Short-wave Infrared Camera Research Articles

ISK: Index steering kernel for multi-SWIR-image super-resolution

Multi-image super-resolution (SR) techniques reconstruct high-resolution (HR) images from multiple low-resolution (LR) counterparts, yet existing methods often fall short in reproducing high-frequency details when compared to true HR images, especially short-wave infrared (SWIR) images. This discrepancy, coupled with the technological and cost limitations of enhancing high-resolution imaging system performance through lens aperture size and sensor density, impedes the advancement of imaging systems and digital image technology. Addressing this, we propose the Index Steering Kernel (ISK) method, an innovation grounded in Gaussian process multi-image super-resolution, equivariant kernel theory, and steering kernel methods. Validated through comparative resolution chart tests using a SWIR camera (InGaAs detector), our method achieves a TV Line resolution of 60lp/mm in 4-image 2 × SR experiments, closely approaching the 65lp/mm of genuine high-resolution SWIR images and surpassing alternative approaches. In 9-image 3 × SR experiments, ISK consistently outperforms comparative methods. As a non-data-driven approach, ISK incorporates data compression designs, enabling 3 × SR of nine 640 × 512 SWIR images on a single NVIDIA GTX GeForce 1080Ti graphics card without image splitting, proving its computational efficiency while maintaining superior SR quality.

Real-time imaging of standing-wave patterns in microresonators

Real-time characterization of microresonator dynamics is important for many applications. In particular, it is critical for near-field sensing and understanding light-matter interactions. Here, we report camera-facilitated imaging and analysis of standing wave patterns in optical ring resonators. The standing wave pattern is generated through bidirectional pumping of a microresonator, and the scattered light from the microresonator is collected by a short-wave infrared (SWIR) camera. The recorded scattering patterns are wavelength dependent, and the scattered intensity exhibits a linear relation with the circulating power within the microresonator. By modulating the relative phase between the two pump waves, we can control the generated standing waves' movements and characterize the resonator with the SWIR camera. The visualized standing wave enables subwavelength distance measurements of scattering targets with nanometer-level accuracy. This work opens broad avenues for applications in on-chip near-field (bio)sensing, real-time characterization of photonic integrated circuits, and backscattering control in telecom systems.

Early Bruise Detection in Apple Based on an Improved Faster RCNN Model

Bruising is a common occurrence in apples that can lead to gradual fruit decay and substantial economic losses. Due to the lack of visible external features, the detection of early-stage bruising (occurring within 0.5 h) is difficult. Moreover, the identification of stems and calyxes is also important. Here, we studied the use of the short-wave infrared (SWIR) camera and the Faster RCNN model to enable the identification of bruises on apples. To evaluate the effectiveness of early bruise detection by SWIR bands compared to the visible/near-infrared (Vis/NIR) bands, a hybrid dataset with images from two cameras with different bands was used for validation. To improve the accuracy of the model in detecting apple bruises, calyxes, and stems, several improvements are implemented. Firstly, the Feature Pyramid Network (FPN) structure was integrated into the ResNet50 feature extraction network. Additionally, the Normalization-based Attention Module (NAM) was incorporated into the residual network, serving to bolster the attention of model towards detection targets while effectively mitigating the impact of irrelevant features. To reduce false positives and negatives, the Intersection over Union (IoU) metric was replaced with the Complete-IoU (CIoU). Comparison of the detection performance of the Faster RCNN model, YOLOv4P model, YOLOv5s model, and the improved Faster RCNN model, showed that the improved model had the best evaluation indicators. It achieved a mean Average Precision (mAP) of 97.4% and F1 score of 0.87. The results of research indicate that it is possible to accurately and effectively identify early bruises, calyxes, and stems on apples using SWIR cameras and deep learning models. This provides new ideas for real-time online sorting of apples for the presence of bruises.

High Quantum Yield Shortwave Infrared Luminescent Tracers for Improved Sorting of Plastic Waste.

More complete recycling of plastic waste is possible only if new technologies that go beyond state-of-the-art near-infrared (NIR) sorting are developed. For example, tracer-based sorting is a new technology that explores the upconversion or down-shift luminescence of special tracers based on inorganic materials codoped with lanthanide ions. Specifically, down-shift tracers emit in the shortwave infrared (SWIR) spectral range and can be detected using a SWIR camera preinstalled in a state-of-the-art sorting machine for NIR sorting. In this study, we synthesized a very efficient SWIR tracer by codoping Li3Ba2Gd3 (MoO4)8 with Yb3+ and Er3+, where Yb3+ is a synthesizer ion (excited near 976 nm) and Er3+ emits near 1550 nm. Fine-tuning of the doping concentration resulted in a tracer (Li3Ba2Gd(3-x-y)(MoO4)8:xYb3+, yEr3+, where x = 0.2 and y = 0.4) with a high photoluminescence quantum yield for 1550 nm emission of 70% (using 976 nm excitation). This tracer was used to mark plastic objects. When the object was illuminated by a halogen lamp and a 976 nm laser, the three parts could be easily distinguished based on reflectance and luminescence spectra in the SWIR range: a plastic bottle made of polyethylene terephthalate, a bottle cap made of high-density polyethylene, and a label made of the tracer Li3Ba2Gd3(MoO4)8:Yb3+, Er3+. Importantly, the use of the tracer in sorting may require only the installation of a 976 nm laser in a state-of-the-art NIR sorting system.

Short-wave infrared polarimetric image reconstruction using a deep convolutional neural network based on a high-frequency correlation.

Imaging in visible and short-wave infrared (SWIR) wavebands is essential in most remote sensing applications. However, compared to visible imaging cameras, SWIR cameras typically have lower spatial resolution, which limits the detailed information shown in SWIR images. We propose a method to reconstruct high-resolution polarization SWIR images with the help of color images using the deep learning method. The training dataset is constructed from color images, and the trained model is well suited for SWIR image reconstruction. The experimental results show the effectiveness of the proposed method in enhancing the quality of the polarized SWIR images with much better spatial resolution. Some buried spatial and polarized information may be recovered in the reconstructed SWIR images.

Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared imaging thermal feature porosity probability maps

Local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology’s potential for in-situ based porosity detection.

Aerial detection of beached marine plastic using a novel, hyperspectral short-wave infrared (SWIR) camera

Abstract Plastic pollution in the marine environment is a pervasive, global problem that threatens wildlife and human health. Routine monitoring is required to determine pollution hotspots, focus clean-up efforts, and assess the efficacy of legislation implemented to reduce environmental contamination. The shoreline represents an accessible area, relative to open water, from which to monitor this. Unmanned aerial vehicles (UAVs) offer a low-cost platform for remote sensing that operates below cloud coverage, which can interfere with satellite imagery. Detection of plastic using visible light is limited however, and results may be improved by using short-wave infrared (SWIR) imagery to collect chemical information. Within the commercial recycling industry, plastic items are sorted successfully based on their composition using SWIR instrumentation that measures the chemical spectra of waste items under controlled illumination. Here, proof of concept is established for aerial detection of domestic and shoreline-harvested plastic items on a beach under natural sunlight with a lightweight (800 g), hyperspectral SWIR camera deployed at an altitude of ∼ 5 m over ∼ 30-m transects. The results of spectral correlation mapping to compare imagery spectra to polyethylene and polypropylene reference spectra demonstrate that these two polymers can be successfully detected with this novel method.

Integrated Infrared Signature Management with Multispectral Selective Absorber via Single‐Port Grating Resonance

AbstractEngineering reflective or thermal signatures is essential in stealth technology for enhancing survivability. For military weapons, infrared (IR) image fusion with reflective and thermal signatures or a laser guidance system has been adopted for detecting enemies. However, the control of reflective and thermal signatures over the entire mid‐IR (MIR) range requires a complex structure. Herein, integrated IR signature (IIRS) management with a multispectral selective absorber (MSA) against IR fusion cameras and a guidance laser is presented. For multispectral engineering with a periodic metal–insulator–metal structure, the physical concept of single‐port grating resonance (SPGR) is proposed. SPGR exhibits an absorption range 6.4 times broader than that of localized surface plasmon resonance. Covering the entire MIR spectrum, the MSA designed with SPGR reduces the reflective signature by 92.2% at 1.06 µm (wavelength of guidance laser), and 64.6% by short‐wave IR (SWIR) camera, and it reduces the thermal signature by 98.0%. The IIRS management with the MSA reduces the lock‐on range by 70% and 26% for the mid‐wave IR and SWIR cameras, respectively, and provides perfect camouflage performance against the laser guidance, in contrast to stainless steel. The IIRS management with the MSA based on SPGR can be applied to multispectral camouflage.

Development perspectives for the application of autonomous, unmanned aerial systems (UASs) in wildlife conservation

Conservation management requires reliable and up-to-date data on land use, wildlife population sizes and resource distribution across highly variable ecosystems. Simultaneously, limited funding in conservation often restrict the assessment and interpretation of these datasets. Unmanned aerial systems (UASs) appear as promising and cost-efficient tools to deliver high-quality data, especially when combined with advanced sensor technologies, e.g. based on specific light features such as spectral signature, short-wave infrared (SWIR) reflection and/or polarization. However, their application rates for conservation purposes remain low, partly because of current technology's inaptitude to extrapolate findings onto large spatial scales due to limited flight ranges of the vehicles and difficulties in animal detection and identification. Particularly, using SWIR cameras and polarization filters combined with thermal cameras may improve animal detection, but only few tests have so far investigated the reliability of these technologies. The analysis of large datasets e.g. from hyperspectral cameras requires skills and time, whereas most interest lies in the results of these surveys. Additionally, legal constraints and high initial investment costs confront their application. Overcoming these challenges requires advancing technological robustness of the tool as well as defining the applicability of unmanned aerial vehicles (UAVs) within conservation management. Moreover, its application needs validation in contrast to ground and/or aerial surveys to recommend protocols in different ecological settings and for different management questions. We conclude that UAVs may not serve as panaceas for monitoring land use changes and wildlife trends, but as additional, intermediary data collection tools to support management decisions.

Short-wave infrared light imaging measures tissue moisture and distinguishes superficial from deep burns.

Existing clinical approaches and tools to measure burn tissue destruction are limited resulting in misdiagnosis of injury depth in over 40% of cases. Thus, our objective in this study was to characterize the ability of short-wave infrared (SWIR) imaging to detect moisture levels as a surrogate for tissue viability with resolution to differentiate between burns of various depths. To accomplish our aim, we constructed an imaging system consisting of a broad-band Tungsten light source; 1,200-, 1,650-, 1,940-, and 2,250-nm wavelength filters; and a specialized SWIR camera. We initially used agar slabs to provide a baseline spectrum for SWIR light imaging and demonstrated the differential absorbance at the multiple wavelengths, with 1,940 nm being the highest absorbed wavelength. These spectral bands were then demonstrated to detect levels of moisture in inorganic and in vivo mice models. The multiwavelength SWIR imaging approach was used to diagnose depth of burns using an in vivo porcine burn model. Healthy and injured skin regions were imaged 72 hours after short (20 seconds) and long (60 seconds) burn application, and biopsies were extracted from those regions for histologic analysis. Burn depth analysis based on collagen coagulation histology confirmed the formation of superficial and deep burns. SWIR multispectral reflectance imaging showed enhanced intensity levels in long burned regions, which correlated with histology and distinguished between superficial and deep burns. This SWIR imaging method represents a novel, real-time method to objectively distinguishing superficial from deep burns.

Photodetector of Shortwave Infrared Range of Format 640 × 512 Elements with Increased Dynamic Range

The paper substantiates the necessity of expanding the dynamic range in a shortwave infrared (SWIR) photodetector (PD). The traditionally used methods have low efficiency, especially in large-format matrices with a step of not more than 15 µm. The greatest efficiency of expanding the dynamic range (up to 100 dB) is provided by accumulative cells with individually variable transfer characteristic depending on the brightness of the fragments of the observed scene. The paper proposes a simple, in topological implementation, and effective way of expanding the dynamic range based on the auto-tuning of accumulation time individually in each cell of the integrated reading circuit. At the same time, the high steepness and linearity of the transformation in the storage cells with moderate illumination (up to 50–70% of the maximum signal) remains, but the sensitivity in the cells close to saturation decreases. As a result, a linear-log transfer characteristic is formed, providing an extended dynamic range. The paper provides examples of images with an extended dynamic range obtained using the first domestic SWIR camera of format 640 × 512 elements.

High-resolution reconstruction of shortwave infrared polarimetric images using the intensity information of visible images.

Shortwave infrared (SWIR) polarimetric imaging has been found very effective in various applications. However, the low resolution of the SWIR camera severely limits the capacity of this technique. Image reconstruction methods have been developed to improve the spatial resolution, but these methods typically do not consider the polarized information that the images may contain. In this paper, we propose a high-resolution reconstruction method for SWIR images based on the spatial information of visible images without losing polarized information in the SWIR image. Experimental results demonstrate that this method is feasible to reconstruct high-resolution polarized SWIR images. We have also demonstrated its potential application in image fusion.

Hyperspectral and thermal temperature estimation during laser cladding

Although there is no doubt about the tremendous industrial potential of metal additive manufacturing techniques such as laser metal deposition, the technology still has some intrinsic quality challenges to overcome before reaching its industrial maturity. Noncontact in situ monitoring of the temperature evolution of the workpiece could provide the necessary information to implement an automated closed-loop process control system and optimize the manufacturing process, providing a robust solution to these issues. However, measuring absolute temperatures is not self-evident: wavelength-dependent emissivity values vary between solid, liquid, and mushy metallic regions, requiring spectral information and dedicated postprocessing to relate the amount of emitted infrared radiation to the material temperature. This paper compares the temperature estimation results obtained from a visible and near-infrared hyperspectral line camera and a conventional short-wave infrared (SWIR) thermal camera during the laser melting and cladding of a 316L steel sample. Both methods show agreeing results for the temperature distribution inside the melt pool, with the SWIR camera extending the temperature measurements beyond the melt pool boundaries into the solid region.

Multisensory System for the Detection and Localization of Peripheral Subcutaneous Veins.

This paper proposes a multisensory system for the detection and localization of peripheral subcutaneous veins, as a first step for achieving automatic robotic insertion of catheters in the near future. The multisensory system is based on the combination of a SWIR (Short-Wave Infrared) camera, a TOF (Time-Of-Flight) camera and a NIR (Near Infrared) lighting source. The associated algorithm consists of two main parts: one devoted to the features extraction from the SWIR image, and another envisaged for the registration of the range data provided by the TOF camera, with the SWIR image and the results of the peripheral veins detection. In this way, the detected subcutaneous veins are mapped onto the 3D reconstructed surface, providing a full representation of the region of interest for the automatic catheter insertion. Several experimental tests were carried out in order to evaluate the capabilities of the presented approach. Preliminary results demonstrate the feasibility of the proposed design and highlight the potential benefits of the solution.

Some Considerations about SWIR Gated Camera Performance by Using SNR and Detection Probability Concepts

The aim of this paper was to assess the improvement which can be add to the surveillance process which is affected by transmission and atmospheric turbulence using a Short Wave Infrared (SWIR) camera with image gating technology instead of a regular SWIR camera. As criteria for comparative analysis of their performance there were considered two key indicators: Target Detection Probability (Pdet) and image quality, materialized through Signal to Noise Ratio (SNR). The authors have considered a case study which revealed that turbulence and visibility of atmospheric influence less the operability of a SWIR system with image gating than a regular SWIR camera, especially in situations where visibility of the air is very low (less than 1 km). To analyze the behavior of the acquired image in variable atmospheric conditions there were used simulation software image as NVLaserID and SSCamIP.

Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV)

Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Perot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

Seeing Beyond the Visible

AbstractShort‐wave infrared (SWIR) cameras open up numerous possibilities for machine vision solutions, since they detect invisible product flaws as well as desired characteristics: In contrast to mainstream machine vision cameras with CCD or CMOS sensors, most SWIR cameras have an InGaAs (Indium Gallium Arsenide) sensor and thus detect wavelengths between 900 nm and 1700 nm. These wavelengths are invisible to the human eye and CCD or CMOS cameras. Thus, SWIR cameras detect the invisible, for example, water accumulations inside fruits or defects within silicon products. This document gives examples of SWIR camera applications in several fields such as the semiconductor industry, recycling, metal and glass inspection, and airborne remote sensing. Since some SWIR cameras are mainly designed for use in research facilities, not only the image quality is crucial for industrial applications, but also an industrial rugged design as well as camera features commonly used in machine vision applications.

Quantitative short-wave infrared multispectral imaging of in vivo tissue optical properties.

Extending the wavelength range of spatial frequency domain imaging (SFDI) into the short-wave infrared (SWIR) has the potential to provide enhanced sensitivity to chromophores such as water and lipids that have prominent absorption features in the SWIR region. Here, we present, for the first time, a method combining SFDI with unstructured (zero spatial frequency) illumination to extract tissue absorption and scattering properties over a wavelength range (850 to 1800 nm) largely unexplored by previous tissue optics techniques. To obtain images over this wavelength range, we employ a SWIR camera in conjunction with an SFDI system. We use SFDI to obtain in vivo tissue reduced scattering coefficients at the wavelengths from 850 to 1050 nm, and then use unstructured wide-field illumination and an extrapolated power-law fit to this scattering spectrum to extract the absorption spectrum from 850 to 1800 nm. Our proof-of-principle experiment in a rat burn model illustrates that the combination of multispectral SWIR imaging, SFDI, and unstructured illumination can characterize in vivo changes in skin optical properties over a greatly expanded wavelength range. In the rat burn experiment, these changes (relative to normal, unburned skin) included increased absorption and increased scattering amplitude and slope, consistent with changes that we previously reported in the near-infrared using SFDI.

Field trial of active remote sensing using a high-power short-wave infrared supercontinuum laser

Field trial results of a 5 W all-fiber broadband supercontinuum (SC) laser covering the short-wave infrared (SWIR) wavelength bands from ~1.55 to 2.35 μm are presented. The SC laser is kept on a 12 story tower at the Wright Patterson Air Force Base and propagated through the atmosphere to a target 1.6 km away. Beam quality of the SC laser after propagating through 1.6 km is studied using a SWIR camera and show a near diffraction limited beam with an M(2) value of <1.3. The SC laser is used as the illumination source to perform spectral reflectance measurements of various samples at 1.6 km, and the results are seen to be in good agreement with in-lab measurements using a conventional lamp source. Spectral stability measurements are performed after atmospheric propagation through 1.6 km and show a relative variability of ~4%-8% across the spectrum depending on the atmospheric turbulence effects. Spectral stability measurements are also performed in-lab and show a relative variability of <0.6% across the spectrum.