Search Results (195)

This paper is about the design and fabrication of infrared lenses, which are the core components of thermal imaging cameras to be mounted on vehicles. To produce an athermalized optical system, chalcogenide glass (As₄₀Se₆₀) with a lower thermo-optic coefficient (dn/dT) than germanium was adopted as a lens material, and each lens was designed so that defocus occurs in opposite directions depending on temperature. The designed lens was fabricated using a compression molding method, and the molded lenses showed less than 1.5 μm of form error (PV) using a mold iteration process. Through evaluations of MTF and thermal images obtained from the lens module, it was judged that this optical design process is obtainable. Full article

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article

To achieve rapid distance estimation and tracking of moving targets in a large field of view, this paper proposes an innovative simulation method. Using a low-cost approach, the imaging and distance measurement performance of the designed cooling-type mid-wave infrared compound-eye camera (CM-CECam) is experimentally evaluated. The compound-eye camera consists of a small-lens array with a spherical shell, a relay optical system, and a cooling-type mid-wave infrared detector. Based on the spatial arrangement of the small-lens array, a precise simulation imaging model for the compound-eye camera is developed, constructing a virtual imaging space. Distance estimation and error analysis for virtual targets are performed using the principle of stereo disparity. This universal simulation method provides a foundation for spatial design and image-plane adjustments for compound-eye cameras with specialized structures. Using the raw images captured by the compound-eye camera, a scene-specific piecewise linear mapping method is applied. This method significantly reduces the brightness contrast differences between sub-images during wide-field observations, enhancing image details. For the fast detection of moving targets, ommatidia clusters are defined as the minimal spatial constraint units. Local information at the centers of these constraint units is prioritized for processing. This approach replaces traditional global detection methods, improving the efficiency of subsequent processing. Finally, the simulated distance measurement results are validated using real-world scene data. Full article

This review paper presents a comprehensive analysis of electroluminescence (EL) imaging techniques for photovoltaic (PV) module diagnostics, focusing on advancements from conventional indoor imaging to outdoor and daylight EL imaging. It examines key challenges, including ambient light interference and environmental variability, and highlights innovations such as infrared-sensitive indium gallium arsenide (InGaAs) cameras, optical filtering, and periodic current modulation to enhance defect detection. The review also explores the role of artificial intelligence (AI)-driven methodologies, including deep learning and generative adversarial networks (GANs), in automating defect classification and performance assessment. Additionally, the emergence of drone-based EL imaging has facilitated large-scale PV inspections with improved efficiency. By synthesizing recent advancements, this paper underscores the critical role of EL imaging in ensuring PV module reliability, optimizing performance, and supporting the long-term sustainability of solar energy systems. Full article

Digital time-delay integration (TDI) enhances the signal-to-noise ratio (SNR) in infrared (IR) imaging, but its effectiveness in push-broom scanning is contingent upon maintaining a stable image shift velocity. Unpredictable image shifts and rotations, caused by carrier or scene movement, can affect the imaging process. This paper proposes an advanced technical approach for infrared multispectral TDI imaging. This methodology concurrently estimates the image shift and rotation between frames by utilizing a high-resolution visible camera aligned parallel to the optical axis of the IR camera. Subsequently, parameter prediction is conducted using the Kalman model, and real-time compensation is achieved by dynamically adjusting the infrared TDI integration unit based on the predicted parameters. Simulation and experimental results demonstrate that the proposed algorithm enhances the BRISQUE score of the TDI images by 21.37%, thereby validating its efficacy in push-scan imaging systems characterized by velocity-height ratios instability and varying camera attitudes. This research constitutes a significant contribution to the advancement of high-precision real-time compensation for image shift and rotation in infrared remote sensing and industrial inspection applications. Full article

Near-infrared imaging devices are extensively used in medical diagnosis, night vision, and security monitoring. However, existing traditional imaging devices rely on a bunch of refracting lenses, resulting in large, bulky imaging systems that restrict their broader utility. The emergence of flat meta-optics offers a potential solution to these limitations, but existing research on compact integrated devices based on near-infrared meta-optics is insufficient. In this study, we propose an integrated NIR imaging camera that utilizes large-size metalens with a silicon nanostructure with high transmission efficiency. Through the detection of target and animal and plant tissue samples, the ability to capture biological structures and their imaging performance was verified. Through further integration of the NIR imaging device, the device significantly reduces the size and weight of the system and optimizes the aperture to achieve excellent image brightness and contrast. Additionally, venous imaging of human skin shows the potential of the device for biomedical applications. This research has an important role in promoting the miniaturization and lightweight of near-infrared optical imaging devices, which is expected to be applied to medical testing and night vision imaging. Full article

Despite notable advancements in the development of borate materials, improving their luminescent efficiency remains an important focus in materials research. The synthesis of lanthanum borates (LaBO₃), doped and co-doped with europium (Eu³⁺) and dysprosium (Dy³⁺), by the solid-state method, has demonstrated significant potential to address this challenge due to their unique optical properties. These materials facilitate efficient energy transfer from UV-excited host crystals to trivalent rare-earth activators, resulting in stable and high-intensity luminescence. To better understand their structural and vibrational characteristics, Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy were employed to identify functional groups and molecular vibrations in the synthesized materials. Additionally, X-ray diffraction (XRD) analysis was conducted to determine the crystalline structure and phase composition of the samples. All observed transitions of Eu³⁺ and Dy³⁺ in the excitation and emission spectra were systematically analyzed and identified, providing a comprehensive understanding of their behavior. Although smartphone cameras exhibit non-uniform spectral responses, their integration into this study highlights distinct advantages, including contactless interrogation, effective UV excitation suppression, and real-time spectral analysis. These capabilities enable practical and portable fluorescence sensing solutions for applications in healthcare, environmental monitoring, and food safety. By combining advanced photonic materials with accessible smartphone technology, this work demonstrates a novel approach for developing low-cost, scalable, and innovative sensing platforms that address modern technological demands. Full article

Infrared deflectometry is an efficient and accurate measuring method for curved surfaces fabricated via grinding or finish milling. The emitting properties and geometrical configurations of the infrared light source is a core component governing the measurement performance. In this paper, an infrared slit light source is designed based on the cavity structure of a polyimide heating film. This design ensures good stability and uniformity of the light source whilst effectively reducing background noise. Additionally, the light source can be applied as a calibration board for calibrating infrared cameras. The light source is aligned using a theodolite and cubic prism to control the positional deviations during scanning. Experimental results demonstrate that the proposed slit light source and calibration method can achieve a measurement accuracy of 1 µm RMS, which can meet the needs of rapid measurement in grinding. This approach provides a reliable, cost-effective, and efficient tool for surface quality assessments in optical workshops and has a broad application potential. Full article

A multi-module staggered (MMS) long-linear-array (LLA) detector is presently recognized as an effective and widely adopted means of improving the field of view (FOV) of in-orbit optical line-array cameras. In particular, in terms of low-orbit whisk-broom payloads, the MMS LLA detector combined with the one-dimensional scanning mirror is capable of achieving both large-swath and high-resolution imaging. However, because of the complexity of the instantaneous relative motion model (IRMM) of the whisk-broom imaging mechanism, it is really difficult to determine and verify the actual numbers of overlapping pixels of adjacent detector sub-module images and consecutive images in the same and opposite scanning directions, which are exceedingly crucial to the instrument design pre-launch as well as the in-orbit geometric quantitative processing and application post-launch. Therefore, in this paper, aiming at addressing the problems above, we propose a global-scale overlapping pixels calculation method based on the IRMM and rigorous geometric positioning model (RGPM) of the whisk-broom payloads with an MMS LLA detector. First, in accordance with the imaging theory and the specific optical–mechanical structure, the RGPM of the whisk-broom payload is constructed and introduced elaborately. Then, we qualitatively analyze the variation tendency of the overlapping pixels of adjacent detector sub-module images with the IRMM of the imaging targets, and establish the associated overlapping pixels calculation model based on the RGPM. And subsequently, the global-scale overlapping pixels calculation models for consecutive images of the same and opposite scanning directions of the whisk-broom payload are also built. Finally, the corresponding verification method is presented in detail. The proposed method is validated using both simulation data and in-orbit payload data from the Thermal Infrared Spectrometer (TIS) of the Sustainable Development Goals Satellite-1 (SDGSAT-1), launched on 5 November 2021, demonstrating its effectiveness and accuracy with overlapping pixel errors of less than 0.3 pixels between sub-modules and less than 0.5 pixels between consecutive scanning images. Generally, this method is suitable and versatile for the other scanning cameras with a MMS LLA detector because of the similarity of the imaging mechanism. Full article

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R² = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R² = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. Full article

In this study, we developed and implemented a convolutional neural network (CNN) to predict thermal variations based on the modal distribution in LPFGs. An LPFG with a period of 450 µm and length of 22.5 mm was constructed in a few-mode optical fiber using a ${\mathrm{C}\mathrm{O}}_2$ laser etching technique. To train and verify the CNN-based model, a database of 355 empirically acquired near-field images corresponding to the ${\mathrm{L}\mathrm{P}}_{11}$ propagation modes was used. The images were captured with a WIDY SWIR 640 vs. infrared camera and a 980 nm laser. Similarly, the model’s hyperparameters were tuned using the computational tool Optuna, which improved its overall performance. The findings show that the constructed deep learning model can predict temperature with 98.5% accuracy over a range of 24 °C to 190 °C, with a maximum error of 3.77 °C. The root mean square error (RMSE) of the forecasts was 0.94 °C, indicating that the model was accurate. Finally, the inference time for a batch of 32 images was 0.055 s, confirming the effectiveness of the proposed approach. Full article

This paper deals with the experimental procedures of lock-in thermography (LIT) for polyetheretherketone (PEEK), which is used as a lightweight material in various industrial fields. The LIT has limitations due to non-uniform heating by external optic sources and the non-uniformity correction (NUC) of the infrared (IR) camera. It is generating unintended contrast in the IR image in thermal imaging inspection, reducing detection performance. In this study, the non-uniformity effect was primarily improved by producing an equivalent array halogen lamp. Then, we presented absolute temperature compensation (ATC) and temperature ratio compensation (TRC) techniques, which can equalize the thermal contrast of the test samples by compensating for them using reference samples. By applying compensation techniques to data acquired from the test samples, defect detectability improvement was quantitatively presented. In addition, binarization was performed and detection performance was verified by evaluating the roundness of the detected defects. As a result, the contrast of the IR image was greatly improved by applying the compensation technique. In particular, raw data were enhanced by up to 54% using the ATC compensation technique. Additionally, due to improved contrast, the signal-to-noise ratio (SNR) was improved by 7.93%, and the R² value of the linear trend equation exceeded 0.99, demonstrating improved proportionality between the defect condition and SNR. Full article

This study introduces a novel method for detecting and measuring transparent glass sheets using hyperspectral imaging (HSI). The main goal of this study is to create a conversion technique that can accurately display spectral information from collected images, particularly in the visible light spectrum (VIS) and near-infrared (NIR) areas. This technique enables the capture of relevant spectral data when used with images provided by industrial cameras. The next step in this investigation is using principal component analysis to examine the obtained hyperspectral images derived from different treated glass samples. This analytical procedure standardizes the magnitude of light wavelengths that are inherent in the HSI images. The simulated spectral profiles are obtained using the generalized inverse matrix technique on the normalized HSI images. These profiles are then matched with spectroscopic data obtained from microscopic imaging, resulting in the observation of distinct dispersion patterns. The novel use of images coloring methods effectively displays the thickness of the glass processing sheet in a visually noticeable way. Based on empirical research, changes in the thickness of the glass coating in the NIR-HSI range cause significant changes in the transmission of infrared light at different wavelengths within the NIR spectrum. This phenomenon serves as the foundation for the study of film thickness. The root mean square error inside the NIR area is impressively low, calculated to be just 0.02. This highlights the high level of accuracy achieved by the technique stated above. Potential areas of investigation that arise from this study are incorporating the proposed approach into the design of a real-time, wide-scale automated optical inspection system. Full article

Polar ring galaxies (PRGs) are unusual relative to common galaxies in that they consist of a central host galaxy—usually a gas-poor, early-type S0 or elliptical galaxy—surrounded by a ring of gas, dust and stars that orbit perpendicular to the major axis of the host. Despite the general quiescence of early-type galaxies (ETGs) and the rings’ lack of spiral density waves, PRGs are the sites of significant star formation relative to typical ETGs. To study these structures and improve PRG statistics, we obtained and analyzed infrared (IR) images from the Infrared Array Camera (IRAC) aboard the Spitzer Space Telescope, and combined these IR data with archival optical data from both the Sloan Digital Sky Survey and the Hubble Space Telescope, and with optical imaging data we obtained with the Gemini South Observatory. We performed structural decomposition and photometry for five PRGs, and fit the spectral energy distributions (SEDs) of each PRG component to estimate the stellar masses, ages, and other physical properties of the PRG components. We show that PRC B-12 and PRC B-22, both lacking previous analysis, obey trends commonly observed among PRGs. We find that the stellar masses of polar rings can be a significant fraction of the host galaxy’s stellar masses (∼10–30%). We note, however, that our estimates of stellar mass and other physical properties are the results of SED fitting and not direct measurements. Our findings corroborate both previous theoretical expectations and measurements of existing samples of PRGs and indicate the utility of SED fitting in the context of these unusual galaxies, which historically have lacked multi-wavelength photometry of their stellar components. Finally, we outline future improvements needed for more definitive studies of PRGs and their formation scenarios. Full article

Tunable laser spectroscopy (TLS) with infrared (IR) imaging is a powerful tool for gas leak detection. This study focuses on direct absorption spectroscopy (DAS) that utilizes wavelength modulation to extract gas information. A tunable interband cascade laser (ICL) with an optical power of 5 mW is periodically modulated by a sawtooth injection current at 10 Hz across the methane absorption around 3271 nm. A fast and sensitive thermal imaging camera for the mid-infrared range between 3 and 5.7 µm is operated at a frame rate of 470 Hz. Offline processing of image stacks is performed using different algorithms (DAS-F, DAS-f and DAS-2f) based on the Lambert–Beer law and the HITRAN database. These algorithms analyze various features of gas absorption, such as area (F), peak (f) and second derivative (2f) of the absorbance. The methane concentration in ppm*m is determined on a pixel-by-pixel analysis without calibration. Leak localization for methane leak rates as low as 0.25 mL/min is accurately displayed in a single concentration image with pixelwise sensitivities of approximately 1 ppm*m in a laboratory environment. Concentration image sequences represent the spatiotemporal dynamics of a gas plume with high contrast. The DAS-2f concept demonstrates promising characteristics, including accuracy, precision, 1/f noise rejection, simplicity and computational efficiency, expanding the applications of DAS. Full article