High-resolution Infrared Images Research Articles

Vertical Barrier Heterostructures for Reliable and High-Performance Self-Powered Infrared Detection.

With their fascinating properties, emerging two-dimensional (2D) materials offer innovative ways to prepare high-performance infrared (IR) detectors. However, the current performance of 2D IR photodetectors is still below the requirements for practical application owing to the severe interfacial recombination, sharply raised contact resistance, and deteriorated metal conductivity at nanoscale. Here, we introduce a vertical barrier heterojunction with a structure of PtSe2/GaAs that combines the excellent optoelectronic properties of transition metal sulfides with topological semi-metals, which allows for an adjustable bandgap and high carrier mobility. The heterojunction was fabricated using the wet transfer method. The heterostructures show significant rectification behaviors and photovoltaic effects, which allow it to operate as a self-driven photodetector at zero bias. The photoresponse parameters at 850 nm with zero bias voltage are 67.2 mA W-1, 6.7 × 1012 Jones, 9.8%, 3.8 × 105, 164 μs, and 198 μs for the responsivity, specific detectivity, external quantum efficiency, Ilight/Idark ratio, rise time, and fall time, respectively. Moreover, the heterojunction is highly sensitive to a wide spectral band from ultraviolet to near-infrared (360-1550 nm). At the same time, this heterostructure demonstrates significant potential for applications in IR polarized light detection and room-temperature high-resolution IR imaging. The excellent properties of the heterojunction make it well-suited for high-performance, self-powered IR detection.

Deep Generative Modeling of Infrared Images Provides Signature of Cracking in Cross-Linked Polyethylene Pipe

Hyperspectral infrared (IR) images contain a large amount of highly spatially resolved information about the chemical composition of a sample. However, the analysis of hyperspectral IR imaging data for complex heterogeneous systems can be challenging because of the spectroscopic and spatial complexity of the data. We implement a deep generative modeling approach using a β-variational autoencoder to learn disentangled representations of the generative factors of variance in a data set of cross-linked polyethylene (PEX-a) pipe. We identify three distinct physicochemical factors of aging and degradation learned by the model and apply the trained model to high-resolution hyperspectral IR images of cross-sectional slices of unused virgin, used in-service, and cracked PEX-a pipe. By mapping the learned representations of aging and degradation to the IR images, we extract detailed information on the physicochemical changes that occur during aging, degradation, and cracking in PEX-a pipe. This study shows how representation learning by deep generative modeling can significantly enhance the analysis of high-resolution IR images of complex heterogeneous samples.

Single infrared image super‐resolution based on lightweight multi‐path feature fusion network

Single infrared (IR) image super-resolution methods can help to reduce the cost and difficulty in manufacturing IR sensors for the imaging system. However, the deep learning-based image SR methods need to build a complex network and thus consume a lot of computational power, which limits the application of SR technology on devices with low computing resources in practice. To solve this problem, the authors present a lightweight multi-path feature fusion network (MFFN) for the single infrared (IR) image SR. A multi-path feature fusion block (MFFB) is developed to extract and fuse multiple and discriminative features in a recursive feedback way. Specifically, the multiple features are refined via the linear feature extraction branch, shared-source residual feature extraction branch, and channel attention branch in MFFB. Finally, the authors reconstruct the high-resolution IR images from the low-resolution counterpart based on the refined multiple features. The experimental results demonstrate that MFFN achieves high-quality single infrared image SR and shows superiority over previous methods for several scale factors (e.g. ×2, ×3, and ×4). MFFN has potential applications in the mobile infrared imaging system.

A lightweight iterative error reconstruction network for infrared image super-resolution in smart grid

Thermal infrared (IR) images are widely used in smart grids for numerous applications. These applications prefer high-resolution (HR) IR images since HR IR images benefit the performance. However, HR IR imaging devices are extremely expensive. To save the cost of upgrading imaging devices, an iterative error reconstruction network (IERN) is proposed to improve the resolution of IR images. We first achieve efficient dense connections based on linearly compressive skip links. Slightly sacrificing the performance, the efficient dense connections can markedly reduce the parameters and computations of the vanilla dense connections. Then, an iterative error reconstruction mechanism is proposed to boost the performance, which enables IERN to restore many more textures and edges. Specifically, an initial SR image, high-level features, and up-sampled features are obtained firstly. Secondly, a SR error image is acquired by reconstructing the errors between the initial high-level features and the back-projected features from the up-sampled features. Thirdly, a new SR image is obtained by adding the SR error image to the initial SR image. Iterating the above process, the final SR image is achieved when the number of iterations reaches to the iteration threshold. Experimental results reveal the superiority of the proposed method over state-of-the-art methods.

Unveiling the stellar origin of the Wolf–Rayet nebula NGC 6888 through infrared observations

ABSTRACT We present a comprehensive infrared (IR) study of the iconic Wolf–Rayet (WR) wind-blown bubble NGC 6888 around WR 136. We use Wide-field Infrared Survey Explorer, Spitzer IRAC, and MIPS and Herschel PACS IR images to produce a sharp view of the distribution of dust around WR 136. We complement these IR photometric observations with Spitzer IRS spectra in the 5–38-μm wavelength range. The unprecedented high-resolution IR images allowed us to produce a clean spectral energy distribution, free of contamination from material along the line of sight, to model the properties of the dust in NGC 6888. We use the spectral synthesis code cloudy to produce a model for NGC 6888 that consistently reproduces its optical and IR properties. Our best model requires a double distribution with the inner shell composed only of gas, whilst the outer shell requires a mix of gas and dust. The dust consists of two populations of grain sizes, one with small-sized grains asmall = [0.002–0.008] $\mu$m and another one with large-sized grains abig = [0.05–0.5] $\mu$m. The population of big grains is similar to that reported for other red supergiants stars and dominates the total dust mass, which leads us to suggest that the current mass of NGC 6888 is purely due to material ejected from WR 136, with a negligible contribution of the swept up interstellar medium. The total mass of this model is 25.5$^{+4.7}_{-2.8}$ M⊙, a dust mass of $M_\mathrm{dust} = 0.14^{+0.03}_{-0.01}$ M⊙, for a dust-to-gas ratio of 5.6 × 10−3. Accordingly, we suggest that the initial stellar mass of WR 136 was ≲50 M⊙, consistent with current single stellar evolution models.

Determining the thermal characteristics of breast cancer based on high-resolution infrared imaging, 3D breast scans, and magnetic resonance imaging

For over the three decades, various researchers have aimed to construct a thermal (or bioheat) model of breast cancer, but these models have mostly lacked clinical data. The present study developed a computational thermal model of breast cancer based on high-resolution infrared (IR) images, real three-dimensional (3D) breast surface geometries, and internal tumor definition of a female subject histologically diagnosed with breast cancer. A state-of-the-art IR camera recorded IR images of the subject’s breasts, a 3D scanner recorded surface geometries, and standard diagnostic imaging procedures provided tumor sizes and spatial locations within the breast. The study estimated the thermal characteristics of the subject’s triple negative breast cancer by calibrating the model to the subject’s clinical data. Constrained by empirical blood perfusion rates, metabolic heat generation rates reached as high as 2.0E04 W/m3 for normal breast tissue and ranged between 1.0E05–1.2E06 W/m3 for cancerous breast tissue. Results were specific to the subject’s unique breast cancer molecular subtype, stage, and lesion size and may be applicable to similar aggressive cases. Prior modeling efforts are briefly surveyed, clinical data collected are presented, and finally thermal modeling results are presented and discussed.

Thermal Imaging To Visualize and Characterize Combustion Fronts in Porous Media

We developed a novel technique based upon time-lapse infrared (IR) images to relate the effects of crude-oil oxidation kinetics on flow during one-dimensional homogeneous and heterogeneous laboratory-scale combustion tube experiments. We performed combustion tube experiments under variable conditions including different sands (i.e., grain-size distribution), air injection rate history (constant versus variable), degree of packing heterogeneity, and reaction heterogeneity. The latter is achieved by using reaction enhancing nanoparticles in controlled packing configurations. During every experiment, we obtain high-resolution IR images of the outer wall of the combustion tube that we calibrate using point-wise temperature measurements from a thermocouple. Here, a new experimental workflow that uses these images and combines knowledge obtained from kinetic cell experiments is used to isolate the spatial zones within the tube where so-called low-temperature and high-temperature oxidation (pseudoreaction regimes) occurs during combustion tube experiments for the first time. Additionally, the IR imaging technique is shown to provide new insight into the propagation of the combustion front in homogeneous and heterogeneous systems and, importantly, visualizes gravity drainage of hot oil.

Nanoscale infrared imaging analysis of carbonaceous chondrites to understand organic-mineral interactions during aqueous alteration

Organic matter in carbonaceous chondrites is distributed in fine-grained matrix. To understand pre- and postaccretion history of organic matter and its association with surrounding minerals, microscopic techniques are mandatory. Infrared (IR) spectroscopy is a useful technique, but the spatial resolution of IR is limited to a few micrometers, due to the diffraction limit. In this study, we applied the high spatial resolution IR imaging method to CM2 carbonaceous chondrites Murchison and Bells, which is based on an atomic force microscopy (AFM) with its tip detecting thermal expansion of a sample resulting from absorption of infrared radiation. We confirmed that this technique permits ∼30 nm spatial resolution organic analysis for the meteorite samples. The IR imaging results are consistent with the previously reported association of organic matter and phyllosilicates, but our results are at much higher spatial resolution. This observation of heterogeneous distributions of the functional groups of organic matter revealed its association with minerals at ∼30 nm spatial resolution in meteorite samples by IR spectroscopy.

Breast cancer detection from thermal images using bispectral invariant features

Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

Functional infrared imaging of the breast.

In order to re-assess the potential contribution of infrared (IR) imaging as a first-line component of a multi-imaging strategy using currently available technology, we first review the history of its introduction and clinical application, including the results of the Breast Cancer Detection Demonstration Projects (BCDDP). We then discuss experiments with a new high-resolution, computerized IR station and software program acquired by the Ville Marie Breast Center to assess IR imaging's ability to complement clinical examination and mammography in the early detection of breast cancer. Our goal is to show that high-resolution IR imaging provides additional safe, practical, and objective information when produced and interpreted by sufficiently trained breast physicians.