--> <!-->kHz. The oscillator generated a signal beam that consistently carried orbital angular momentum (OAM) across a continuous tuning range from 3.57 to 4.02 μm, while the corresponding idler beam remained OAM-free and exhibited a Gaussian-like intensity profile. At a pump pulse energy of 6.4 mJ, the signal vortex beam achieved a maximum output energy of 2.7 mJ at a central wavelength of 3.86 μm, corresponding to an optical‑to‑optical conversion efficiency (OOCE) exceeding 42 %, which clearly demonstrated efficient OAM transfer from the pump to the signal beam. At maximum output, the signal vortex beam exhibited beam quality factors <em>M</em><sup>2</sup> of 2.7 and 2.6 in the <em>x</em> and <em>y</em> directions, respectively.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106413"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023736","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 : 2026-03-01Epub Date: 2026-02-04DOI: 10.1016/j.infrared.2026.106451
Mengyuan Tao , Kai Che , Jiaqi Liang , Yun Zhou , Jiayuan Gong , Jian Lv
Infrared image super-resolution (IRSR) aims to generate high-resolution thermal images from low-resolution inputs while preserving structural and textural information. However, infrared images often suffer from low contrast, sparse textures, and severe attenuation of high-frequency details, which poses significant challenges for accurate reconstruction. To address these challenges, we propose a lightweight network named Structure–Texture Collaborative Learning for Infrared Image Super-Resolution via Dual-Domain Modulation (STDM-Net). Its core Structural–Textural Hybrid Block (STHB) disentangles structural and textural features via a dual-branch design. The structural branch employs the Efficient Depthwise Large Kernel Attention (EDLKA) for long-range dependencies, while the texture branch leverages Dual-domain Frequency Modulation (DFM) to enhance high-frequency details. Multi-scale Dilated Guided Edge (MDGE) provides stable edge guidance, and a gated fusion mechanism adaptively integrates spatial, frequency, and edge cues. Extensive experiments on benchmark infrared datasets demonstrate that the proposed network achieves superior reconstruction accuracy and visual quality.
{"title":"Structure–texture collaborative learning for infrared image super-resolution via dual-domain modulation","authors":"Mengyuan Tao , Kai Che , Jiaqi Liang , Yun Zhou , Jiayuan Gong , Jian Lv","doi":"10.1016/j.infrared.2026.106451","DOIUrl":"10.1016/j.infrared.2026.106451","url":null,"abstract":"<div><div>Infrared image super-resolution (IRSR) aims to generate high-resolution thermal images from low-resolution inputs while preserving structural and textural information. However, infrared images often suffer from low contrast, sparse textures, and severe attenuation of high-frequency details, which poses significant challenges for accurate reconstruction. To address these challenges, we propose a lightweight network named Structure–Texture Collaborative Learning for Infrared Image Super-Resolution via Dual-Domain Modulation (STDM-Net). Its core Structural–Textural Hybrid Block (STHB) disentangles structural and textural features via a dual-branch design. The structural branch employs the Efficient Depthwise Large Kernel Attention (EDLKA) for long-range dependencies, while the texture branch leverages Dual-domain Frequency Modulation (DFM) to enhance high-frequency details. Multi-scale Dilated Guided Edge (MDGE) provides stable edge guidance, and a gated fusion mechanism adaptively integrates spatial, frequency, and edge cues. Extensive experiments on benchmark infrared datasets demonstrate that the proposed network achieves superior reconstruction accuracy and visual quality.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106451"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397537","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}
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