Imaging Lens Research Articles

Quantitative phase imaging endoscopy with a metalens

Quantitative phase imaging (QPI) recovers the exact wavefront of light from intensity measurements. Topographical and optical density maps of translucent microscopic bodies can be extracted from these quantified phase shifts. We demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid metalens. Our method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color camera. Our 0.5 mm aperture metalens shows robust quantitative phase imaging capability with a 28∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${28}^{\\circ}$$\\end{document} field of view and 0.2π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${2}{\\pi}$$\\end{document} phase resolution ( ~ 0.1λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${1}{\\lambda}$$\\end{document} in air) for experiments with an endoscopic fiber bundle. Since the spectral functionality is encoded directly in the imaging lens, the metalens acts both as a focusing element and a spectral filter. The use of a simple computational backend will enable real-time operation. Key limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition, interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.

A Strong Gravitational Lens Is Worth a Thousand Dark Matter Halos: Inference on Small-scale Structure Using Sequential Methods

Strong gravitational lenses are a singular probe of the Universe’s small-scale structure—they are sensitive to the gravitational effects of low-mass (<1010 M ⊙) halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have shown promise in extracting the halo-population signal. However, it is unclear whether the constraints from these models are limited by the methodology or the data. In this study, we introduce an accelerator-optimized simulation pipeline that can generate lens images with realistic subhalo populations in milliseconds. Leveraging this simulator, we identify the main limitation of our fiducial SBI analysis: training set size. We then adopt a sequential neural posterior estimation (SNPE) approach, allowing us to refine the training distribution to align with the observed data. Using only one-fifth as many mock Hubble Space Telescope images, SNPE matches the constraints on the low-mass halo population produced by our best nonsequential model. Our experiments suggest that an over 3 order-of-magnitude increase in training set size and GPU hours would be required to achieve an equivalent result without sequential methods. While the full potential of the existing lens sample remains to be explored, the notable improvement in constraining power enabled by our sequential approach highlights that current constraints are limited primarily by methodology and not the data itself. Moreover, our results emphasize the need to treat training set generation and model optimization as interconnected stages of any cosmological analysis using SBI.

Point spread function engineering enable resolution enhanced imaging for Interferenceless coded aperture correlation holography

The point spread function (PSF) of an optical system could characterize the resolving ability of the whole optical system for point light sources. Therefore, the imaging performance of the system could be significantly improved by regulating and optimizing the PSF. In this paper, we innovatively propose a single-exposure hologram resolution enhanced cross-correlation (RECC) method for Interferenceless coded aperture holography(I-COACH) system, circumventing the necessity to obtain the point spread hologram (PSH) of an ideal point object. The RECC method firstly acquires an approximate image of a large-size point object by Lucy-Richardson (LR) algorithm in lens imaging mode, and takes it as a PSF to acquire a PSH with ideal size of the I-COACH system by LR algorithm again, and finally acquires a reconstructed image by the single-exposure hologram RECC method. In the RECC method, the approximate ideal PSHs at different axial positions of the system are acquired by offline operation, therefore, it has a high imaging temporal resolution, and the imaging transverse resolution is not affected by the size of the point objects at the time of recording the PSH, which provides a high imaging signal-to-noise ratio and stable resolution. The proposed method provides powerful technical support for further extending the application field of the I-COACH system, and provides technical reference for other incoherent imaging.

Sulfur‐Rich Norbornadiene‐Derived Infrared Transparent Polymers by Inverse Vulcanization

Infrared (IR) transparent polymer materials prepared by inverse vulcanization, as a promising candidate to replace inorganic materials, are new materials for constructing key devices in IR optics. However, it is difficult to achieve a balance between infrared optical and thermal properties in polymers due to the intrinsic infrared absorption of organic materials. Herein, our strategy is to construct a high boiling point symmetrical molecular norbornadiene derivative cross‐linking agent (DMMD) which can be inverse vulcanized with molten sulfur, and obtain Poly(S‐r‐DMMD) with different sulfur content by controlling the feed ratio of sulfur. With the rigid core and low IR activity in DMMD, the prepared polymers exhibit tunable thermal properties (Tg: 98.3‐119.8 °C) and high IR transmittance (medium‐wave infrared region (MWIR): 42.9‐52.6 %; long‐wave infrared region (LWIR): 1.5‐5.29 %). In addition, Poly(S‐r‐DMMD) can be used to prepare large‐size free‐standing Fresnel lenses for IR imaging by simple hot‐pressing, which provides flexibility in the design and production of IR fine lenses. This study provides a novel strategy for balancing the thermal and optical properties of IR transparent polymer materials, while providing relevant references for balancing the IR optical and thermal properties of polymer materials.

Additively Manufactured Structures for Precise and Robust Mounting of Optical Elements

The design freedom of Additive Manufacturing offers new opportunities for the design of mounting structures of optical systems, which are not feasible for conventional manufacturing approaches, thus opening up new areas of application for optical systems. We use this to develop a fully monolithic mounting structure for precise positioning of the optical elements, while simultaneously increasing their robustness against harsh environmental conditions. We additively manufacture such a mounting structure for an imaging lens and evaluate its optical performance with interferometric measurement of its wavefront, before and after applying a mechanical shock to the entire system. The results indicate a centering accuracy better than 6~µm, both in the initial positioning as well as the subsequent repositioning after a mechanical shock of 15G.

Dielectric-elastomer-driven long-wave infrared Alvarez lenses for continuous zooming imaging

Dielectric-elastomer-driven long-wave infrared Alvarez lenses for continuous zooming imaging

A qualitative validation of an in-situ monitoring system for EHD inkjet printing via laser diffraction

Electrohydrodynamic inkjet printing enables high-resolution patterning for nano features. In-flight dynamics of EHD inkjet printing play an essential role in the quality control of printing results. We applied a laser diffraction/scattering in-situ analyzing setup for the EHD inkjet printing system to replace the zoom lens and high-speed camera imaging system. In contrast to conventional imaging systems, the laser diffraction/scattering system is based on analyzing the diffraction pattern and scattering intensity, respectively, which provided higher resolution for micro-scale jetting measurement and enabled sub-micron level jetting correlation between the voltage applied to the electrode and printing results. Furthermore, Taylor cone information from the nozzle head could also be analyzed in real-time to make adjustments to the printing process. In this work, we successfully validated the feasibility of laser diffraction analysis in-situ monitoring for EHD inkjet printing at micron and sub-micron levels.

Multi-population differential evolution approach for feature selection with mutual information ranking

Feature selection is a crucial aspect of data preprocessing because of the significant effect of redundant features on classification performance and the extensive computational resources required. Evolutionary algorithm-based feature-selection methods have shown remarkable results in determining the optimal feature subset. To further enhance classification performance, this paper proposes a novel multi-population differential evolution approach for feature selection with mutual information ranking (MI-MPODE). Firstly, population preprocessing guided by mutual information is employed to reduce the dimensionality of the initial feature space. Then, the feature subset obtained from mutual information serves as the initial population for MI-MPODE. MI-MPODE incorporates a novel multi-population information-sharing mechanism, with common individuals from three layers contributing to inter-subpopulation information sharing. Additionally, an individual enhancement strategy is proposed to handle the variations of individuals in the population, and a Lens imaging opposition-based learning method is adopted to improve the algorithm’s optimization capability. MI-MPODE is compared with several state-of-the-art evolutionary algorithm-based feature selection methods through experimental comparisons. The results show that MI-MPODE outperforms all comparison algorithms on more than half of the datasets, with a significant reduction in the number of features used, demonstrating a significant advantage over competitors.

Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation.

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.

The effects of an augmented reality lens imaging learning system on students’ science achievement, learning motivation, and inquiry skills in physics inquiry activities

The effects of an augmented reality lens imaging learning system on students’ science achievement, learning motivation, and inquiry skills in physics inquiry activities

Fault diagnosis of photovoltaic array with multi-module fusion under hyperparameter optimization

Photovoltaic (PV) arrays’ random and intermittent output characteristics impact power system safety. To improve the performance of the PV array fault diagnosis model, a novel online fault monitoring technique is introduced. (1) Fault diagnostic model construction: Significant differences in PV arrays’ I-V and P-V curves under various fault conditions led to constructing a 3D channel feature map based on I, V, and P features. (2) Multi-source information fusion network (MSIFN): this multi-module fusion model includes a time–frequency domain fusion module (TDFM), a multi-feature shuffle expansion convolution module (MSECM), a parameter-free parallel hybrid attention enhancement module, and a multi-scale mixed pooling fusion classification module (MMPCM). (3) Multi-strategy fusion whale optimization algorithm (MSFWOA): addressing the original WOA’s deficiencies, we designed time control, parameter modification, and greedy control strategies based on lens imaging to optimize MSIFN’s hyper-parameters. Experimental results show that the MSFWOA-MSIFN model excels in PV array fault diagnosis (Paccuracy=Pprecision=Precall = 99.92 %). In three types of noise experiments with 15 dB, 25 dB, and 30 dB, the average performance index remained above 99 %. In practical experiments, the average performance indices werePaccuracy = 97.53 %, Pprecision = 97.32 %, andPrecall = 97.41 %, further demonstrating its excellent diagnostic performance. This model effectively diagnoses various faults in PV arrays, providing scientific and theoretical support for PV system operations.

A broadband acoustic metamaterial lens for sub-wavelength imaging based on bandwidth enhancement

Acoustic metamaterials manufactured from natural materials can exhibit exotic characteristics that cannot be found in nature materials through the design of geometric structures and resonance effects, and acoustic metamaterials can be used to achieve sub-wavelength acoustic imaging as the acoustic metamaterial lens (AML). Currently, the AMLs based on the Fabry–Pérot (FP) resonance principle are all with single effective length hole, which is directly related to the thickness of its structure. They can only achieve acoustic imaging in a relatively narrow frequency range, and there is no AML which can achieve broadband sub-wavelength acoustic imaging. In this paper, a kind of broadband acoustic metamaterial lens (BAML) is introduced. While maintaining a constant overall thickness of the AML, a design which is composed of the supercell consisting of a cross distribution of different effective length holes is proposed. This design enables the AML formed by these supercells to encompass multiple operating frequencies corresponding to various holes, thereby broadening the frequency range of acoustic imaging. Finite element simulation and imaging experiments were conducted, and results demonstrated that the BAML consisting of multiple effective length holes has the ability to enhance bandwidth of acoustic imaging. Compared to AML with single effective length hole, it offers a broader range of imaging frequencies, this design offers a general method of bandwidth enhancement. BAML has potential application value in ultrasonic imaging, sound absorption, intelligent thermal control and medical diagnosis.

Investigating the impact of OCT imaging of the crystalline lens on the accuracy and precision of cataract assessment.

To determine if supplementing standard clinical assessments with Optical Coherence Tomography (OCT) imaging of the crystalline lens improves the accuracy and precision of lens opacity assessment and associated clinical management decisions by optometrists. Fifty optometrists registered in the UK or Éire undertook a clinical vignette study where participants graded lens opacities and made associated clinical management decisions based on the image(s)/information displayed. Three forms of vignettes were presented: (1) Slit-lamp (SL) images of the lens, (2) SL and OCT images and (3) SL, OCT and visual function measures. Vignettes were constructed using anonymised data from 50 patients with varying cataract severity, each vignette being presented twice in a randomised order (total vignette presentations = 300). The accuracy of opacity and management decisions were evaluated using descriptive statistics and non-parametric Bland-Altman analysis where assessments from experienced clinicians were the reference. The precision of assessments was examined for each vignette form using non-parametric Bland-Altman analysis. All (n = 50) participants completed the study, with 36 working in primary eyecare (primary eyecare) settings and 14 in hospital eyecare services (HES). Agreement was highest where vignettes contained all clinical data (i.e., SL, OCT and visual function data-grading: 51.0%, management: 50.5%), and systematically reduced with decreasing vignette content (p < 0.001). A larger number of vignettes containing imaging and visual function measures exhibited below reference (i.e., less conservative) grading compared with vignettes containing imaging data alone (all p < 0.05). HES-based optometrists were more likely to grade lens opacities lower than clinicians working in primary eyecare (p < 0.001). Good measurement precision was evident for all vignettes, with a mean bias close to zero and limits of agreement below one grading step for all conditions. The addition of anterior segment OCT to SL images improved the accuracy of lens opacity grading. Structural assessment alone yielded more conservative decision making, which reversed once visual functional data was available.

On-axis full-field swept-source optical coherence tomography for murine retinal imaging

A full-field swept-source optical coherence tomography (FF-SS-OCT) for in vivo murine retinal imaging is demonstrated. The on-axis FF-SS-OCT system was built in a Mach–Zehnder interferometer configuration employing a tunable laser source with an adjustable sweep rate and sweep range in conjunction with a fast 2D-CMOS camera. A large field retinal (coherent) illumination was accomplished using an imaging interface comprised of a short-focal length imaging lens and a contact lens. The magnification between the camera and retina (spatial sampling) was appropriately chosen to record the microscopic structural features of the retina in the image. A pupil stop was employed in the detection path to reject unwanted backscattering from the mouse eye and other sources and limit aberrations distorting the retinal images. In vivo mouse retinal imaging was performed at a sweep rate of 150 Hz to acquire volumes unaffected by the system vibrations, which predominated at lower frequencies. Operating the FF-SS-OCT at this speed yielded an effective axial scan rate of 20 million A-scans/s and a field of view of 820 × 410 µm (24.12° × 12.06°). High-quality retinal B-scans and enface images of the retina were obtained with the SS-FF-OCT, revealing all major retinal layers and vascular plexuses.

Curriculum learning for ab initio deep learned refractive optics

Deep optical optimization has recently emerged as a new paradigm for designing computational imaging systems using only the output image as the objective. However, it has been limited to either simple optical systems consisting of a single element such as a diffractive optical element or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a DeepLens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces without human intervention, therefore overcoming the need for a good initial design. We demonstrate the effectiveness of our approach by fully automatically designing both classical imaging lenses and a large field-of-view extended depth-of-field computational lens in a cellphone-style form factor, with highly aspheric surfaces and a short back focal length.

CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path Planning.

The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.

Simultaneous observation of multiple interferograms with Mach-Zehnder type electron interferometer on a 1.2-MV field-emission transmission electron microscope

Abstract We developed a Mach-Zehnder type electron interferometer (MZ-EI) that enables simultaneous observation of interferograms created at multiple output locations on a 1.2-MV field-emission transmission electron microscope. This MZ-EI is composed of two single-crystal thin films, a lens located between the single-crystal thin films and imaging lenses. By comparing interferograms created by electron waves travelling through different beam paths, we found that the relative phase difference was caused by phase modulation passing through the single crystals and by aberrations and defocus values of the lenses. We also confirmed that the relative phase difference can be controlled using the tilted illumination method.

An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems

As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.

Forecasting a Short-Term Photovoltaic Power Model Based on Improved Snake Optimization, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network.

The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network-bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network-bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions.

Asymmetrical Three-Dimensional Conformal Imaging Lens

Asymmetrical Three-Dimensional Conformal Imaging Lens