Self-inspired learning for denoising live-cell super-resolution microscopy.

Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances.

Single-Shot 3D Imaging Meta-Microscope.

Three-dimensional (3D) imaging enables high-precision and high-resolution axial positioning, which is crucial for biological imaging, semiconductor defect monitoring, and other applications. Conventional implementations rely on bulky optical elements or scanning mechanisms, resulting in low speed and complicated setups. Here, we generate the double-helix (DH) point spread function with an all-dielectric metasurface and thus innovate the 3D imaging microscope (hence dubbed meta-microscope), both in 4f and 2f imaging systems. The 4f-meta-microscope with a numerical aperture of 0.7 achieves an axial localization accuracy below 0.12 µm within a 15.47 µm detection range, while the 2f-DH meta-microscope with a numerical aperture of 0.3 shows a 1.12 µm accuracy within a 227.33 µm range. We also demonstrate single-shot and accurate 3D biological imaging of the mouse kidney tissue and peach anther, providing a comprehensive and efficient approach for 3D bioimaging and other applications through a single-shot 3D meta-microscope.

Multispectral stealth multilayer etched film structure based on ultrathin silver.

Spontaneous infrared radiation dissipation is a critical factor in facilitating object cooling, which influences the thermal stability and stealth efficacy of infrared stealth devices. Furthermore, the compatibility between efficient visible, infrared, and radar stealth is challenging due to different camouflage principles in different bands. This Letter presents a five-layer etched film structure to achieve multispectral stealth, and the utilization of the high-quality ultrathin silver films enables highly efficient infrared selective emission. This etched film structure with few layers demonstrates potential applications in diverse domains, including multi-band anti-detection and multispectral manipulation.

Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

3D Shape Measurement of Aeroengine Blade Based on Fringe Projection Profilometer Improved by Multi-Layer Concentric Ring Calibration.

The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring calibration plate. The effectiveness of this calibration method was verified through actual testing of standard ball gauges. Compared with the checkerboard-grid calibration method, the average deviation of the multilayer concentric ring calibration method for measuring the center distance of the standard sphere is 0.02352, which improves the measurement accuracy by 3-4 times. On the basis of multi-layer concentric ring calibration, this study builds a fringe projection profiler based on the three-frequency twelve-step phase shift method. Compared with the CMM, the average deviation of the blade chord length measured by this solution is 0.064, which meets the measurement index requirements of aeroengine fan blades.

A Method for Measuring Parameters of Defective Ellipse Based on Vision.

Ellipse detection has a very wide range of applications in the field of object detection, especially in the geometric size detection of inclined microporous parts. However, due to the processing methods applied to the parts, there are certain defects in the features. The existing ellipse detection methods do not meet the needs of rapid detection due to the problems of false detection and time consumption. This article proposes a method of quickly obtaining defective ellipse parameters based on vision. It mainly uses the approximation principle of circles to repair defective circles, then combines this with morphological processing to obtain effective edge points, and finally uses the least squares method to obtain elliptical parameters. By simulating the computer-generated images, the results demonstrate that the center fitting error of the simulated defect ellipses with major and minor axes of 600 and 400 pixels is less than 1 pixel, the major and minor axis fitting error is less than 3 pixels, and the tilt angle fitting error is less than 0.1°. Further, experimental verification was conducted on the engine injection hole. The measurement results show that the surface size deviation was less than 0.01 mm and the angle error was less than 0.15°, which means the parameters of defective ellipses can obtained quickly and effectively. It is thus suitable for engineering applications, and can provide visual guidance for the precise measurement of fiber probes.

Method of micro hole tilt adjustment based on a vision-guided touch probe.

In order to meet the increasing miniaturization and high precision requirements of high-performance devices in aerospace and other fields for space array micro holes with a high aspect ratio, a method of measuring geometric parameters by penetrating the micro holes with a contact probe guided by vision is proposed, which can achieve rapid and efficient measurements. This method adopts the principle of vision measurement, preliminarily determines the geometric parameters of measurement through the processing of micropore images, and then needs to establish a collaborative measurement model of vision and probe using the principle of vision to guide the probe to go deep into the hole to measure and adjust the inclined micropores. According to this principle, a five-axis measuring system is set up, and a hole with a diameter of 3 mm is tested at different angles. The experimental results preliminarily verify the effectiveness and feasibility of the proposed method.

High-precision autocollimation method based on a multiscale convolution neural network for angle measurement.

A high-precision autocollimation method based on multiscale convolution neural network (MSCNN) for angle measurement is proposed. MSCNN is integrated with the traditional measurement model. Using the multiscale representation learning ability of MSCNN, the relationship between spot shape (large-scale feature), gray distribution (small-scale feature), and the influence of aberration and assembly error in the collimating optical path is extracted. The constructed accurate nonlinear measurement model directly improves the uncertainty of angle measurement. Experiments demonstrate that the extended uncertainty reaches 0.29 arcsec (k = 2), approximately 7 times higher than that with the traditional measurement principle, and solves the nonlinear error caused by aberration and assembly error in the autocollimation system. Additionally, this method has a good universality and can be applied to other autocollimation systems.

Simulation-driven learning: a deep learning approach for image scanning microscopy via physical imaging models.

Image reconstruction based on deep learning has become an effective tool in fluorescence microscopy. Most deep learning reconstruction methods ignore the mechanism of the imaging process where a large number of datasets are required. In addition, a lot of time is spent solving the aliasing problem from multi-scaled image pairs for data pre-processing. Here we demonstrate an improved generative adversarial network for image scanning microscopy (ISM) that can be trained by simulation data and has good generalization. Based on physical imaging models, this method can generate matching image pairs from simulation images and uses them as datasets for network training, without capturing a large number of real ISM images and avoiding image alignment preprocessing. Simulation and experimental results show that this simulation data-driven method improves the imaging quality of conventional microscopic images and reduces the cost of experiments. This method provides inspiration for optimizing network generalizability of the deep learning network.

Interferometric laser imaging for droplet-size measurement in spray.

An upgraded droplet-size measurement method, based on laser interference particle imaging (IPI) technology, is applied to accomplish high-precision measurement of particle size and spatial distribution of gas-liquid two-phase flow in the atomization field. In this study, an improved morphological-Hough transform interference fringe location algorithm is applied to IPI measurement. The particle size of the standard particle field with a diameter of 24 µm is measured by the upgraded IPI measurement experimentally, whose absolute error and relative error are 0.14 µm and 0.58%, respectively. The atomization field of the 400 µm centrifugal nozzle under different pressures is demonstrated by direct imaging and IPI technology, where the assessment results are evaluated by SMD value and particle size distribution, and the results exhibit good agreement.

Precision roll angle measurement system based on autocollimation.

The traditional autocollimation method is widely used for small angle measurement due to its high precision and high resolution, but it cannot be used to measure the roll angle. To overcome this problem, a roll angle measurement method based on autocollimation is proposed in this paper. To achieve roll angle measurement, a transmission grating is selected to generate a pair of measurement beams, and a combined target is designed as the angle sensor. A roll angle with higher accuracy and resolution can be obtained by differential measurement, because the measurement error introduced by the beam angle drift of the light source can be effectively suppressed. A series of experiments is carried out to verify the performance of the proposed system. In the experiments, the resolution of the roll angle is better than 0.05 arcsec, and the accuracy of the system is 0.20 arcsec with a measurement range of 250 arcsec.

Nonlinear correction of a laser scanning interference system based on a fiber ring resonator.

The laser scanning interferometry system has been successfully applied to many measurement fields because of its efficient measurement ability. However, the practical application ability of this measurement method is restricted due to the laser tuning nonlinearity. In this paper, the fiber ring resonator is equidistant in the frequency domain, which is used as the external clock signal to resample the main interference signal so as to realize the equifrequency sampling of the laser scanning interference system and correct the tuning nonlinearity. The final experimental result shows that this method can effectively reduce the phase noise caused by tuning nonlinearity and improve the performance of the system.

Homodyne laser vibrometer modified by an LCVR for measurement at the nanometer level.

The existence of periodic nonlinear error restricts the performance of the homodyne laser vibrometer in sub-fringe amplitude vibration measurement. A homodyne laser vibrometer with nanoscale-amplitude detectability by using a liquid crystal variable retarder (LCVR) is proposed. The LCVR introduces an extra variation of optical path difference larger than the laser wavelength to acquire a full ellipse so that the nonlinearity correction parameters could be pre-extracted. The experiments showed that the nonlinear error could be well suppressed with the correction process based on the pre-extracted parameters, and the detectable minimum amplitude is less than 1 nm. In addition, measurement of vibration with the reflectivity of measured targets down to 0.048% was achieved with an automatic-gain-control module.

Generalized Cross-Correlation Strain Demodulation Method Based on Local Similar Spectral Scanning.

Optical fiber measurement technology is widely used in the strength testing of buildings, the health testing of industrial equipment, and the minimally invasive surgery of modern medical treatment due to its characteristics of free calibration, high precision, and small size. This paper presents an algorithm that can improve the range and stability of strain measurements in order to solve the problems of the small range and measurement failure of optical fiber strain sensors based on optical frequency-domain reflectometry (OFDR). Firstly, a Rayleigh scattering model based on the refractive index perturbation of an optical fiber is proposed to study the characteristics of Rayleigh scattering and to guide the strain demodulation algorithm based on the spectral shift. Secondly, a local similar scanning method that can maintain a high similarity by monitoring local Rayleigh scattering signals (LSs) before and after strain is proposed. Thirdly, a generalized cross-correlation algorithm is proposed to detect spectral offset, solving the problem of demodulation failure in the case of a Rayleigh scattering signal with a low signal-to-noise ratio. Experiments show that the proposed method still has high stability when the spatial resolution is 3 mm. The measurement precision is 6.2 µÎµ, which proves that the multi-peaks or pseudo-peaks of the traditional algorithm in the case of a large strain, the high spatial resolution, and the poor signal-to-noise ratio are solved, and the stability of the strain measurement process is improved.

Analysis of light coupling in AlxGa1-xAs waveguide arrays.

Light propagation in arrays of AlxGa1-xAs waveguides is studied. The power coupling constant between two adjacent waveguides is precisely measured as waveguide material and structure is varied. Aluminum concentration contrast between waveguide core/cladding layers and waveguide width/height produce an asymmetric effective refractive index between linearly polarized modes, which in turn causes a polarization dependence of the coupling constants. Experimental measurement results agree well with an analytical model. The sensitivity of coupling constant to the waveguide parameters is analyzed. Through a careful geometric design, comparable coupling constants can be achieved in three waveguide arrays with different structure. Similar formation processes of discrete spatial optical solitons are observed respectively, confirming that the parameterization in the discrete nonlinear Schrödinger equation characterizes waveguide arrays.

Optically transparent and microwave diffusion coding metasurface by utilizing ultrathin silver films.

The past few years have witnessed the great success of artificial metamaterials with effective medium parameters to control electromagnetic waves. Herein, we present a scheme to achieve broadband microwave low specular reflection with uniform backward scattering by using a coding metasurface, which is composed of a rational layout of subwavelength coding elements, via an optimization method. We propose coding elements with high transparency based on ultrathin doped silver, which are capable of generating large phase differences (∼180°) over a wide frequency range by designing geometric structures. The electromagnetic diffusion of the coding metasurface originates from the destructive interference of the reflected waves in various directions. Numerical simulations and experimental results demonstrate that low reflection is achieved from 12 to 18 GHz with a high angular insensitivity of up to ±40° for both transverse electric and transverse magnetic polarizations. Furthermore, the excellent visible transparency of the encoding metasurface is promising for various microwave and optical applications such as electronic surveillance, electromagnetic interference shielding, and radar cross-section reduction.

Deep learning enables confocal laser-scanning microscopy with enhanced resolution.

Theoretical resolution enhancement of confocal laser-scanning microscopy (CLSM) is sacrificed for the best compromise between optical sectioning and the signal-to-noise ratio (SNR). The pixel reassignment reconstruction algorithm can improve the effective spatial resolution of CLSM to its theoretical limit. However, current implementations are not versatile and are time-consuming or technically complex. Here we present a parameter-free post-processing strategy for laser-scanning microscopy based on deep learning, which enables a spatial resolution enhancement by a factor of ∼1.3, compared to conventional CLSM. To speed up the training process for experimental data, transfer learning, combined with a hybrid dataset consisting of simulated synthetic and experimental images, is employed. The overall resolution and SNR improvement, validated by quantitative evaluation metrics, allowed us to correctly infer the fine structures of real experimental images.

Parallel beam generation method for a high-precision roll angle measurement with a long working distance.

A novel measurement system for a high-precision roll angle measurement of long working distance on the basis of two parallel beams in association with two detectors is presented. The measurement system consists of a light source part and a detecting part. The light source part uses transmission grating and a plane mirror to produce a pair of high-precision parallel beams. The nonparallelism of the dual beam caused by the installation error can be compressed to ensure the measurement system achieves high-precision measurement and long working distance. The effectiveness of the measurement system and proposed methods are demonstrated by a series of experiments. The resolution of 0.5'' and measurement accuracy of 1.1'' can be obtained by the set-up measurement system.

Correction of refractive index mismatch-induced aberrations under radially polarized illumination by deep learning.

Radially polarized field under strong focusing has emerged as a powerful manner for fluorescence microscopy. However, the refractive index (RI) mismatch-induced aberrations seriously degrade imaging performance, especially under high numerical aperture (NA). Traditional adaptive optics (AO) method is limited by its tedious procedure. Here, we present a computational strategy that uses artificial neural networks to correct the aberrations induced by RI mismatch. There are no requirements for expensive hardware and complicated wavefront sensing in our framework when the deep network training is completed. The structural similarity index (SSIM) criteria and spatial frequency spectrum analysis demonstrate that our deep-learning-based method has a better performance compared to the widely used Richardson-Lucy (RL) deconvolution method at different imaging depth on simulation data. Additionally, the generalization of our trained network model is tested on new types of samples that are not present in the training procedure to further evaluate the utility of the network, and the performance is also superior to RL deconvolution.

Ptychography imaging by 1-D scanning with a diffuser.

It is beneficial to improve the resolution by a diffuser in imaging systems, because higher frequency information could be involved into the captured patterns via scattering effect. In this paper, a lensless imaging method is designed by 1-D scanning. A diffuser is placed upstream of the object, which is translated in a one-dimensional path and corresponding positions are corrected by cross-correlation. Our method requires a diffraction pattern of the object without a diffuser to speed up convergence and improve resolution. In field reconstruction, the amplitude constraint is added into the iterative phase retrieval algorithm. The high-quality complex-valued images can be obtained with ∼15 patterns. As a ptychography, the proposed method only needs a 1-D device, which could simplify the experimental equipment for reducing costs and measurement time.