Search Results (83)

Achieving rapid and accurate object classification holds significant importance in various domains. However, conventional vision-based techniques suffer from several limitations, including high data redundancy and strong dependence on image quality. In this work, we present a high-speed, image-free object classification method based on dual-pixel measurement and normalized central moment invariants. Leveraging the complementary modulation capability of a digital micromirror device (DMD), the proposed system requires only five tailored binary illumination patterns to simultaneously extract geometric features and perform classification. The system can achieve a classification update rate of up to 4.44 kHz, offering significant improvements in both efficiency and accuracy compared to traditional image-based approaches. Numerical simulations verify the robustness of the method under similarity transformations—including translation, scaling, and rotation—while experimental validations further demonstrate reliable performance across diverse object types. This approach enables real-time, low-data throughput, and reconstruction-free classification, offering new potential for optical computing and edge intelligence applications. Full article

In the framework of the SURPRISE EU project, the Compressive Sensing paradigm was applied for the development of a laboratory demonstrator with improved spatial sampling operating from visible up to Medium InfraRed (MIR). The demonstrator, which utilizes a commercial Digital Micromirror Device modified by replacing its front window with one transparent up to MIR, has 10 bands in the VIS-NIR range and 2 bands in the MIR range, showing a super resolution factor up to 32. Measurements performed in the MIR spectral range using hot sources as targets show that CS is effective in reconstructing super-resolved hot targets. Full article

The output energy of Laser Active Optical Systems (LAOSs), in which image brightness is amplified within the laser-active medium, is always higher than the input energy. This contrasts with conventional optical systems (OSs). As a result, a LAOS enables the creation of laser beams with tailored energy distribution across the aperture, making them ideal for material processing applications. This concept was first successfully implemented using metal vapor lasers as the gain medium. In these systems, material processing was achieved by using a laser beam that either carried the required energy profile or the image of the object itself. Later, other laser media were utilized for LAOSs, including barium vapor, strontium vapor, excimer XeCl lasers, and solid-state media. Additionally, during the development of these systems, several modifications were introduced. For example, Space-Time Light Modulators (STLMs) and CCD cameras were incorporated, along with the use of multipass amplifiers, disk-shaped or thin-disk (TD) solid-state laser amplifiers, and other advancements. These techniques have significantly expanded the range of power, energy, pulse durations, and operating wavelengths. Currently, TD laser amplifiers and STLMs based on Digital Light Processor (DLP) technology or Digital Micromirror Devices (DMDs) enhance the potential to develop LAOS devices for Subtractive and Additive Technologies (ST, AT), applicable in both macromachining (cutting, welding, drilling) and micro-nano processing. This review presents comparable characteristics and requirements for these various LAOS applications. Full article

Conventional vision-based sensors face limitations such as low update rates, restricted applicability, and insufficient robustness in dynamic environments with complex object motions. Single-pixel tracking systems offer high efficiency and minimal data redundancy by directly acquiring target positions without full-image reconstruction. This paper proposes a single-pixel detection system for adaptive multi-target tracking based on the geometric moment and the exponentially weighted moving average (EWMA). The proposed system leverages geometric moments for high-speed target localization, requiring merely $\mathit{3}\mathit{N}$ measurements to resolve centroids for $\mathit{N}$ targets. Furthermore, the output values of the system are used to continuously update the weight parameters, enabling adaptation to varying motion patterns and ensuring consistent tracking stability. Experimental validation using a digital micromirror device (DMD) operating at 17.857 kHz demonstrates a theoretical tracking update rate of 1984 Hz for three objects. Quantitative evaluations under 1920 × 1080 pixel resolution reveal a normalized root mean square error (NRMSE) of 0.00785, confirming the method’s capability for robust multi-target tracking in practical applications. Full article

Three-dimensional (3D) photolithography has found wide applications in microelectronics, optoelectronics, biomedicine, etc. Traditionally, it requires repetitive exposure and developing cycles. Meanwhile, a laser direct writing (LDW) system with a digital micromirror device (DMD) enables high-speed maskless lithography with programmable doses. In this paper, we propose a quasi-3D digitized photolithography via LDW with a DMD to remove multiple developing cycles from the process. This approach quantizes the dose of the 3D geometry and stores it in a grayscale image. And the entire dose distribution can be formed by overlapping the exposures with sliced binary dose maps from the above grayscale dose map. In the image slicing algorithm, a modified Golomb coding is introduced to make full use of the highest available exposure intensity. Both 1D multi-step patterns and diffractive optical devices (DOEs) have been fabricated to verify its feasibility. This type of digitized quasi-3D photolithography can be applied to fabricating DOEs, microlens arrays (MLAs), micro-refractive optical elements (μROEs), etc., and 3D molds for micro-embossing/nano-imprinting. Full article

Stellar radiation simulation is critical in the space industry; however, with the current simulation methods, only a single color temperature and magnitude can be modulated at a time. Furthermore, star sensors rely on star observation tests for accurate calibration; this seriously restricts their development. This paper presents a novel star spectral information multi-partition mapping simulation method to closely simulate real sky star map information, thus replacing non-scenario-specific field stargazing experiments. First, using the stellar spectral simulation principle, a multi-partition mapping principle based on a digital micro-mirror device is proposed, and the theoretical basis of sub-region division is provided. Second, multi-component mapping simulation of stellar spectral information is expounded, and a general architecture for the same based on a double-prism symmetry structure is presented. Next, the influence of peak spectral half-peak width and peak interval on spectral simulation accuracy is analyzed, and a pre-collimated beam expansion system, multi-dimensional slit, and spectral splitting system are designed accordingly. Finally, a test platform is set up, and single-region simulation results and multi-region consistency experiments are conducted to verify the feasibility of the proposed method. Our method can realize high-precision simulation and independently control the output of various color temperatures and magnitudes. It provides a theoretical and technical basis for the development of star sensor ground calibration tests and space target detection light environment simulation. Full article

A multi-order combined diffraction spatial filter, integrated with a set of Zernike phase functions (representing wavefront aberrations) and Zernike polynomials, enables the simultaneous formation of multiple aberration-transformed point spread function (PSF) patterns in a single plane. This is achieved using an optical Fourier correlator and provides significantly more information than a single PSF captured in focal or defocused planes—all without requiring mechanical movement. To analyze the resulting complex intensity patterns, which include 49 diffraction orders, a convolutional neural network based on the Xception architecture is employed. This model effectively identifies wavefront aberrations up to the fourth Zernike order. After 80 training epochs, the model achieved a mean absolute error (MAE) of no more than 0.0028. Additionally, a five-fold cross-validation confirmed the robustness and reliability of the approach. For the experimental validation of the proposed multi-order filter, a liquid crystal spatial light modulator was used. Optical experiments were conducted using a Fourier correlator setup, where aberration fields were generated via a digital micromirror device. The experimental results closely matched the simulation data, confirming the effectiveness of the method. New advanced aberrometers and multichannel diffractive optics technologies can be used in industry for the quality control of optical elements, assessing optical system alignment errors, and the early-stage detection of eye diseases. Full article

Full-field optical coherence tomography (FF-OCT) is a fast, en face interferometric technique that allows imaging inside a scattering tissue with high spatial resolution. However, camera-based detection, which lacks confocal gating, results in a suboptimal signal-to-noise ratio (SNR). To address this, we implemented a time-domain FF-OCT system that uses a digital micromirror device (DMD). The DMD allows us to scan multiple illumination spots across the sample and simultaneously realize confocal detection with multiple pinholes. Confocal imaging can also be demonstrated with line illumination and detection. Using a USAF target mounted behind a scattering layer, we demonstrate an order-of-magnitude improvement in SNR. Full article

The architectures of hydrogels fabricated with stereolithography (SLA) 3D printing systems have played various roles in bioengineering applications. Typically, the SLA systems successively illuminated light to a layer of photo-crosslinkable hydrogel precursors for the fabrication of hydrogels. These SLA systems can be classified into point-scanning types and digital micromirror device (DMD) types. The point-scanning types form layers of hydrogels by scanning the precursors with a focused light, while DMD types illuminate 2D light patterns to the precursors to form each hydrogel layer at once. Overall, SLA systems were cost-effective and allowed the fabrication of hydrogels with good shape fidelity and uniform mechanical properties. As a result, hydrogel constructs fabricated with the SLA 3D printing systems were used to regenerate tissues and develop lab-on-a-chip devices and native tissue-like models. Full article

Food safety is gaining increasing attention worldwide. Currently, low-density organic foreign objects such as insects are extremely challenging to detect using conventional metal detectors and X-ray inspection systems. This study aimed to develop a visible-near-infrared single-pixel imaging (Vis-NIR-SPI) method to detect small insects inside food. The advantages of Vis-NIR light include its ability to analyze samples non-destructively and measure multiple components simultaneously and quickly, while SPI is robust against dark noise, high scattering, and high equipment costs. The experimental results demonstrated that (1) the newly designed system effectively reduces scattering effects from the highly scattering sample (intralipid 20%), allowing for the capture of information beyond the capabilities of a charge-coupled-device camera; (2) insects positioned behind ham and bread were readily detectable using the imaging reconstruction algorithm; and (3) even for chocolate samples with very high light absorption, only 1 uncontaminated sample out of 100 was mistakenly classified as contaminated, yielding an overall accuracy of 99%. This high level of accuracy underscores the potential of the Vis-NIR-SPI method to provide reliable detection while maintaining sample integrity. Furthermore, this method is cost-effective, offering a practical and efficient solution to improve quality control processes and consumer trust in the food industry. Full article

Hyperspectral video acquisition requires a precise balance between spectral and temporal resolution, often achieved through compressive sampling using two-dimensional detectors and spectral reconstruction algorithms. However, the reliance on spatial light modulators for coding reduces optical efficiency, while complex recovery algorithms hinder real-time reconstruction. To address these challenges, we propose a digital-micromirror-device-based complementary dual-channel hyperspectral (DMD-CDH) video imaging system. This system employs a DMD for simultaneous light splitting and spatial encoding, enabling one channel to perform non-aliasing spectral sampling at lower frame rates while the other provides complementary high-rate sampling for panchromatic video. Featuring high optical throughput and efficient complementary sampling, the system ensures reliable hyperspectral video reconstruction and serves as a robust ground-based validation platform for remote sensing applications. Additionally, we introduce tailored optical error calibration and fixation techniques alongside a lightweight hyperspectral fusion network for reconstruction, achieving hyperspectral frame rates exceeding 30 fps. Compared to the existing models, this system simplifies the calibration process and provides a practical high-performance solution for real-time hyperspectral video imaging. Full article

We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid imaging of highly sparse scenes at the native digital micromirror device (DMD) resolution of 1024 × 768. The reconstruction algorithm consists of two stages. In the first stage, the vector of SPI measurements is multiplied by the generalized inverse of the measurement matrix. In the second stage, we compare two reconstruction approaches: one based on an iterative algorithm and the other on a trained neural network. The neural network outperforms the iterative method when the object resembles the training set, though it lacks the generality of the iterative approach. For images captured at a compression of 0.41 percent, corresponding to a measurement rate of 6.8 Hz with a DMD operating at 22 kHz, the typical reconstruction time on a desktop with a medium-performance GPU is comparable to the image acquisition rate. This allows the proposed SPI method to support high-resolution dynamic SPI in a variety of applications, using a standard SPI architecture with a DMD modulator operating at its native resolution and bandwidth, and enabling the real-time processing of the measured data with no additional delay on a standard desktop PC. Full article

The 3D printing method based on digital light processing (DLP) technology can transform liquid resin materials into complex 3D models. However, due to the limitations of digital micromirror device (DMD) specifications, the normal DLP 3D printing method (NDPM) cannot simultaneously process large-size and small-feature parts. Therefore, a scalable DLP 3D printing method (SDPM) was proposed. Different printing resolutions for a part were designed by changing the distance between the projector and the molding liquid level. A scalable DLP printer was built to realize the printing resolution requirements at different sizes. A series of experiments were performed. Firstly, the orthogonal experimental method was used, and the minimum and maximum projection distances were obtained as 20.5 cm and 30.5 cm, respectively. Accordingly, the layer thickness, exposure time, and waiting leveling time were 0.08 mm, 3 s, and 6 s and 0.08 mm, 7 s, and 10 s. Secondly, single-layer column feature printing was finished, which was shown to have two minimum printing resolutions of 101 μm and 157 μm at a projection distance of 20.5 cm and 30.5 cm. Thirdly, a shape accuracy test was conducted by using the SDPM. Compared with the NDPM, the shape accuracy of the small-feature round, diamond, and square parts was improved by 49%, 42%, and 2%, respectively. This study verified that the SDPM can build models with features demonstrating high local shape accuracy. Full article

With the increasing acquisition speed of image sensors, it has become necessary to provide image sources with higher frame rates and grayscale in order to meet testing requirements. In the field of semi-physical simulation projection, digital micromirror devices are often chosen for their high resolution, uniformity, response speed, and energy concentration. In this study, we utilized digital micromirror devices to construct a high-frame-rate and large-grayscale simulation projection system. To achieve our experiment goals, we employed two digital micromirror devices. One DMD was used to modulate the light intensity of the light source, while the other generated images with different bit planes. By projecting the target images onto the image sensor, we were able to achieve a frame rate of 1611 hz for the projected 12-bit image. This system meets the requirements for our experiment design and ensures the accurate testing of image sensors. Full article

We advanced the practical development of compressive hyperspectral cameras for remote sensing scenarios with a design that simultaneously compresses and captures high-quality spectral information of a scene via configurable measurements. We built a prototype imaging system that is compatible with light-modulation devices that encode the incoming spectrum. The sensing approach enables a substantial reduction in the volume of data collected and transmitted, facilitating large-scale remote hyperspectral imaging. A main advantage of our sensing design is that it allows for adaptive sampling. When prior information of a survey region is available or gained, the modulation patterns can be re-programmed to efficiently sample and detect desired endmembers. Given target spectral signatures, we propose an optimization scheme that guides the encoding process. The approach severely reduces the number of required sampling patterns, with the ability to achieve image segmentation and correct distortions. Additionally, to decode the modulated data, we considered a novel reconstruction algorithm suited for large-scale images. The computational methodology leverages the multidimensional structure and redundant representation of hyperspectral images via the canonical polyadic decomposition of multiway arrays. Under realistic remote sensing scenarios, we demonstrated the efficiency of our approach with several data sets collected by our prototype camera and reconstructed by our low-rank tensor decoder. Full article