Infrared Physics & Technology最新文献

{"title":"Visible, infrared and laser multi-spectral compatible camouflage metasurface emitter based on ultrathin silver","authors":"Jiaxin Li ,&nbsp;Yiyang Shen ,&nbsp;Yimin Zhang ,&nbsp;Aoxue Guo ,&nbsp;Yufang Liu ,&nbsp;Shuwen Zheng ,&nbsp;Mengdan Qian","doi":"10.1016/j.infrared.2026.106419","DOIUrl":"10.1016/j.infrared.2026.106419","url":null,"abstract":"<div><div>Camouflage technology serves as a critical anti-reconnaissance tool, utilizing technical methods to minimize the likelihood of detection and identification by surveillance systems. Accordingly, the advancement of new camouflage technologies is a fundamental strategy to improving the survivability of military weapons and equipment. In this study, an optically transparent metasurface emitter consisting of Ag disk /SiO₂/Ag/ZnS is designed and fabricated by virtue of the inherent absorption characteristics of transparent dielectric films and ultrathin Ag layers. This configuration integrates multispectral camouflage, covering the visible, infrared, and laser ranges, with effective radiative heat management. Relative to a blackbody reference (T = 300 K), the fabricated emitter demonstrates infrared signal reduction rates of 79.5 % and 83.2 % in the 3–5 μm and 8–14 μm detection bands, respectively, together with enhanced absorption performance at the 10.6 μm operating wavelength of CO₂ laser radar. The ultrathin Ag film ensures excellent optical transparency while maintaining its other functionalities. Experimental verification confirms that the emitter effectively dissipates heat through the non-atmospheric windows under high temperatures, with significantly lower surface temperatures observed compared to conventional broadband low-emissivity materials, indicating excellent thermal management performance. This work proposes an innovative approach for achieving multispectral-compatible camouflage, offering substantial potential for applications in observation windows of military equipment and related fields.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106419"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023734","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}

{"title":"A novel encoded line laser array method of scanning infrared thermography nondestructive testing for CFRP defect","authors":"Rui Li ,&nbsp;Di Wu ,&nbsp;Yunsheng An ,&nbsp;Yucai Xie ,&nbsp;Hongpeng Zhang ,&nbsp;Jizhe Wang ,&nbsp;Wei Li ,&nbsp;Chenzhao Bai ,&nbsp;Chenyong Wang ,&nbsp;Li Sun","doi":"10.1016/j.infrared.2026.106397","DOIUrl":"10.1016/j.infrared.2026.106397","url":null,"abstract":"<div><div>This paper presents a novel encoded line laser array (ELLA) method for scanning infrared thermography nondestructive testing (IRT‑NDT) of carbon fiber reinforced plastics (CFRP). By spatially arranging multiple line lasers, ELLA generates Lock‑in like, frequency‑modulation like, and 13‑bit Barker‑coded pulse like (Barker13‑like) excitation waveforms, extending modulated thermography from static to scanning systems. Numerical simulations confirmed that ELLA heating profiles closely match static excitations. To process dynamic image sequences, pseudo‑static matrix reconstruction (PSMR) converts them into spatially static datasets, enabling established algorithms such as fast Fourier transform, thermal signal reconstruction, and matched filtering (MF). The algorithmic results demonstrated that, compared with single line laser scanning, the combination of ELLA with PSMR and post‑processing effectively improves defect detectability, even under added salt‑and‑pepper and Gaussian noise. Both the signal‑to‑clutter ratio and signal‑to‑noise ratio (SNR) evaluations confirm the effectiveness of ELLA, with overall values being improved by post‑processing algorithms. Barker13‑like excitation achieves consistently high SNR across most algorithms, and the moving average model of MF reached the maximum SNR of 12.91. Overall, ELLA integrates the rapid coverage of scanning IRT‑NDT with the sensitivity of modulated excitation, offering a feasible and efficient approach for large‑scale CFRP defect detection.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106397"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074068","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}

{"title":"DWPCNNFusion: Deep pulse-coupled neural networks incorporating Weber’s law for efficient infrared and visible image fusion","authors":"Jia Zhao ,&nbsp;Sirui Jia ,&nbsp;Jing Di ,&nbsp;Jing Lian ,&nbsp;Yide Ma ,&nbsp;Yuelan Xin ,&nbsp;Jisheng Dang ,&nbsp;Jizhao Liu","doi":"10.1016/j.infrared.2026.106417","DOIUrl":"10.1016/j.infrared.2026.106417","url":null,"abstract":"<div><div>Infrared and visible image fusion is a key task in computer vision, aiming to combine complementary multimodal information to generate a salient and texture-rich image. However, existing deep learning-based fusion methods typically rely on increasing network depth to enhance performance, often overlooking the significant computational resources required, which leads to inefficiency. To address this, we propose a novel brain-inspired, end-to-end trainable infrared and visible image fusion method (DWPCNNFusion). Specifically, in the feature extraction stage, we design a deep pulse-coupled neural networks based on Weber’s law (DWPCNN) , where the coupling weight matrix is treated as a learnable parameter, enabling the network to flexibly adapt to varying data characteristics. Additionally, linking strength coefficients are set according to Weber’s law, simulating the nonlinear perception of brightness in the human visual system, which effectively mitigates detail loss in low-light environments. To accommodate dynamic changes in input data over time, a time adaptive batch normalization method is proposed, and temporal information is integrated via a rate encoding scheme, allowing DWPCNN to be efficiently incorporated into existing deep learning frameworks. Furthermore, pulse convolutional dense blocks (PCDB) are employed to extract high-level semantic features, further enhancing the model’s feature representation capability. Experimental results on the TNO and MSRS datasets, compared with 15 representative methods using both objective and subjective metrics, demonstrate that the proposed method excels in detail preservation while achieving a better balance between computational efficiency and fusion performance.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106417"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074070","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}

{"title":"Noise influence on defect depth estimation in CFRP by One-Dimensional convolution neural network in Non-Linear frequency modulated thermal wave Imaging","authors":"Naga Prasanthi Yerneni ,&nbsp;S.S. Banda ,&nbsp;G.T. Vesala ,&nbsp;V.S. Ghali ,&nbsp;Fei Wang ,&nbsp;Junyan Liu ,&nbsp;A. Sridhar ,&nbsp;Ravibabu Mulaveesala","doi":"10.1016/j.infrared.2026.106398","DOIUrl":"10.1016/j.infrared.2026.106398","url":null,"abstract":"<div><div>Quantitative depth estimation of defects in composite structures using active infrared thermography is a challenging task and recent advancements promote deep learning (DL) techniques to achieve this in a quick and automated manner. However, noise present in thermal data at extreme inspection conditions degrades the performance of the DL models. This paper introduces a one-dimensional convolution neural network (1D-CNN) and analyzes its performance in different levels of noise for automatic depth estimation in Logarithmic Frequency Modulated Thermal Wave Imaging. The experimentation is conducted over two carbon fiber reinforced polymer (CFRP) specimens of different thickness and varying sizes and depths of flat-bottom hole defects. Including the original thermal data of one CFRP specimen, five datasets are prepared by adding additive white Gaussian noise of four levels: 5 dB, 10 dB, 15 dB and 20 dB and the proposed 1D-CNN model, named as M1 to M5 for each case, is individually trained and tested. Further, transfer learning is applied for identifying depths in the second CFRP structure. The results, along with performance metrics, indicate that the 1D-CNN presents more than 98 % and 95 % accuracy for training from scratch and transfer learning cases over original data, whereas it degrades as the noise level increases. Performance metrics such as accuracy, F-score and mean intersection of union demonstrate the defect depth estimation performance, including the defect boundary preservation under various noise conditions that are consistent with the accuracy of the model. In addition, the proposed 1D-CNN achieves the best quantitative performance, with consistently higher accuracy (≈95–98 %), F-score (≈0.85–0.95), and mean IoU (≈0.45–0.75) across all defect depths and noise levels, outperforming conventional DT, SVM, ANN, and 1D-ResNet, whose metrics drop sharply, for deeper defects and higher noise conditions.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106398"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397478","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}

{"title":"Infrared small target detection based on multiscale low-rankness and firm thresholding function","authors":"Xiaorun Li,&nbsp;Yuan Luo,&nbsp;Shuhan Chen","doi":"10.1016/j.infrared.2026.106439","DOIUrl":"10.1016/j.infrared.2026.106439","url":null,"abstract":"<div><div>Recently, infrared (IR) small target detection based on low-rank and sparse decomposition (LRSD) has gained increasing attention among civilian and military fields. Whereas, the LRSD-based methods face the challenges of accurately mathematically describing the intrinsic characteristics of each component and effectively separating targets from non-targets. This paper proposes a target detection model with multiscale low-rankness and firm thresholding function (MSR-FT). Specifically, a high-pass filtering spatial–temporal tensor corresponding to the original IR sequence is constructed. Secondly, we define a firm thresholding function-based norm for target characterization, leading to more accurate sparse target estimation. Meanwhile, using a multiscale low-rank background decomposition technique, we introduce a multiscale Log-based tensor nuclear norm, which ensures that the estimated background fully considers the global and local low-rank properties from different scales. Furthermore, with a posterior information feedback strategy, we propose a target detection method called MSR-FT. Through an optimization scheme based on the alternating direction method of multipliers (ADMM), it proves that MSR-FT exceeds seventeen competitive IR small target detection methods on six IR sequences from the perspectives of target detectability (TD), background suppressibility (BS), and overall performance.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106439"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397483","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}

{"title":"Early detection of tobacco leaf mildew using multi-attention enhanced 3D residual convolutional Neural network with hyperspectral imaging","authors":"Lei Zhang ,&nbsp;Jinsong Du ,&nbsp;Jiakang Li ,&nbsp;Chengyuan Li ,&nbsp;Jiandong Zhang ,&nbsp;Lianlian Wu","doi":"10.1016/j.infrared.2026.106375","DOIUrl":"10.1016/j.infrared.2026.106375","url":null,"abstract":"<div><div>Early detection of mildew in tobacco leaves is essential for maintaining product quality. While hyperspectral imaging (HSI) offers a non-destructive alternative with rich spectral–spatial information, but the high dimensionality of HSI and complex characteristics of early mildew pose significant challenges for conventional deep learning approach. In this article, we propose a novel multi-attention enhanced 3D Residual Convolutional Neural Network (3D-ResCNN) for early mildew detection of tobacco leaves using HSI data. First, the model employs 3D convolutions to simultaneously extract spatial and spectral features, while residual connections mitigate the vanishing gradient problem in deep networks. To improve mildew localization and spectral discrimination, a spatial–spectral attention module is integrated to selectively emphasize mildew-sensitive spatial regions and identify key spectral bands. Subsequently, a channel attention mechanism is introduced to adaptively reweight feature channels, thereby suppressing redundancy and emphasizing the most discriminative feature maps. Extensive experiments conducted on a real-world HSI tobacco dataset demonstrate that the proposed method achieves superior performance over traditional deep learning models in terms of accuracy and early-stage detection sensitivity, which validate the model’s effectiveness and superiority.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106375"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923453","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}

{"title":"Corrigendum to “Infrared small target detection fusion network based on singular value decomposition and rayleigh feature amplifier”. [Infrared Phys. Technol. 153(2026) 106353, ISSN 1350–4495, https://doi.org/10.1016/j.infrared.2025.106353]","authors":"Tianlei Ma ,&nbsp;Liang Fu ,&nbsp;Jinzhu Peng ,&nbsp;Fang-Lue Zhang ,&nbsp;Heng Zhang ,&nbsp;Xiangbo Feng ,&nbsp;Teng Wang ,&nbsp;Lu Xin","doi":"10.1016/j.infrared.2026.106390","DOIUrl":"10.1016/j.infrared.2026.106390","url":null,"abstract":"","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106390"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397026","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}

{"title":"Research on measurement accuracy correction for TDLAS-based methane leakage monitoring under environmental variations","authors":"Jinyu Li,&nbsp;Feng He,&nbsp;Xiaokang Liu,&nbsp;Fangting Liu,&nbsp;Junhui Li","doi":"10.1016/j.infrared.2026.106371","DOIUrl":"10.1016/j.infrared.2026.106371","url":null,"abstract":"<div><div>Tunable diode laser absorption spectroscopy (TDLAS), owing to its high selectivity, sensitivity, and fast response, has been widely employed for monitoring methane leakage in urban natural gas pipelines. However, variations in ambient temperature and pressure can alter the absorption spectral lines, thereby reducing the accuracy of concentration measurements. To address this issue and improve measurement reliability, a 1654 nm DFB laser was adopted as the light source, and methane (CH₄) at different concentrations was used as the target gas for testing under conditions of 263–323 K and 0.6–1.1 atm. To handle temperature effects, we developed two corrections: one for direct absorption spectroscopy (DAS) that integrates line-strength variation with a system-error compensation coefficient, and another for wavelength modulation spectroscopy (WMS) based on dual-peak combined intensity, while pressure effects were mitigated via a least-squares correction. The temperature correction reduced the maximum relative errors of DAS and WMS from about 30 % and 20 % to around 2 %, respectively, while the pressure correction decreased the maximum relative error from 3.69 % to 1.05 %. Allan deviation analysis indicated that the sensor achieved a minimum detection limit (MDL) of 4.41 ppm at an integration time of 30 s. In a 24-hour continuous monitoring test conducted under fluctuating temperature conditions, the maximum relative errors for measuring 1 × 10⁴ ppm CH₄, after applying the correction formulas, were reduced to 1.92 % for DAS and 0.84 % for WMS. This study provides a novel and effective approach to enhancing gas concentration measurement accuracy in urban natural gas pipeline leakage detection and related industrial applications.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"154 ","pages":"Article 106371"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923449","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}

{"title":"Self-Q-switching laser performance of Nd:ASL crystals at 1.3 μm","authors":"Wenfang Lin ,&nbsp;Conghui Huang ,&nbsp;Shulong Zhang ,&nbsp;Min Xu ,&nbsp;Siliang Tao ,&nbsp;Shanming Li ,&nbsp;Chengchun Zhao ,&nbsp;Qiannan Fang ,&nbsp;Xisheng Ye ,&nbsp;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_0.7Nd_0.05La_0.25Mg_0.3Al_11.7O₁₉ (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.</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}

{"title":"DSHANet: Dual-path sampling and hybrid attention network for infrared image destriping","authors":"Xue Li ,&nbsp;Hongying Zhang ,&nbsp;Lijun Yang ,&nbsp;Xi Yang ,&nbsp;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}