Night Vision Imaging Research Articles

High-Efficiency and Wavelength-Tunable Near-Infrared Emission of Lanthanide Ions Doped Lead-Free Halide Double Perovskite Nanocrystals toward Fluorescence Imaging.

Near-infrared (NIR) fluorescent materials show unique photophysical properties, which make them widely used in optical communication, night vision imaging, biomedicine, and other applications. However, the development of high-efficiency and wavelength-tunable NIR nanomaterials is still a challenge. Herein, a series of lanthanide ions doped Cs2AgIn0.99Bi0.01Cl6 double perovskite nanocrystals (DPNCs) with wavelength-tunable NIR light emission (800-1600 nm) have been synthesized. The optimal photoluminescence quantum yield (PLQY) of the DPNCs reaches 66.7%, which is a record value for DPNCs. It is mainly attributed to the contribution of NIR emission of lanthanide ions doped into DPNCs. More importantly, the series of NIR emission wavelengths of lanthanide ions doped Cs2AgIn0.99Bi0.01Cl6 DPNCs include not only shorter-wavelength NIR light (≤900 nm) but also longer-wavelength NIR light (>900 nm), which are more appropriate for foodstuff analysis and medical diagnosis applications. Furthermore, 11.2% Nd3+ doped Cs2AgIn0.99Bi0.01Cl6 DPNCs with the optimal PLQY were embedded in a polymethyl methacrylate (PMMA) polymer matrix (DPNCs@PMMA), and the stability of DPNCs modified by PMMA has been greatly improved. Finally, the 11.2% Nd3+ ions doped Cs2AgIn0.99Bi0.01Cl6 DPNCs@PMMA based NIR LEDs have demonstrated good night vision and human tissue penetration. This work indicates that lanthanide ions doped DPNCs have a potential in NIR light applications and could inspire future research to explore novel lanthanide ions doped semiconductor NCs based NIR LEDs.

Single-/fused-band dual-mode mid-infrared imaging with colloidal quantum-dot triple-junctions

Image data acquired with fused multispectral information can be used for effective identification and navigation owing to additional information beyond human vision, including thermal distribution, night vision, and molecular composition. However, the construction of photodetectors with such capabilities is hindered by the structural complexity arising from the integration of multiple semiconductor junctions with distinct energy gaps and lattice constants. In this work, we develop a colloidal quantum-dot dual-mode detector capable of detecting, separating, and fusing photons from various wavelength ranges. Using three vertically stacked colloidal quantum-dot homojunctions with alternating polarity, single-band short-wave infrared imaging and fused-band imaging (short-wave and mid-wave infrared) can be achieved with the same detector by controlling bias polarity and magnitude. The dual-mode detectors show detectivity up to 8 × 10 10 Jones at the fused-band mode and 3.1 × 10 11 Jones at the single-band mode, respectively. Without image post-processing algorithms, the dual-mode detectors could provide both night vision and thermal information-enhanced night vision imaging capability. To the best of our knowledge, this is the first colloidal quantum-dot detector that can achieve such functionality. The operation mode can be changed at a high frequency up to 1.7 MHz, making it possible to achieve simultaneously dual-mode imaging and remote temperature sensing.

Ion-induced electron and hydrogen ions emission of lead-bismuth silicate glass by ToF

A micro-channel Plate (MCP) is a continuous photo-electron multiplier, which has been widely used in the field of particle detectors and low-light night vision imaging devices. The electrons produced by particles collision from the incident end of MCP constitute the useful signal, while the electrons and negative ions produced by collision of ions formed by ionizing gas molecules are considered noise. To reduce the noise from ion collisions, it is important to identify the electron and negative ion emissions caused by collision of unexpected ion. In this paper, the electron and hydrogen ion emissions caused by 1.5MeV Au2+ were analyzed for the cases of acid-etched, alkali-etched, and hydrogen reduced lead-bismuth silicate glass (LBSG). The Time of Flight (ToF) method was used to detect the electron and hydrogen ion emission yields of LBSG. Hydrogen reduction significantly increases the yield of ion-induced electron and negative ions, and the alkali treatment gets the higher yield compared to acid treatment. It is proved that the ToF method is feasible to detect the ion-induced negative particles emission and would be more effectively if the accumulation of positive charges is avoided. The results are valuable not only for noise analysis and manufacturing parameters optimization of low-noise and high-gain MCP, but also as a reference for preparation of new-type micro-channels and investigation on the electron emission properties of glass for space.

Doping Compensation Enables High-Detectivity Infrared Organic Photodiodes for Image Sensing.

Infrared organic photodiodes have gained increasing attention due to their great application potentials in night vision, optical communication, and all-weather imaging. However, the commonly occurring high dark current and low detectivity impede infrared photodetectors from portable applications at room temperature. Herein, an efficient and generic doping compensation strategy is developed to improve the detectivity of infrared organic photodiodes. A series of n-type organic semiconductors is investigated, and it is found that doping compensation strategy not only reduces the trap density of states and dark currents, but also restrains the nonradiative recombination with improved charge transport and collection. As a result, an ultralow noise spectral density of 8× 10-15 A Hz-1/2 as well as a high specific detectivity over 1013 Jones in 780-1070nm is achieved at room temperature. More importantly, the high-performance infrared organic photodiodes can be successfully applied in high-pixel-density image arrays without patterning sensing layers. These findings provide important compensation design insights that will be crucial to further improve the performance of infrared organic photodiodes in the future.

Adaptive Dark Channel Prior Enhancement Algorithm for Different Source Night Vision Halation Images

The existing enhancement algorithms amplify the halation area and noise when enhancing the night vision halation image. Therefore, this paper proposes an adaptive dark channel prior (ADCP) enhancement algorithm for the different source night vision halation image. The algorithm constructs an adaptive transmittance function according to the relationship between the initial transmittance and the critical gray value of halation. The function can automatically adjust the transmittance according to the halation degree in the night vision image, which ensure the ADCP algorithm to achieve the adaptive enhancement of the images. The experimental results show that the proposed algorithm can effectively improve the clarity and contrast of visible and infrared images in night vision, and avoid over-enhancement of the halation region of visible images. When the proposed algorithm is applied to the anti-halation processing of different source night vision image fusion, the halation elimination of the fused image is more complete, the details of the dark area such as edge, brightness and color are moderately improved, and the overall visual effect is better than the existing enhancement algorithms. The effectiveness and universality of the proposed algorithm are verified for processing different night vision halation scene images.

Discussions on the development of advanced night vision imaging technology

Discussions on the development of advanced night vision imaging technology

Overcoming Intensity Saturation in Nonlinear Multiple-Quantum-Well Metasurfaces for High-Efficiency Frequency Upconversion.

Engineered intersubband transitions in semiconductor heterostructures featuring multiple quantum wells (MQWs) are shown to support record-high second-order nonlinear susceptibilities. By integrating these materials in metasurfaces with tailored optical resonances, it is possible to further enhance photonic interactions, yielding giant nonlinear responses in ultrathin devices. These metasurfaces form a promising platform for efficient nonlinear processes, including frequency upconversion of low-intensity thermal infrared radiation and harmonic generation, free of phase-matching constraints intrinsic to bulk nonlinear crystals. However, nonlinear saturation at moderately large pump intensities due to the transfer of electron population into excited subbands facilitated by strongly enhanced light-matter interactions in metasurfaces fundamentally limits their overall efficiency for various nonlinear processes. Here, the saturation limits of nonlinear MQW-based metasurfaces for mid-infrared frequency upconversion are significantly extended by optimizing their designs for excitation with a strong pump coherently coupled with unpopulated upper electron subbands. This counterintuitive pumping scheme, combined with tailored material and photonic engineering of the metasurface, avoids saturation at practical levels of continuous-wave pump intensities, yielding significantly larger upconversion efficiencies than in conventional approaches. The present results open new opportunities for nonlinear metasurfaces, less limited by saturation mechanisms, with important implications for night-vision imaging and compact nonlinear wave mixingsystems.

Improvements in Smartphone and Night Vision Imaging Technologies Enable Low Cost, On-Site Assays of Bioluminescent Cells.

Technologies enabling on-site environmental detection or medical diagnostics in resource-limited settings have a strong disruptive potential compared to current analytical approaches that require trained personnel in laboratories with immobile, resource intensive instrumentation. Handheld devices, such as smartphones, are now routinely produced with CPUs, RAM, wireless data transfer capabilities, and high-resolution complementary metal oxide semiconductor (CMOS) cameras capable of supporting the capture and processing of bioluminescent signals. In theory, combining the capabilities of these devices with continuously bioluminescent human cell-based bioreporters would allow them to replicate the functionality of more expensive, more complex, and less flexible platforms while supporting human-relevant conclusions. In this work, we compare the performance of smartphone (CMOS) and night vision (image intensifier) devices with in vivo (CCD camera), and in vitro (photomultiplier tube) laboratory instrumentation for monitoring signal dynamics from continuously bioluminescent human cellular models under toxic, stable, and induced expression scenarios. All systems detected bioluminescence from cells at common plating densities. While the in vivo and in vitro systems were more sensitive and detected signal dynamics representing cellular health changes earlier, the night vision and smartphone systems also detected these changes with relatively similar coefficients of variation and linear detection capabilities. The smartphone system did not detect transcriptional induction. The night vision system did detect transcriptional activation, but was less sensitive than the in vivo or in vitro systems and required a stronger induction before the change could be resolved.

Color night-vision imaging rangefinder

This paper describes a color night-vision imaging rangefinder that supports increased range, as well as the capability for remote transmission of images, real-time image processing, and precise measurement of the range to an observation target. This problem is addressed using a device that includes three separate television channels with a large-aperture optical system, an electronic image processing unit that outputs the video signal to a radio transmitter with a rod antenna, and a laser rangefinder channel.

Color night vision ghost imaging based on a wavelet transform.

Night vision imaging is a technology that converts objects not visible to the human eye into visible images for night scenes and other low-light environments. However, conventional night vision imaging can directly produce only grayscale images. Here, we report a novel, to the best of our knowledge, color night vision imaging method based on a ghost imaging framework and optimized coincidence measurement based on wavelet transformation. An interesting phenomenon is that color night vision image can be directly produced by this new method. To our knowledge, this is the first direct color night vision imaging method without any conventional pseudocolor image fusion techniques. The experimental results show that this method can restore color very well for some objects. Moreover, the color of the night vision image is more natural and friendly to the human eye than that of conventional color night vision images. Due to the advantages of wavelet transforms, this method has high reconstruction ability for distorted signals.

Dark infrared night vision imaging proposed work for pedestrian detection and tracking

This framework presents three efficient proposed algorithms for pedestrian detection and tracking in Dark Infrared Night Vision (DIRNV) images. The first approach is relied on Gradient Estimation (GE) after mixing structure Equalization Exponential Contrast Limited Adaptive Histogram Equalization (ECLAHE) with Gamma Correction, and finally Cumulative Histogram (GECUGC) for discrimination. The GECUGC relies on enhancement using mixing ECLAHE Using Gamma Correction (ECUG) in addition to pre-processing followed by the GE using Laplacian Filter (LAF), and finally Cumulative Histograms (CH) for the detection or classification task. The second approach is based GE after a hybrid structure Histogram Equalization (HE) with Nonlinear Technique and finally CH (GHNTC) for discrimination. The GHNTC depends on enhancement by merging HE with Nonlinear Technique (NT) (HENT) followed by the GE using LAF and finally CH for pedestrian detection and tracking using DIRNV imaging. After the CH estimation, the difference between cumulative histograms with and without objects is estimated and used for pedestrian detection and tracking using DIRNV imaging. The third algorithm is based scale space analysis with the number of the Speeded Up Robust Features (SURF) points as the key parameters for classification. This technique is presented to detect the features of DIRNV pedestrian images and tracking. The performance metrics are the difference area between the cumulative histograms of DIRNV images with and without pedestrian, computation time, points of features and speed up factor. Simulation results prove that the success of three suggested techniques in pedestrian detection and tracking using DIRNV imaging. By comparing the three presented algorithms, it is clear that the second suggested technique gives superior for pedestrian detection and tracking from point view difference area between the cumulative histograms.On the other hand the first suggested technique is the best algorithms for pedestrian detection and tracking from point view the computation time. The obtained results clear that the third approach has sucesseded in gait pedestrian detection and tracking using DIRNV imaging.

Fabrication and performance of a NiMn2O4/LaNiO3 composite film detector with multi electrodes for night vision imaging application

Detection performance of a composite structured infrared detector of NiMn2O4/LaNiO3 films has been studied. The thermally sensitive film of NiMn2O4 and conductive oxide film of LaNiO3 were deposited on silicon substrate by using a sol-gel method. The transmission and reflection measurements revealed that the absorption of the composite films grows from 6 to 8% (bare NiMn2O4) to about 40 %–70 % by increasing the LaNiO3 film thickness to 600 nm. A broadband optical detector was built by using the composite film. As revealed by the detection experiment, the detector showed a time constant of 0.45 ms to a 1550 nm light source, which was an order faster than the traditional thermal detectors. The detectivity of the detector was measured to be 0.37 × 107 cm·Hz0.5/W (Johnson noise) to a mid-infrared source, which can be improved to be about 109 cm·Hz0.5/W by using thermal isolation structures. The composite structure detector showed potentials in the application of night vision imaging.

Broadband near-infrared emission of K3ScF6:Cr3+ phosphors for night vision imaging system sources

• K 3 ScF 6 :Cr 3+ can be excited by blue light, leading to a broadband NIR emission. • K 3 ScF 6 :Cr 3+ retain 87.3% of their initial emission intensity at 150 °C. • The fabricated NIR-LED based on K 3 ScF 6 :Cr 3+ has good photoelectric efficiency. • NIR-LED based on K 3 ScF 6 :Cr 3+ can be applied in night vision imaging systems. Broadband near-infrared light (NIR) near 800 nm has broad application prospects in food detection and night vision monitoring. Compared with NIR chips, NIR phosphors have distinct advantages, including low cost, a mature synthesis process, and tunable combination. However, at present, oxide NIR phosphors have some disadvantages, such as a narrow full width at half maximum (FWHM), poor thermal stability, and low photoelectric efficiency. Herein, K 3 ScF 6 :Cr 3+ NIR phosphors with a cubic structure were synthesized using the hydrothermal method. These phosphors can be effectively excited by blue light (430 nm) and red light (630 nm), leading to a broadband Cr 3+ emission from 650 nm to 950 nm with a large FWHM of 430 nm. They also exhibit excellent thermal stability and retain 87.3% of their initial intensity at 150 °C. Under a driving current of 100 mA, their photoelectric efficiency can reach 9.315% of that of fabricated NIR light-emitting diodes (NIR-LED). An NIR-LED device based on this phosphor can thus be implemented in night vision imaging systems.

Pseudo color night vision correlated imaging without an infrared focal plane array.

Night vision is the ability to see in low-light conditions. However, conventional night vision imaging technology is limited by the requisite high-performance infrared focal plane array. In this article, we propose a novel scheme of color night vision imaging without the use of an infrared focal plane array. In the experimental device, the two-wavelength infrared laser beam reflected by the target is modulated by a spatial light modulator, and the output light is detected by a photomultiplier tube. Two infrared night vision images are reconstructed by measuring the second-order intensity correlation function between two light fields. Thus, the processing mode of optical electric detection in conventional night vision imaging is transformed into the processing mode of light field control. Furthermore, two gray images with different spectra are processed to form a color night vision image. We show that a high-quality color night vision image can be obtained by this method.

Super-efficient enhancement algorithm for infrared night vision imaging system

This paper presents new three proposed approaches for enhancement of Infrared (IR) night vision images. The first approach is based on merging gamma correction with the Histogram Matching (HM). The second approach depends on hybrid gamma correction with Contrast Limited Adaptive Histogram Equalization (CLAHE). The third approach is based on a trilateral enhancement that the IR images pass through three stages: segmentation, enhancement and sharpening. In the first stage, the IR image is divided into segments based on Optimum Global Thresholding (OTSU) method. The second stage, which is the heart of the enhancement approach, depends on Additive Wavelet Transform (AWT) to decompose the image into an approximation and details. Homomorphic enhancement is performed on the detail components, while Plateau Histogram Equalization (PHE) is performed on the approximation plane. Then, the image is reconstructed and subjected to a post-processing high pass filter. Average gradient, Sobel edge magnitude, entropy and spectral entropy has been used as quality metrics for assessment the three proposed approaches. It is clear that the third proposed approach gives superior results to the two proposed approaches point view the quality metrics. On the other hand, clear that the third proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The first proposed approach gives better results to the two proposed approaches from the computation time perspective.

Color Correction of True-color Night Vision Image by Extracting Green Plants with Differential Depth of Field

Color Correction of True-color Night Vision Image by Extracting Green Plants with Differential Depth of Field

Automatic Identification of Individual Primates with Deep Learning Techniques.

SummaryThe difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

An efficient proposed framework for infrared night vision imaging system

This research presents new three proposed approaches to enhancement the visibility of the Infrared (IR) night vision images. The first proposed approach depends on Hybrid Adaptive Gamma Correction (AGC) with Histogram Matching (HGCHM). The second proposed approach stands up Merging Gamma Correction with Contrast Limited Adaptive Histogram Equalization (MGCCLAHE). The HM uses a reference visual image for converting of night vision images into daytime images. The third approach mixes the benefits of the CLAHE with the undecimated Additive Wavelet Transform (AWT) Using Homomorphic processing (CSAWUH). The quality assessments for the suggested approaches are entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy, lightness order error and the similarity of edges. Simulation results clear that the third proposed approach gives superior results to the two proposed approaches from entropy, average gradient, contrast improvement factor, Sobel edge magnitude, spectral entropy and the computation time perspectives. On the other hand, the second proposed approach takes long computation time in the implementation with respect to the two proposed approaches. The second proposed approach gives better results to the first proposed approach entropy, average gradient, contrast improvement factor, Sobel edge magnitude, and spectral entropy perspectives. The first proposed approach gives better results to the two proposed approaches from lightness order error and the similarity of edges perspectives.

Hybrid enhancement of infrared night vision imaging system

This paper presents a proposed approach for the enhancement of Infrared (IR) night vision images. This approach is based on a trilateral contrast enhancement in which the IR night vision images pass through three stages: segmentation, enhancement and sharpening. In the first stage, the IR image is divided into segments based on thresholding. The second stage, which is the heart of the enhancement approach, depends on additive wavelet transform (AWT) to decompose the image into an approximation and details. Homomorphic enhancement is performed on the detail components, while plateau histogram equalization is performed on the approximation plane. Then, the image is reconstructed and subjected to a post-processing high-pass filter. Average gradient, Sobel edge magnitude and spectral entropy are used as quality metrics for evaluation of the proposed approach. The used metrics ensure good success of this proposed approach.

Cepstrum adaptive plateau histogram for dark IR night vision images enhancement

This paper presents a novel enhancement algorithm for infrared (IR) images. This suggested algorithm mixes the benefits of the adaptive plateau histogram equalization (APH) and the cepstrum of homomorphic filtering enhancement (CHFE). The main idea of this approach depends on applying the APH on the IR image. Then, the resultant image is applied on the frequency domain by using the CHFE. This CHFE model is depending on the image has produced high energy that received from object. The energy received is determined by illumination radiation source and the features of reflectance of the object itself. Applying this model on the APH gives more details in the IR image and the IR night vision images show like as in the morning. The performance quality metrics for the suggested approach are entropy, average gradient, contrast, and Sobel edge magnitude. Simulation results reveal the success of the proposed approach in enhancing the quality of IR images.