Search Results (17)

Pixelless infrared imaging devices based on optoelectronic up-conversion offer a compact and scalable alternative to conventional focal plane arrays; however, their performance is fundamentally limited by lateral carrier diffusion, image smearing, and the trade-off between spatial resolution and conversion efficiency. Existing systems employing quantum well infrared phototransistors (QWIPTs) integrated with light-emitting diodes (LEDs) suffer from degraded modulation transfer function (MTF) at high spatial frequencies and restricted design flexibility. In this article, a quantum cascade detector (QCD)–LED pixelless imaging architecture is proposed and comprehensively modeled as a next-generation alternative. A unified analytical framework is developed to describe carrier concentration, modulation transfer function, image resolution, and image conversion efficiency (ICE) in QCD-LED systems under spatially modulated far-infrared illumination. The models explicitly account for cascade transport, diffusion–drift dynamics, photon recycling, and radiative recombination, enabling direct comparison with conventional QWIPT-LED imagers. Numerical investigations reveal that multi-stage cascade transport significantly suppresses lateral carrier spreading, resulting in a pronounced enhancement in spatial-frequency response. The proposed QCD-LED architecture demonstrates a >32.5% improvement in maximum MTF, a 32.5% increase in conversion efficiency, and a 25% enhancement in response speed, while maintaining comparable or improved image resolution. An optimal performance is achieved for a 10-stage quantum cascade detector with a 2.5 μm period length and a radiative-to-nonradiative lifetime ratio of 0.999, yielding a figure of merit (R × ICE) of 30.99, outperforming previously reported QWIPT-LED systems. Experimental validation confirms excellent agreement with theoretical predictions (R² = 0.989), particularly at high spatial frequencies where QCD-LED devices exhibit more than 140% improvement in contrast transfer. These results establish quantum cascade detector-based pixelless imagers as a robust platform for high-resolution, high-speed infrared imaging, offering superior spatial fidelity and design flexibility for next-generation optoelectronic imaging systems. Full article

Continuous-spectrum illumination induces severe channel crosstalk and resolution degradation in color imaging systems. To address this issue, this paper introduces near-monochromatic LED illumination matched to the lens design wavelength and proposes a linear crosstalk correction method based on CCD intensity superposition. Experiments under ISO 12233 evaluation reveal that mixed-color LED illumination only enhances the blue channel, while separate monochromatic illumination and image synthesis effectively eliminate crosstalk and boost overall resolution. The proposed linear method calibrates crosstalk coefficients via a standard reference area to correct single-shot mixed-color images, bringing R/B channels close to pure monochromatic performance, with minor over-sharpening in the green channel. This approach provides a feasible linear correction solution to suppress crosstalk and improve resolution for single-shot industrial color imaging with near-monochromatic LED illumination. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article

This study presents the design and optimization of a digital-imaging afocal telescope system that integrates an afocal telescope architecture with an imaging optical subsystem. The proposed system employs a combination of spherical and aspherical optical elements to enhance imaging flexibility, reduce aberrations, and ensure effective system coupling. Proper pupil matching is achieved by aligning the exit pupil of the afocal telescope with the entrance pupil of the imaging system, ensuring minimal vignetting and optimal energy transfer. Circular apertures and lens elements are used throughout the system to simplify alignment and minimize pupil-matching errors. The complete system comprises three imaging optical subsystems and a digital camera module, each independently optimized to ensure balanced optical performance. The design achieves an overall magnification of 16×, with near-diffraction-limited quality confirmed by an RMS wavefront error of 0.0474λ and a Strehl ratio of 0.915. The modulation transfer function (MTF) reaches 0.42 at 80 lp/mm, while the distortion remains below 4.87%. Chromatic performance is well controlled, with maximum lateral color deviations of 1.007 µm (short-to-long wavelength) and 1.52 µm (short-to-reference wavelength), evaluated at 656 nm, 587 nm, and 486 nm. The results demonstrate that the proposed digital-imaging afocal telescope system provides high-resolution, low-aberration imaging suitable for precision optical applications. Full article

This study presents an optical and thermal design for a compact 10× periscope zoom lens suitable for smartphones, employing a hybrid thermal compensation scheme to ensure stable imaging performance over a wide range of temperatures. Our proposed zoom optics system integrates passive and active compensation mechanisms, further enhancing thermal stability through the use of a curved image sensor. Passive compensation is achieved through the selection of low-G optical materials and an optimized structural configuration. In contrast, active compensation dynamically adjusts the zoom group position in response to changes in ambient temperature. Optical simulations confirm that this 10× periscope zoom lens, composed of a prism, eight aspherical lenses, and two parallel plates, maintains diffraction-limited resolution and less than 2% distortion at all zoom positions (Zoom 1 to Zoom 6), achieving a total depth of 4.96 mm. Thermal analysis at temperatures ranging from −20 °C to 60 °C demonstrates that the optimized design, utilizing a curved sensor (Design type 3), achieves an average MTF of 0.58 and an average degradation rate of only 12.8%, exhibiting excellent non-thermal performance. These results highlight the effectiveness of the proposed novel hybrid thermal compensation strategy and surface sensor integration in realizing high-magnification, thermally stable periscope optics for next-generation smartphone imaging systems. Full article

In recent decades, data collection technologies have evolved to facilitate the monitoring and improvement of numerous activities and processes in everyday human life. Their evolution is propelled by the advancement of artificial intelligence (AI), which aims to emulate human intelligence in the execution of related tasks. The remarkable success of deep learning (DL) and computer vision (CV) on image data prompted researchers to consider its application to time series and multivariate data. In this context, time series imaging has been identified as the research field for the transformation of time series data (a one-dimensional data format) into images (a two-dimensional data format). These data can be the variables or features of a system or phenomenon under consideration. State-of-the-art techniques for time series imaging include recurrence plot (RP), Gramian angular field (GAF), and Markov transition field (MTF). This paper proposes a novel, robust, and simple technique of time series imaging using Grayscale Fingerprint Features Field Imaging (G3FI). This novel technique is distinguished by the low resolution of the resulting image and the simplicity of the transformation procedure. The efficacy of the novel and state-of-the-art techniques for enhancing the performance of CNN-based classification models on time series datasets is thoroughly examined and compared. Full article

Background/Objectives: Mesoporous silica materials, particularly KIT-6, offer promising features, such as large surface area, tunable pore structures, and biocompatibility, making them ideal candidates for advanced drug delivery systems. The aims of this study were to develop and evaluate an innovative modified-release platform for metformin hydrochloride (MTF), using KIT-6 mesoporous silica as a matrix, to enhance oral antidiabetic therapy. Methods: KIT-6 was synthesized using an ultrasound-assisted sol-gel method and subsequently loaded with MTF via adsorption from alkaline aqueous solutions at two concentrations (1 and 3 mg/mL). The structural and morphological characteristics of the matrices—before and after drug loading—were assessed using SEM-EDX, TEM, and nitrogen adsorption–desorption isotherms (the BET method). In vitro drug release profiles were recorded in simulated gastric and intestinal fluids over 12 h. Kinetic modeling was performed using seven classical models, and a multifractal theoretical framework was used to further interpret the complex release behavior. Results: The loading efficiency increased with increasing drug concentration but nonlinearly, reaching 56.43 mg/g for 1 mg/mL and 131.69 mg/g for 3 mg/mL. BET analysis confirmed significant reductions in the surface area and pore volume upon MTF incorporation. In vitro dissolution showed a biphasic release: a fast initial phase in an acidic medium followed by sustained release at a neutral pH. The Korsmeyer–Peppas and Weibull models best described the release profiles, indicating a predominantly diffusion-controlled mechanism. The multifractal model supported the experimental findings, capturing nonlinear dynamics, memory effects, and soliton-like transport behavior across resolution scales. Conclusions: The study confirms the potential of KIT-6 as a reliable and efficient carrier for the modified oral delivery of metformin. The combination of experimental and multifractal modeling provides a deeper understanding of drug release mechanisms in mesoporous systems and offers a predictive tool for future drug delivery design. This integrated approach can be extended to other active pharmaceutical ingredients with complex release requirements. Full article

Background: Retinal ganglion cell (RGC) loss is crucial in eye diseases like glaucoma. Axon damage and dendritic degeneration precede cell death, detectable within optical coherence tomography (OCT) resolution, indicating their correlation with neuronal degeneration. The purpose of this study is to evaluate the optical changes of early retinal degeneration. Methods: The detection of optical changes in the axotomised retinal explants was completed in six C57BL/6J mice. OCT images were acquired up to 120 min from enucleation. A grey-level co-occurrence-based texture analysis was performed on the inner plexiform layer (IPL) to monitor changes in the optical speckles using a principal component analysis (PCA) and a support vector machine (SVM). In parallel tests, retinal transparency was confirmed by a comparison of the modulation transfer functions (MTFs) at 0 and 120 min. Results: Quantitative confirmation by analysis of the MTFs confirmed the non-degradation of optical transparency during the imaging period: MTF (fx) = 0.267 ± 0.02. Textural features in the IPL could discriminate between the optical signals of RGC degeneration. The mean accuracy of the SVM classification was 86.3%; discrimination was not enhanced by the combination of the SVM and PCA (81.9%). Conclusions: Optical changes in the IPL can be detected using OCT following RGC axotomy. High-resolution OCT can provide an index of retinal neuronal integrity and its degeneration. Full article

In this paper, we propose a zoom auxiliary imaging lens based on the four-component mechanical zoom method for a modulation transfer function (MTF) test system. The auxiliary imaging lenses of the current MTF test system typically use fixed-focus optical systems, which are unable to meet the test scenarios of fast and batch measurement and measure lenses with an extensive focal length range. Compared with the fixed-focus auxiliary imaging lens, the zoom auxiliary imaging lens can simultaneously satisfy the measurement of wide-angle and telephoto miniature lenses without losing measurement accuracy. The entrance pupil distance of the zoom lens is greater than that of traditional lenses, and it is constant for each focal length of the zoom lens. The zoom lens uses an intermediate real image surface to obtain the perfect image quality and lower the diameter of the rear group. Additionally, the zoom lens dynamically adjusts magnification to optimize image size and align with the detector’s pixel resolution, thereby preventing undersampling and enhancing measurement precision. The optical design is optimized for stability, delivering high resolution and minimal aberrations across the zoom range. The image quality of the zoom lens is nearly at the diffraction limit at each focal length, which significantly reduces the impact of the auxiliary lens on MTF test results, enhancing both flexibility and accuracy. This design is particularly well suited for testing miniature lenses in optoelectronic technology applications. Full article

Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard’s relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices. Full article

In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification. Full article

Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale residual network is established for the spatial deep feature branch. In addition, an enhanced residual network is established for the spectral deep feature branch This operation is adopted to enhance the spectral and spatial deep features. Finally, this method establishes the spectral–spatial deep feature simultaneity to circumvent the independence of spectral and spatial deep features. The proposed model was evaluated on three groups of real-world hyperspectral and panchromatic image datasets which are collected with a ZY-1E sensor and are located at Baiyangdian, Chaohu and Dianchi, respectively. The experimental results and quality evaluation values, including RMSE, SAM, SCC, spectral curve comparison, PSNR, SSIM ERGAS and Q metric, confirm the superior performance of the proposed model compared with the state-of-the-art methods, including AWLP, CNMF, GIHS, MTF_GLP, HPF and SFIM methods. Full article

The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data. Full article

We developed a new 2.5 μm global shutter (GS) pixel using a 65 nm process with an advanced light pipe (LP) structure. This is the world’s smallest charge domain GS pixel reported so far. This new developed pixel platform is a key enabler for ultra-high resolution sensors, industrial cameras with wide aperture lenses, and low form factors optical modules for mobile applications. The 2.5 μm GS pixel showed excellent optical performances: 68% quantum efficiency (QE) at 530 nm, ±12.5 degrees angular response (AR), and quite low parasitic light sensitivity (PLS)—10,400 1/PLS with the F#2.8 lens. In addition, we achieved an extremely low memory node (MN) dark current 13 e⁻/s at 60 °C by fully pinned MN. Furthermore, we studied how the LP technology contributes to the improvement of the modulation transfer function (MTF) in near infrared (NIR) enhanced GS pixel. The 2.8 μm GS pixel using a p-substrate showed 109 lp/mm MTF@50% at 940 nm, which is 1.6 times better than that without an LP. The MTF can be more enhanced by the combination of the LP and the deep photodiode (PD) electrically isolated from the substrate. We demonstrated the advantage of using LP technology and our advanced stacked deep photodiode (SDP) technology together. This unique combination showed an improvement of more than 100% in NIR QE while maintaining an MTF that is close to the theoretical Nyquist limit (MTF @50% = 156 lp/mm). Full article

Pansharpening is the process of integrating a high spatial resolution panchromatic image with a low spatial resolution multispectral image to obtain a multispectral image with high spatial and spectral resolution. Over the last decade, several algorithms have been developed for pansharpening. In this paper, a technique, called enhanced back-projection (EBP), is introduced and applied as postprocessing on the pansharpening. The proposed EBP first enhances the spatial details of the pansharpening results by histogram matching and high-pass modulation, followed by a back-projection process, which takes into account the modulation transfer function (MTF) of the satellite sensor such that the pansharpening results obey the consistency property. The EBP is validated on four datasets acquired by different satellites and several commonly used pansharpening methods. The pansharpening results achieve substantial improvements by this postprocessing technique, which is widely applicable and requires no modification of existing pansharpening methods. Full article