Imager Research Articles

Quasi-Single-Crystal Tin Halide Perovskite Films with High Structural Integrity for Near-Infrared Imaging Array Enabling Hidden Object Recognition.

Near-infrared (NIR) image sensors based on solution-processed thin film are gaining prominence in various applications, including security detection, remote sensing, medical imaging, and environmental monitoring, owing to superior penetration capabilities of NIR light. However, the reported perovskite image sensors suffer from limited resolution and performance due to poor structural integrity, bioincompatibility, and constrained response wavelength range from lead-based perovskite materials employed. In this study, we present non-toxic quasi-single-crystal (QSC) CH(NH2)2SnI3 (FASnI3) perovskite films, prepared via a simple spin-coating technique, that demonstrate high structural integrity and effective NIR response. Through a series of in situ characterizations, we reveal that the orderly growth mode of QSC-FASnI3 perovskite films enables a more oriented crystal growth with reduced trap and grain boundaries. Consequently, self-powered NIR photodetector based on QSC-FASnI3 films exhibited large detectivity over 1013 Jones in the NIR range (780-890 nm). Ultimately, due to the high structural integrity, the high-resolution 64×64 (4096) pixels NIR imaging array, exhibiting reduced photo and dark response non-uniformity value, was fabricated on the thin-film transistors (TFT) backplanes, enabling ultraweak NIR light (63 nW cm-2) real-time imaging, fingerprint imaging, and hidden object recognition. This work pioneers the application of lead-free perovskite for high-resolution NIR imaging arrays.

Quasi‐Single‐Crystal Tin Halide Perovskite Films with High Structural Integrity for Near‐Infrared Imaging Array Enabling Hidden Object Recognition

AbstractNear‐infrared (NIR) image sensors based on solution‐processed thin film are gaining prominence in various applications, including security detection, remote sensing, medical imaging, and environmental monitoring, owing to superior penetration capabilities of NIR light. However, the reported perovskite image sensors suffer from limited resolution and performance due to poor structural integrity, bioincompatibility, and constrained response wavelength range from lead‐based perovskite materials employed. In this study, we present non‐toxic quasi‐single‐crystal (QSC) CH(NH2)2SnI3 (FASnI3) perovskite films, prepared via a simple spin‐coating technique, that demonstrate high structural integrity and effective NIR response. Through a series of in situ characterizations, we reveal that the orderly growth mode of QSC‐FASnI3 perovskite films enables a more oriented crystal growth with reduced trap and grain boundaries. Consequently, self‐powered NIR photodetector based on QSC‐FASnI3 films exhibited large detectivity over 1013 Jones in the NIR range (780–890 nm). Ultimately, due to the high structural integrity, the high‐resolution 64×64 (4096) pixels NIR imaging array, exhibiting reduced photo and dark response non‐uniformity value, was fabricated on the thin‐film transistors (TFT) backplanes, enabling ultraweak NIR light (63 nW cm−2) real‐time imaging, fingerprint imaging, and hidden object recognition. This work pioneers the application of lead‐free perovskite for high‐resolution NIR imaging arrays.

Postharvest Management Techniques for Improved Shelf Life of Horticultural Crops: A Review

Postharvest management in horticulture is important for maintaining the quality, safety, and shelf life of produce, significantly impacting global food security and economic stability. This review explores critical advancements in postharvest practices, focusing on quality control, safety standards, and emerging technologies. Postharvest quality assessment involves the evaluation of physical, chemical, and sensory attributes, supported by microbiological testing to ensure food safety. Global standards like HACCP, GlobalG.A.P., and ISO 22000 provide frameworks for ensuring compliance and market access. Technological innovations, including near-infrared spectroscopy, hyperspectral imaging, and smart sensors, enable precise, real-time monitoring of produce quality, enhancing operational efficiency. Emerging technologies are revolutionizing the field; nanotechnology offers advanced packaging solutions that improve shelf life and food safety, while drones and AI enhance postharvest monitoring and logistics through real-time data collection and predictive analytics. Blockchain technology introduces unprecedented levels of traceability and transparency, ensuring accountability and rapid response to food safety issues. The integration of these technologies not only improves the efficiency and sustainability of postharvest systems but also aligns with consumer demands for high-quality, safe, and sustainably sourced produce. This holistic approach reduces post- harvest losses, enhances market competitiveness, and contributes to global efforts in ensuring food security. The synergy of traditional quality control methods with cutting-edge technologies paves the way for a resilient and adaptive post- harvest management system that meets the evolving challenges of the agricultural sector.

Highly-efficient (>70%) and Wide-spectral (400-1700 nm) sub-micron-thick InGaAs photodiodes for future high-resolution image sensors.

This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors (PDs) designed for high-resolution imaging from the visible to short-wavelength infrared (SWIR) spectrum. Conventional approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer (AL). Therefore, we propose a guided-mode resonance (GMR) structure to enhance the quantum efficiency (QE) of the InGaAs PDs in the SWIR region with only sub-micron-thick AL. The TiOx/Au-based GMR structure compensates for the reduced AL thickness, achieving a remarkably high QE (>70%) from 400 to 1700 nm with only a 0.98 μm AL InGaAs PD (defined as 1 μm AL PD). This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high QE. Furthermore, the rapid transit time is highly expected to result in decreased electrical crosstalk. The effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness, simultaneously enhancing the transit time. This breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.

Multispectral multiplexed confocal FLIM for live cell imaging

Abstract Spectrally resolved fluorescence lifetime imaging with high spatial precision offers comprehensive information on species localization and behavior. It is challenging to resolve weak fluorescence signals in multiple dimensions (spatial, spectral, and temporal) at high frame rates, especially in dynamic live cell processes, as photobleaching and phototoxicity limit acceptable photon count rates. We developed a multiplexed confocal FLIM technique, which uses a prism-based imaging spectrometer to separate a 10x10 array of confocal foci into their spectral components. This allows the sampling of the spectra by a time-resolved image sensor to produce a multispectral time-resolved data set used for generating multispectral lifetime images. This system captures 300x300 pixel fluorescence lifetime images containing 12 unique spectral bands covering a 450-700 nm spectral range in 1.8 seconds of exposure. Its performance was demonstrated in fixed stained samples and in multispectral imaging of FLIM-FRET in live cells.

Retina-Inspired X-Ray Optoelectronic Synapse Using Amorphous Ga2O3 Thin Film.

Machine vision techniques are widely applied for object identification in daily life and industrial production, where images are captured and processed by sensors, memories, and processing units sequentially. Neuromorphic optoelectronic synapses, as a preferable option to promote the efficiency of image recognition, are hotly pursued in non-ionizing radiation range, but rarely in ionizing radiation including X-rays. Here, the study proposes an X-ray optoelectronic synapse using amorphous Ga2O3 (a-Ga2O3) thin film. Boosted by the interfacial VO 2+ defects and its slow neutralization rate, the enhanced electron tunneling process at metal/a-Ga2O3 interface produces remarkable X-ray-induced post-synaptic current, contributing to a sensitivity of 20.5, 64.3, 164.1 µC mGy-1 cm-2 for the 1st, 5th, and 10th excitation periods, respectively. Further, a 64×64 imaging sensor is constructed on a commercial amorphous Si (a-Si) thin film transistor (TFT) array. The image contrast can be apparently improved under a series of X-ray pulses due to an outstanding long-term plasticity of the single pixel, which is beneficial to the subsequent image recognition and classification based on artificial neural network. The merits of large-scale production ability and good compatibility with modern microelectronic techniques belonging to amorphous oxide semiconductors may promote the development of neuro-inspired X-ray imagers and corresponding machine vision systems.

Opto-Electrical Decoupled Phototransistor for Starlight Detection.

Highly sensitive shortwave infrared (SWIR) detectors are essential for detecting weak radiation (typically below 10-8 W·Sr-1·cm-2·µm-1) with high-end passive image sensors. However, mainstream SWIR detection based on epitaxial photodiodes cannot effectively detect ultraweak infrared radiation due to the lack of inherent gain. Here, we develop a heterojunction-gated field-effect transistor (HGFET) consisting of a colloidal quantum dot (CQD)-based p-i-n heterojunction and a carbon nanotube (CNT) field-effect transistor, which achieves a high inherent gain based on an opto-electric decoupling mechanism for suppressing noise. The stacked heterojunction absorbs infrared radiation and separates electron-hole pairs. Then, the generated photovoltage tunes the drain current of the CNT FET through an Y2O3 gate insulator. As a result, the HGFET significantly detects and amplifies SWIR signals with a high inherent gain while minimally amplifying noise, leading to a recorded specific detectivity above 1014 Jones at 1300nm and a recorded maximum gain-bandwidth product of 69.2 THz. Direct comparative testing indicates that the HGFET can detect weak infrared radiation at 0.46 nWcm-2 levels; thus, compared to commercial and reported SWIR detectors, this detector is much more sensitive and enables starlight detection or vision. As the fabrication process is very compatible with CMOS readout integrated circuits, the HGFET is a promising SWIR detector for realizing passive night vision imaging sensors with high resolutions that are high-end, highly sensitive, and inexpensive.

Color Models in the Process of 3D Digitization of an Artwork for Presentation in a VR Environment of an Art Gallery

This study deals with the color reproduction of a work of art to digitize it into a 3D realistic model. The experiment aims to digitize a work of art for application in a virtual reality environment concerning faithful color reproduction. Photogrammetry and scanning with a LiDAR sensor are used to compare the methods and work with colors during the reconstruction of the 3D model. An innovative tablet with a camera and LiDAR sensor is used for both methods. At the same time, current findings from the field of color vision and colorimetry are applied to 3D reconstruction. The experiment focuses on working with the RGB and L*a*b* color models and, simultaneously, on the sRGB, CIE XYZ, and Rec.2020(HDR) color spaces for transforming colors into a virtual environment. For this purpose, the color is defined in the Hex Color Value format. This experiment is a starting point for further research on color reproduction in the digital environment. This study represents a partial contribution to the much-discussed area of forgeries of works of art in current trends in forensics and forgery.

Visible light visual indoor positioning system for based on residual convolutional networks and image restoration

Abstract Visible light positioning technology, with its advantages of low cost, strong anti-interference, and high precision, is widely researched and applied in various different scenarios. In this paper, for the complexity of indoor environments, considering the problem of occlusion by various obstacles that may exist in indoor spaces, which may lead to incomplete imaging of CMOS image sensors, a maximum gray value-based occlusion recovery and decoding scheme is proposed. This scheme effectively solves the problem of visible light transmission channel being blocked and accomplishes LED-ID decoding. In addition, the overflow effect due to uneven light irradiation gathered in each pixel row affects the accuracy of decoding LED-ID, which in turn leads to poor positioning accuracy. In this paper, we propose to use an adaptive gamma correction method to eliminate the influence of the overflow effect and to improve the accuracy of decoding. In order to improve the positioning accuracy, a visible light positioning algorithm based on residual convolutional network (VisiResNet) is proposed to achieve high accuracy positioning. The experimental results show that the average positioning error is 9.7 cm in a space of 9m× 3m× 3m, and a decoding accuracy of 90% within 1.4m is achieved in the face of different occlusion situations. The system can achieve centimeter-level positioning accuracy and meet the indoor positioning requirements.

Hyper‐Sampling Imaging by Measurement of Intra‐Pixel Quantum Efficiency Using Steady Wave Field

AbstractThe transition from optical film to digital image sensors (DIS) in imaging systems has brought great convenience to human life. However, the sampling resolution of DIS is considerably lower than that of optical film due to the limitation that the pixels are significantly larger than the silver halide molecules. How to break DIS's sampling limit and achieve high‐resolution imaging is highly desired for imaging applications. In the research, a novel mechanism that allows for a significant reduction in the smallest sampling unit of DIS to as small as 1/16th of a pixel, or even smaller, through measuring the intra‐pixel quantum efficiency for the first time and recomputing the image — a technique referred to as hyper‐sampling imaging (HSI) is developed. Employing the HSI method, the physical sampling resolution of regular DIS can be enhanced by 4 × 4 times or potentially higher, and detailed object information can be acquired. The HSI method has undergone rigorous testing in real‐world imaging scenarios, demonstrating its robustness and efficiency in overcoming the sampling constraints of conventional DIS. This advancement is particularly beneficial for applications such as remote sensing, long‐range reconnaissance, and astronomical observations, where the ability to capture fine details is paramount.

Flat-field methods for color image sensors

The research provides a detailed analysis of the primary sources of non-uniformity in color CMOS image sensors. It introduces the fundamental theory of traditional photon conversion and the main objective and subjective quality evaluation metrics for images. The theoretical foundations of traditional correction methods are also discussed. Based on the photon response characteristics of the color image sensor, a method for flat-field correction of color image sensors is proposed by analyzing the grayscale response curves of each pixel. This method addresses the non-linearity issue in pixel photon response caused by crosstalk between pixels and improves the non-uniformity problem in pixel response.

Comparison of Camera-Acquired Vital Signs to Conventional Vital Signs in a Space-Analog Environment.

Vital sign acquisition is a key component of modern medical care. In wilderness and space medical settings, vital sign acquisition can be a difficult process because of limitations on available personnel or lack of access to the patient. Camera-acquired vital signs could address each of these difficulties. Healthy volunteers used software designed by Presage Technologies to acquire heart rate and respiratory rate at the HI-SEAS space-analog site in Mauna Loa, Hawai'i. Camera-acquired vital signs were compared to more conventionally acquired vital signs. Camera-acquired heart rate showed high correlation to conventionally acquired heart rate (R ∼ 0.95). Camera-acquired respiratory rate showed moderate correlation (R ∼ 0.65). These results show that camera acquisition of vital signs is theoretically feasible in wilderness and space-analog environments. HR may be highly accurate even using current technology. Additional studies will be needed to further validate other types of camera sensors and other potential environments such as partial gravity and microgravity.

Proton irradiation effect mechanism of backside illuminated CMOS image sensors applied to FGS

A study of the mechanism of proton irradiation effects in the in-orbit operation of the backside illuminated CMOS image sensor (BSI CIS) used for imaging by the Fine Guidance Sensor (FGS) is presented in this paper. According to the proton irradiation environment of the Halo L2 orbit where FGS is located and the duration of in orbit operation, proton irradiation experiments were conducted on the BSI CIS, the core imaging device of FGS. Results show that proton irradiation is expected to cause a significant increase in the dark current of the BSI CIS of the FGS after four years of its in orbit, with the peak of the dark signal distribution shift to the right and an exponential trailing of the tail of the dark signal distribution when the proton fluence reached 6.71 × 1010p/cm2. This is the result of a combination of ionization defects and displacement damage defects. In addition, we also report about the device gain, which is of particular interest in space astronomical exploration. We performed a more detailed single pixel gain test of the device gain and found that the degradation of the gain appears to be correlated with the intensity of the ionization defects. The results of this study can provide theoretical basis for the radiation hardening by design of FGS to some extent.

Research on real-time 3D imaging technology based on CMOS sensors

Abstract. With the continuous progress of science and technology, 3D imaging technology has been widely used in many fields, such as medical imaging, virtual reality and protection of cultural heritage. It can provide more information than traditional 2D images, greatly improving the accuracy and reliability of various applications. CMOS (Complementary Metal Oxide Semiconductor) sensors, as efficient and low-cost image sensors, have been widely used in image imaging and processing technologies in recent years. However, how to realize real-time and high-precision 3D imaging with CMOS sensors is still a technical challenge to be solved. Based on this background, this paper will focus on real-time 3D imaging techniques based on CMOS sensors through three topics. This paper will first introduce how CMOS sensors work, followed by the introduction of real-time 3D imaging technology, and finally the real-time 3D imaging technology based on CMOS sensors. The research in this paper will be valuable for the research and application of real-time 3D imaging technology based on CMOS sensors.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests’ detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

A broadband hyperspectral image sensor with high spatio-temporal resolution

Hyperspectral imaging provides high-dimensional spatial–temporal–spectral information showing intrinsic matter characteristics1–5. Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible–near-infrared (400–1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.

How destination image projected on social media influences online sales of tourism services: exploring the moderating role of tourists’ experience

ABSTRACT While previous research has focused on the cognitive and affective dimensions of destination image and their effects on tourists’ attitudes and intentions (rather than actual behaviours), this study takes a three-dimensional approach to examine tourists’ objective responses. It examines how cognitive, affective, and sensory destination images projected by the destination marketing organisation (DMO) through social media influence its online sales of tourism services. Analysing 1676 social media posts from a DMO, findings reveal that cognitive and sensory images positively affect online sales. Additionally, tourists’ previous experience with the destination moderates the effects of the three dimensions of projected destination image.

Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Learned phase mask to protect camera under laser irradiation

The electro-optical imaging system works under focus conditions for clear imaging. However, under unexpected laser irradiation, the focused light with extremely high intensity can easily damage the imaging sensor, resulting in permanent degradation of its perceptual capabilities. With the escalating prevalence of compact high-performance lasers, safeguarding cameras from laser damage presents a formidable challenge. Here, we report an end-to-end method to construct the wavefront coding (WFC) imaging systems with simultaneous superior laser protection and imaging performance. In the optical coding part, we employ four types of phase mask parameterization methods: pixel-wise, concentric rings, linear combinations of Zernike bases, and odd-order polynomial bases, with parameters that are learnable. In the algorithm decoding part, a method combined of deconvolution module and residual-Unet is proposed to furthest restore the phase-mask-induced image blurring. The optical and algorithm parts are jointly optimized within the end-to-end framework to determine the performance boundary. The governing rule of the laser protection capability versus imaging quality is revealed by tuning the optimization loss function, and the system database is established for various working conditions. Numerical simulations and experimental validations both demonstrate that the proposed laser-protection WFC imaging system can reduce the peak single-pixel laser power by 99.4% while maintaining high-quality imaging with peak signal-to-noise ratio more than 22 dB. This work pioneers what we believe to be a new path for the design of laser protection imaging systems, with promising applications in security and autonomous driving.

Implementation of an Image Tampering Detection System with a CMOS Image Sensor PUF and OP-TEE

Since image recognition systems use image data acquired by image sensors for analysis by AI technology, an important security issue is guaranteeing the authenticity of data transmitted from image sensors to successfully perform inference using AI. There have been reports of physical attacks on image sensor interfaces by tampering with images to cause misclassifications in AI classification results. As a countermeasure against these attacks, it is effective to add authenticity to image data with a message authentication code (MAC). For the implementation of this, it is important to have technologies for generating MAC keys on image sensors and to create an environment for secure MAC verification on the host device. For MAC key generation, we used the CIS-PUF technology, which generates MAC keys from PUF responses and random numbers from CMOS image sensor variations. For the secure MAC verification, we used TEE technology, which executes security-critical processes in an environment isolated from the normal operating system. In this study, we propose and demonstrate an image tampering detection system based on MAC verification with CIS-PUF and OP-TEE in an open portable TEE on an ARM processor. In the experiments, we demonstrated a system that computed and transmitted MAC for captured images using the CIS-PUF key and then performed MAC verification in the secure world of the OP-TEE.