Daily Papers

Automatic Target Detection (ATD) and Recognition (ATR) from Thermal Infrared (TI) imagery in the defense and surveillance domain is a challenging computer vision (CV) task in comparison to the commercial autonomous vehicle perception domain. Limited datasets, peculiar domain-specific and TI modality-specific challenges, i.e., limited hardware, scale invariance issues due to greater distances, deliberate occlusion by tactical vehicles, lower sensor resolution and resultant lack of structural information in targets, effects of weather, temperature, and time of day variations, and varying target to clutter ratios all result in increased intra-class variability and higher inter-class similarity, making accurate real-time ATR a challenging CV task. Resultantly, contemporary state-of-the-art (SOTA) deep learning architectures underperform in the ATR domain. We propose a modified anchor-based single-stage detector, called YOLOatr, based on a modified YOLOv5s, with optimal modifications to the detection heads, feature fusion in the neck, and a custom augmentation profile. We evaluate the performance of our proposed model on a comprehensive DSIAC MWIR dataset for real-time ATR over both correlated and decorrelated testing protocols. The results demonstrate that our proposed model achieves state-of-the-art ATR performance of up to 99.6%.

Thermal imaging is crucial for scene understanding, particularly in low-light and nighttime conditions. However, collecting large thermal datasets is costly and labor-intensive due to the specialized equipment required for infrared image capture. To address this challenge, researchers have explored visible-to-thermal image translation. Most existing methods rely on Generative Adversarial Networks (GANs) or Diffusion Models (DMs), treating the task as a style transfer problem. As a result, these approaches attempt to learn both the modality distribution shift and underlying physical principles from limited training data. In this paper, we propose F-ViTA, a novel approach that leverages the general world knowledge embedded in foundation models to guide the diffusion process for improved translation. Specifically, we condition an InstructPix2Pix Diffusion Model with zero-shot masks and labels from foundation models such as SAM and Grounded DINO. This allows the model to learn meaningful correlations between scene objects and their thermal signatures in infrared imagery. Extensive experiments on five public datasets demonstrate that F-ViTA outperforms state-of-the-art (SOTA) methods. Furthermore, our model generalizes well to out-of-distribution (OOD) scenarios and can generate Long-Wave Infrared (LWIR), Mid-Wave Infrared (MWIR), and Near-Infrared (NIR) translations from the same visible image. Code: https://github.com/JayParanjape/F-ViTA/tree/master.

Quantifying exhaled CO2 from free-roaming cattle is both a direct indicator of rumen metabolic state and a prerequisite for farm-scale carbon accounting, yet no existing system can deliver continuous, spatially resolved measurements without physical confinement or contact. We present TRACE (Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock), the first unified framework to jointly address per-frame CO2 plume segmentation and clip-level emission flux classification from mid-wave infrared (MWIR) thermal video. TRACE contributes three domain-specific advances: a Thermal Gas-Aware Attention (TGAA) encoder that incorporates per-pixel gas intensity as a spatial supervisory signal to direct self-attention toward high-emission regions at each encoder stage; an Attention-based Temporal Fusion (ATF) module that captures breath-cycle dynamics through structured cross-frame attention for sequence-level flux classification; and a four-stage progressive training curriculum that couples both objectives while preventing gradient interference. Benchmarked against fifteen state-of-the-art models on the CO2 Farm Thermal Gas Dataset, TRACE achieves an mIoU of 0.998 and the best result on every segmentation and classification metric simultaneously, outperforming domain-specific gas segmenters with several times more parameters and surpassing all baselines in flux classification. Ablation studies confirm that each component is individually essential: gas-conditioned attention alone determines precise plume boundary localization, and temporal reasoning is indispensable for flux-level discrimination. TRACE establishes a practical path toward non-invasive, continuous, per-animal CO2 monitoring from overhead thermal cameras at commercial scale. Codes are available at https://github.com/taminulislam/trace.

To meet the challenges of high-resolution molecular spectroscopy, increasingly sophisticated spectroscopic techniques were developed. For a long time FTIR and laser-based spectroscopies were used for these studies. The recent development of dual-comb spectroscopy at high-resolution makes this technique a powerful tool for gas phase studies. We report on the use and characterization of the IRis-F1, a tabletop mid-infrared dual-comb spectrometer, in the newly developed step-sweep mode. The resolution of the wavenumber axis is increased by step-wise tuning (interleaving) and accurate measurement of the laser center wavelength and repetition frequency. Doppler limited measurements of N2O and CH4 reveal a wavenumber accuracy of 1E-4 cm-1 on the covered range of > 50 cm-1. Measured half-widths of absorption lines show no systematic broadening, indicating a negligible instrument response function. Finally, measurements of nitrogen pressure broadening coefficients in the v4 band of methane show that quantum cascade laser dual-comb spectroscopy in step-sweep mode is well adapted for measurements of precision spectroscopic data, in particular line shape parameters.

S255N is a luminous far-infrared source that contains many indications of active star formation but lacks a prominent near-infrared stellar cluster. We present mid-infrared through radio observations aimed at exploring the evolutionary state of this region. Our observations include 1.3mm continuum and spectral line data from the Submillimeter Array, VLA 3.6cm continuum and 1.3cm water maser data, and multicolor IRAC images from the Spitzer Space Telescope. The cometary morphology of the previously-known UCHII region G192.584-0.041 is clearly revealed in our sensitive, multi-configuration 3.6cm images. The 1.3mm continuum emission has been resolved into three compact cores, all of which are dominated by dust emission and have radii < 7000AU. The mass estimates for these cores range from 6 to 35 Msun. The centroid of the brightest dust core (SMA1) is offset by 1.1'' (2800 AU) from the peak of the cometary UCHII region and exhibits the strongest HC3N, CN, and DCN line emission in the region. SMA1 also exhibits compact CH3OH, SiO, and H2CO emission and likely contains a young hot core. We find spatial and kinematic evidence that SMA1 may contain further multiplicity, with one of the components coincident with a newly-detected H2O maser. There are no mid-infrared point source counterparts to any of the dust cores, further suggesting an early evolutionary phase for these objects. The dominant mid-infrared emission is a diffuse, broadband component that traces the surface of the cometary UCHII region but is obscured by foreground material on its southern edge. An additional 4.5 micron linear feature emanating to the northeast of SMA1 is aligned with a cluster of methanol masers and likely traces a outflow from a protostar within SMA1. Our observations provide direct evidence that S255N is forming a cluster of intermediate to high-mass stars.

In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical designs for acquiring hyperspectral images, there is a growing interest in interferometric spectral imaging systems based on division of aperture. These systems have the advantage of capturing snapshot acquisitions while maintaining a compact design. However, they require a careful calibration to operate properly. In this work, we present the interferometer response characterization algorithm (IRCA), a robust three-step procedure designed to characterize the transmittance response of multi-aperture imaging spectrometers based on the interferometry of Fabry-Perot. Additionally, we propose a formulation of the image formation model for such devices suitable to estimate the parameters of interest by considering the model under various regimes of finesse. The proposed algorithm processes the image output obtained from a set of monochromatic light sources and refines the results using nonlinear regression after an ad-hoc initialization. Through experimental analysis conducted on four different prototypes from the Image SPectrometer On Chip (ImSPOC) family, we validate the performance of our approach for characterization. The associated source code for this paper is available at https://github.com/danaroth83/irca.

The polycyclic aromatic hydrocarbon (PAH) hypothesis proposes that the aromatic infrared bands (AIBs) observed at 3.3, 6.2, 7.7, 8.6, 11.3, and 12.7 mic originate from gas-phase PAH molecules. These bands exhibit consistent peak wavelengths and profiles in diverse sources, and ISO SWS and JWST spectra show a nearly identical red wing of the 3.3 mic AIB and blue wing of the 11.2 mic AIB in the dominant Class A sources. This spectral uniformity suggests that the AIBs arise from a small, well-defined set of gas phase PAH species, regardless of the excitation conditions or the nature of the source such as HII regions, reflection nebulae, planetary nebula, young stellar objects, or the diffuse interstellar medium. However, a small number of gas phase PAH species is inconsistent with current modeling of the AIBs that require a wide range of PAH types and sizes. It is also inconsistent with the lack of observed UV and optical absorption bands from gas phase PAH molecules. Furthermore, there is no plausible formation pathway to produce only a small number of specific PAH molecules in the interstellar medium. These issues require quantitative investigation in order to definitively establish gas-phase PAH molecules as the carrier of the AIBs.

Materials combining both a high refractive index and a wide band gap are of great interest for optoelectronic and sensor applications. However, these two properties are typically described by an inverse correlation with high refractive index appearing in small gap materials and vice-versa. Here, we conduct a first-principles high-throughput study on more than 4000 semiconductors (with a special focus on oxides). Our data confirm the general inverse trend between refractive index and band gap but interesting outliers are also identified. The data are then analyzed through a simple model involving two main descriptors: the average optical gap and the effective frequency. The former can be determined directly from the electronic structure of the compounds, but the latter cannot. This calls for further analysis in order to obtain a predictive model. Nonetheless, it turns out that the negative effect of a large band gap on the refractive index can counterbalanced in two ways: (i) by limiting the difference between the direct band gap and the average optical gap which can be realized by a narrow distribution in energy of the optical transitions and (ii) by increasing the effective frequency which can be achieved through either a high number of transitions from the top of the valence band to the bottom of the conduction or a high average probability for these transitions. Focusing on oxides, we use our data to investigate how the chemistry influences this inverse relationship and rationalize why certain classes of materials would perform better. Our findings can be used to search for new compounds in many optical applications both in the linear and non-linear regime (waveguides, optical modulators, laser, frequency converter, etc.).

Infrared imaging technology has gained significant attention for its reliable sensing ability in low visibility conditions, prompting many studies to convert the abundant RGB images to infrared images. However, most existing image translation methods treat infrared images as a stylistic variation, neglecting the underlying physical laws, which limits their practical application. To address these issues, we propose a Physics-Informed Diffusion (PID) model for translating RGB images to infrared images that adhere to physical laws. Our method leverages the iterative optimization of the diffusion model and incorporates strong physical constraints based on prior knowledge of infrared laws during training. This approach enhances the similarity between translated infrared images and the real infrared domain without increasing extra training parameters. Experimental results demonstrate that PID significantly outperforms existing state-of-the-art methods. Our code is available at https://github.com/fangyuanmao/PID.

Thermal imaging has a variety of applications, from agricultural monitoring to building inspection to imaging under poor visibility, such as in low light, fog, and rain. However, reconstructing thermal scenes in 3D presents several challenges due to the comparatively lower resolution and limited features present in long-wave infrared (LWIR) images. To overcome these challenges, we propose a unified framework for scene reconstruction from a set of LWIR and RGB images, using a multispectral radiance field to represent a scene viewed by both visible and infrared cameras, thus leveraging information across both spectra. We calibrate the RGB and infrared cameras with respect to each other, as a preprocessing step using a simple calibration target. We demonstrate our method on real-world sets of RGB and LWIR photographs captured from a handheld thermal camera, showing the effectiveness of our method at scene representation across the visible and infrared spectra. We show that our method is capable of thermal super-resolution, as well as visually removing obstacles to reveal objects that are occluded in either the RGB or thermal channels. Please see https://yvette256.github.io/thermalnerf for video results as well as our code and dataset release.

Infrared imaging is essential for autonomous driving and robotic operations as a supportive modality due to its reliable performance in challenging environments. Despite its popularity, the limitations of infrared cameras, such as low spatial resolution and complex degradations, consistently challenge imaging quality and subsequent visual tasks. Hence, infrared image super-resolution (IISR) has been developed to address this challenge. While recent developments in diffusion models have greatly advanced this field, current methods to solve it either ignore the unique modal characteristics of infrared imaging or overlook the machine perception requirements. To bridge these gaps, we propose DifIISR, an infrared image super-resolution diffusion model optimized for visual quality and perceptual performance. Our approach achieves task-based guidance for diffusion by injecting gradients derived from visual and perceptual priors into the noise during the reverse process. Specifically, we introduce an infrared thermal spectrum distribution regulation to preserve visual fidelity, ensuring that the reconstructed infrared images closely align with high-resolution images by matching their frequency components. Subsequently, we incorporate various visual foundational models as the perceptual guidance for downstream visual tasks, infusing generalizable perceptual features beneficial for detection and segmentation. As a result, our approach gains superior visual results while attaining State-Of-The-Art downstream task performance. Code is available at https://github.com/zirui0625/DifIISR

We present a new method for generating emission spectra from polycyclic aromatic hydrocarbons (PAHs) in arbitrary radiation fields. We utilize the single-photon limit for PAH heating and emission to treat individual photon absorptions as independent events. This allows the construction of a set of single-photon emission "basis spectra" that can be scaled to produce an output emission spectrum given any input heating spectrum. We find that this method produces agreement with PAH emission spectra computed accounting for multi-photon effects to within simeq10% in the 3-20~{rm mu m} wavelength range for radiation fields with intensity U<100. We use this framework to explore the dependence of PAH band ratios on the radiation field spectrum across grain sizes, finding in particular a strong dependence of the 3.3 to 11.2~mum band ratio on radiation field hardness. A Python-based tool and a set of basis spectra that can be used to generate these emission spectra are made publicly available.

Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods.

High-accuracy microwave sensing is widely demanded in various fields, ranging from cosmology to microwave quantum technology. Quantum receivers based on inorganic solid-state spin systems are promising candidates for such purpose because of the stability and compatibility, but their best sensitivity is currently limited to a few pT/rm{Hz}. Here, by utilising an enhanced readout scheme with the state-of-the-art solid-state maser technology, we develop a robust microwave quantum receiver functioned by organic molecular spins at ambient conditions. Owing to the maser amplification, the sensitivity of the receiver achieves 6.14 pm 0.17 fT/rm{Hz} which exceeds three orders of magnitude than that of the inorganic solid-state quantum receivers. The heterodyne detection without additional local oscillators improves bandwidth of the receiver and allows frequency detection. The scheme can be extended to other solid-state spin systems without complicated control pulses and thus enables practical applications such as electron spin resonance spectroscopy, dark matter searches, and astronomical observations.

Carbonaceous nano-grains are a significant component of interstellar dust and dominate the mid-infrared emission of photodissociation regions (PDRs). We study the evolution of nano-grains across the illuminated edge of the Horsehead PDR, especially their abundance and size properties. This work is part of the Physics and Chemistry of PDR Fronts program studying dust and gas in PDRs with JWST. We use NIRCam+MIRI photometric bands and NIRSpec+MRS spectroscopy to map the illuminated edge. We model dust emission using the THEMIS dust model with the SOC radiative transfer code. Detailed modeling of high angular resolution JWST data allows us to obtain constraints on nano-grain properties. We find that diffuse ISM dust cannot account for the observed data, requiring evolved grains. A sharp density increase is observed at the illuminated edge, consistent with ALMA observations revealing a sharp transition between molecular and ionized gas. Although the PDR length could not be directly determined, we estimate an upper limit of approximately 0.015 pc. This implies a lower limit on small grain abundance (greater than 0.003), showing small grains are not depleted at the Horsehead edge, unlike in the Orion Bar. Our findings indicate a high-density environment and less steep size distribution for nano-grains at the illuminated edge versus the diffuse ISM. This implies nano-grain destruction mechanisms might be less efficient in the Horsehead's moderate-UV field than in more intense PDRs. These results support a model where nano-grain population recovery is slower in moderate-UV environments, leading to a unique dust size distribution at the edge of the Horsehead Nebula.

The development of advanced diagnostic systems to measure and optimize emerging energetic material performance is critical for the defeat of Chemical Warfare Agents (CWA). This study presents an integrated multi-spectroscopic approach to monitor the interaction between a CWA simulant, Diisopropyl Methyl Phosphonate (DIMP), and combusting composite metal particles. A custom benchtop Polygonal Rotating Mirror Infrared Spectrometer (PRiMIRS), equipped with a customizable experimental chamber, is employed to observe DIMP decomposition. Tunable Diode Laser Absorption Spectroscopy (TDLAS) is used to measure path-averaged gas temperature profiles during combustion. In the experiment, the chamber is preheated to evaporate liquid DIMP. Various composite metal powders (Al-8Mg):3Zr, (Al-8Mg):Zr, 2(Al-8Mg):Zr, and 4(Al-8Mg):Zr are placed on a stainless steel mount and ignited using 3Al-2Ni sputter-deposited nanolayered foils. The combusting metal particles mix with the DIMP vapor, initiating chemical and thermal interactions. PRiMIRS captures DIMP spectral evolution, while TDLAS simultaneously monitors gas temperature. A spectral defeat parameter was developed to enable quantitative real-time assessment of the DIMP destruction. It uses infrared light absorption by both from DIMP and its immediate decomposition products Isopropyl Methyl Phosphonate (IMP) and Isopropyl Alcohol (IPA). Fourier Transform Infrared Spectroscopy (FTIR) serves as a secondary verification tool quantifying the decomposition products over extended timeframes, and Transmission Electron Microscopy (TEM) confirms the expected metal oxide dispersion within the reaction space. This study reports variability in DIMP defeat as a function of metal powder stoichiometry, metal powder loading, and path-averaged gas temperature profiles, offering critical insights into optimizing reactive materials for effective CWA neutralization.

We present subarcsecond-resolution ALMA mosaics of the Orion Bar PDR in [CI] 609 um, C2H (4-3), and C18O (3-2) emission lines, complemented by JWST images of H2 and aromatic infrared band (AIB) emission. The rim of the Bar shows very corrugated structures made of small-scale H2 dissociation fronts (DFs). The [CI] 609 um emission peaks very close (~0.002 pc) to the main H2-emitting DFs, suggesting the presence of gas density gradients. These DFs are also bright and remarkably similar in C2H emission, which traces 'hydrocarbon radical peaks' characterized by very high C2H abundances, reaching up to several x10^-7. The high abundance of C2H and of related hydrocarbon radicals, such as CH3, CH2, and CH, can be attributed to gas-phase reactions driven by elevated temperatures, the presence of C+ and C, and the reactivity of FUV-pumped H2. The hydrocarbon radical peaks roughly coincide with maxima of the 3.4/3.3 um AIB intensity ratio, a proxy for the aliphatic-to-aromatic content of PAHs. This implies that the conditions triggering the formation of simple hydrocarbons also favor the formation (and survival) of PAHs with aliphatic side groups, potentially via the contribution of bottom-up processes in which abundant hydrocarbon radicals react in situ with PAHs. Ahead of the DFs, in the atomic PDR zone (where [H]>>[H2]), the AIB emission is brightest, but small PAHs and carbonaceous grains undergo photo-processing due to the stronger FUV field. Our detection of trace amounts of C2H in this zone may result from the photoerosion of these species. This study provides a spatially resolved view of the chemical stratification of key carbon carriers in a PDR. Overall, both bottom-up and top-down processes appear to link simple hydrocarbon molecules with PAHs in molecular clouds; however, the exact chemical pathways and their relative contributions remain to be quantified.

The task of translating visible-to-infrared images (V2IR) is inherently challenging due to three main obstacles: 1) achieving semantic-aware translation, 2) managing the diverse wavelength spectrum in infrared imagery, and 3) the scarcity of comprehensive infrared datasets. Current leading methods tend to treat V2IR as a conventional image-to-image synthesis challenge, often overlooking these specific issues. To address this, we introduce DiffV2IR, a novel framework for image translation comprising two key elements: a Progressive Learning Module (PLM) and a Vision-Language Understanding Module (VLUM). PLM features an adaptive diffusion model architecture that leverages multi-stage knowledge learning to infrared transition from full-range to target wavelength. To improve V2IR translation, VLUM incorporates unified Vision-Language Understanding. We also collected a large infrared dataset, IR-500K, which includes 500,000 infrared images compiled by various scenes and objects under various environmental conditions. Through the combination of PLM, VLUM, and the extensive IR-500K dataset, DiffV2IR markedly improves the performance of V2IR. Experiments validate DiffV2IR's excellence in producing high-quality translations, establishing its efficacy and broad applicability. The code, dataset, and DiffV2IR model will be available at https://github.com/LidongWang-26/DiffV2IR.

Current autonomous driving algorithms heavily rely on the visible spectrum, which is prone to performance degradation in adverse conditions like fog, rain, snow, glare, and high contrast. Although other spectral bands like near-infrared (NIR) and long-wave infrared (LWIR) can enhance vision perception in such situations, they have limitations and lack large-scale datasets and benchmarks. Short-wave infrared (SWIR) imaging offers several advantages over NIR and LWIR. However, no publicly available large-scale datasets currently incorporate SWIR data for autonomous driving. To address this gap, we introduce the RGB and SWIR Multispectral Driving (RASMD) dataset, which comprises 100,000 synchronized and spatially aligned RGB-SWIR image pairs collected across diverse locations, lighting, and weather conditions. In addition, we provide a subset for RGB-SWIR translation and object detection annotations for a subset of challenging traffic scenarios to demonstrate the utility of SWIR imaging through experiments on both object detection and RGB-to-SWIR image translation. Our experiments show that combining RGB and SWIR data in an ensemble framework significantly improves detection accuracy compared to RGB-only approaches, particularly in conditions where visible-spectrum sensors struggle. We anticipate that the RASMD dataset will advance research in multispectral imaging for autonomous driving and robust perception systems.

We report the discovery of two broad-line X-ray AGNs cid_414 and cid_947 at z~3 that exhibit prominent He Iλ10830+ Paγ emission and absorption, identified from the JWST Cycle 3 large GO treasury program COSMOS-3D using NIRCam F444W grism spectroscopy. Additional UV/optical line measurements (e.g., Lyα, Si IV, C IV) come from complementary COSMOS-field spectroscopy. Both sources are robustly detected in the mid-infrared, with detections in MIRI F1000W for both AGNs and an additional detection in MIRI F2100W for cid_414, indicating the presence of hot dust emission. The source cid_947 shows a higher He Iλ10830 absorption column density and X-ray-inferred N_{rm H}, and displays strong outflow signatures in He I, Si IV, and C IV with velocity offsets exceeding 5000 km/s. The source cid_414 shows a narrow Lyα emission line with luminosity log L_{rm Lyα}=42.49pm0.01~erg~s^{-1} and a higher intrinsic 2-10 keV X-ray luminosity. Host-galaxy decomposition and multi-component SED fitting indicate that cid_947 hosts a more massive black hole but lower star formation rate than cid_414. From simplified photoionization modeling, we infer that the dense absorbing gas has a characteristic size comparable to the nuclear broad-line region and is likely kinematically coupled to the obscuration associated with the dust torus. He Iλ1083 absorption has also been identified in several compact little red dots at similar redshifts. Together with the two AGNs reported here, these findings suggest that dense circumnuclear gas are plausibly prevalent at high redshift and plays an important role in regulating AGN obscuration and black hole--host co-evolution.