On-axis deflectometric system for freeform surface measurement.

We propose an on-axis deflectometric system for the accurate measurement of freeform surfaces with large slope ranges. A miniature plane mirror is attached on the illumination screen to fold the optical path and achieve the on-axis deflectometric testing. Due to the existence of the miniature folding mirror, the deep-learning method is applied to recover the missing surface data in a single measurement. Low sensitivity to the calibration error of system geometry and high testing accuracy can be achieved with the proposed system. The feasibility and accuracy of the proposed system have been validated. The system is low in cost and simple in configuration, and it provides a feasible way for the flexible and general testing of freeform surfaces, with a significant potential of the application in on-machine testing.

Inter-observer agreement among specialists in the diagnosis of oral potentially malignant disorders and oral cancer using store-and-forward technology.

OBJECTIVES: Oral cancer is a leading cause of morbidity and mortality. Screening and mobile Health (mHealth)-based approach facilitates early detection remotely in a resource-limited settings. Recent advances in eHealth technology have enabled remote monitoring and triage to detect oral cancer in its early stages. Although studies have been conducted to evaluate the diagnostic efficacy of remote specialists, to our knowledge, no studies have been conducted to evaluate the consistency of remote specialists. The aim of this study was to evaluate interobserver agreement between specialists through telemedicine systems in real-world settings using store-and-forward technology. MATERIALS AND METHODS: The two remote specialists independently diagnosed clinical images (n=822) from image archives. The onsite specialist diagnosed the same participants using conventional visual examination, which was tabulated. The diagnostic accuracy of two remote specialists was compared with that of the onsite specialist. Images that were confirmed histopathologically were compared with the onsite diagnoses and the two remote specialists. RESULTS: There was moderate agreement (k= 0.682) between two remote specialists and (k= 0.629) between the onsite specialist and two remote specialists in the diagnosis of oral lesions. The sensitivity and specificity of remote specialist 1 were 92.7% and 83.3%, respectively, and those of remote specialist 2 were 95.8% and 60%, respectively, each compared with histopathology. CONCLUSION: The diagnostic accuracy of the two remote specialists was optimal, suggesting that "store and forward" technology and telehealth can be an effective tool for triage and monitoring of patients. CLINICAL RELEVANCE: Telemedicine is a good tool for triage and enables faster patient care in real-world settings.

pHSCNN: CNN-based hyperspectral recovery from a pair of RGB images.

To increase the fidelity of hyperspectral recovery from RGB images, we propose a pairwise-image-based hyperspectral convolutional neural network (pHSCNN) to recover hyperspectral images from a pair of RGB images, obtained by the same color sensor with and without an optical filter in front of the imaging lens. The proposed method avoids the pitfall of requiring multiple color sensors to obtain different RGB images and achieves higher accuracy than recovery from single RGB image. Besides, pHSCNN can also optimize the optical filter to further improve the performance. To experiment on real data, we built a dual-camera hyperspectral imaging system and created a real-captured hyperspectral-RGB dataset. Experimental results demonstrate the superiority of pHSCNN with the highest accuracy of the recovered hyperspectral signature perceptually and numerically.

Theoretical model and digital extraction of subsurface damage in ground fused silica.

Based on the fracture mechanics and grinding kinematics, a theoretical model is developed to determine various subsurface damage (SSD) parameters and roughness Rz of the ground brittle material with consideration of the material removal mode and spring back. Based on the image processing, a digital method is proposed to extract various SSD parameters from the cross-section micrograph of the ground sample. To verify the model and method, many fused silica samples are ground under different processing parameters, and their SSD depth and roughness Rz are measured. The research results show the average SSD depth (SSDa) can be expressed as SSDa = χ1Rz4/3 + χ2Rz (χ1 and χ2 are coefficients). The SSDa is closer to half of the maximum SSD depth (SSDm) as the wheel speed decreases or the grinding depth, feed speed, or abrasive diameter increases. The SSD length or density basically increases linearly with the increase of the SSDm. The digital method is reliable with a largest relative error of 6.65% in SSD depth, extraction speed of about 1.63s per micrograph, and good robustness to the micrograph size and small-scale residue interference. The research will contribute to the evaluation of SSDs and the optimization of the grinding process of fused silica.

Deep-learning-based deflectometry for freeform surface measurement.

We propose a deep-learning based deflectometric method for freeform surface measurement, in which a deep neural network is devised for freeform surface reconstruction. Full-scale skip connections are adopted in the network architecture to extract and incorporate multi-scale feature maps from different layers, enabling the accuracy and robustness of the testing system to be greatly enhanced. The feasibility of the proposed method is numerically and experimentally validated, and its excellent performance in terms of accuracy and robustness is also demonstrated. The proposed method provides a feasible way to achieve the general measurement of freeform surfaces while minimizing the measurement errors due to noise and system geometry calibration.

Short-wave infrared polarimetric image reconstruction using a deep convolutional neural network based on a high-frequency correlation.

Imaging in visible and short-wave infrared (SWIR) wavebands is essential in most remote sensing applications. However, compared to visible imaging cameras, SWIR cameras typically have lower spatial resolution, which limits the detailed information shown in SWIR images. We propose a method to reconstruct high-resolution polarization SWIR images with the help of color images using the deep learning method. The training dataset is constructed from color images, and the trained model is well suited for SWIR image reconstruction. The experimental results show the effectiveness of the proposed method in enhancing the quality of the polarized SWIR images with much better spatial resolution. Some buried spatial and polarized information may be recovered in the reconstructed SWIR images.

Design and error calibration of an on-axis deflectometric microscope system.

An on-axis deflectometric microscope system (ODMS) is proposed for the microscopic surface measurement with high accuracy and a large slope dynamic range. To reduce the geometry sensitivity, a beam splitter is employed to build the coaxial configuration among the illumination screen, camera, and tested sample, which facilitates the calibration of system geometrical parameters. Due to the small working distance, the system model miscalibration in the model-ray-tracing-based "null" testing could cause obvious geometrical aberrations. In this paper, the geometrical aberrations due to the system model miscalibration are analyzed, and the corresponding calibration method based on computer-aided reverse optimization is applied to achieve accurate measurement. In addition, the systematic error introduced by the system components in the ODMS are also discussed. Both the simulation and experiment have been carried out to demonstrate the feasibility and high accuracy of the proposed measurement method. The proposed system is compact in structure, large in measurable slope range, and high in spatial resolution, providing a viable metrological tool for the microscopic testing of various freeform surfaces, microstructural elements, and micro-devices.

Low-pass filtering based polarimetric dehazing method for dense haze removal.

Polarimetric dehazing method is very promising in enhancing the quality of images captured in the scattering media. However, it is found that the dehazing results calculated by hazy images are very sensitive to the noise, which may cause the method unstable or even invalid. To overcome this drawback and enhance the capability and stability of the polarimetric dehazing method, digital image processing algorithms or bias parameters need to be added into the method, however, they will make the algorithm complex and time consuming. In this paper, using low pass filter to suppress the noise of the hazy images, a novel polarimetric dehazing method is proposed to enhance the visibility of hazy images, especially for dense haze removal. Experimental results demonstrate that this method is totally automatic and very effective in dense haze processing. This method may have great potential usage in many applications, such as optical surveillance, underwater imaging, and bio-tissue imaging, etc.

Color polarization demosaicking by a convolutional neural network.

To address color polarization demosaicking problems in polarization imaging with a color polarization camera, we propose a color polarization demosaicking convolutional neural network (CPDCNN), which has a two-branch structure to ensure the fidelity of polarization signatures and enhance image resolution. To train the network, we built a unique dual-camera system and captured a pairwise color polarization image dataset. Experimental results show that CPDCNN outperformances other methods by a large margin in contrast and resolution.

MicroLED chromatic confocal microscope.

In this Letter, a microLED-based chromatic confocal microscope with a virtual confocal slit is proposed and demonstrated for three-dimensional (3D) profiling without any mechanical scanning or external light source. In the proposed method, a micro-scale light-emitting diode (microLED) panel works as a point source array to achieve lateral scanning. Axial scanning is realized through the chromatic aberration of an aspherical objective. A virtual pinhole technique is utilized to improve the contrast and precision of depth reconstruction. The system performance has been demonstrated with a diamond-turned copper sample and onion epidermis. The experimental results show that the microLED panel could be a potential solution for portable 3D confocal microscopy. Several considerations and prospects are proposed for future microLED requirements in confocal imaging.

Unsupervised learning for hyperspectral recovery based on a single RGB image.

Hyperspectral imagery often suffers from the degradation of spatial, spectral, or temporal resolution due to the limitations of hyperspectral imaging devices. To address this problem, hyperspectral recovery from a single red-green-blue (RGB) image has recently achieved significant progress via deep learning. However, current deep learning-based methods are all learned in a supervised way under the availability of RGB and correspondingly hyperspectral images, which is unrealistic for practical applications. Hence, we propose to recover hyperspectral images from a single RGB image in an unsupervised way. Moreover, based on the statistical property of hyperspectral images, a customized loss function is proposed to boost the performance. Extensive experiments on the BGU iCVL Hyperspectral Image Dataset demonstrate the effectiveness of the proposed method.

High-accuracy deflectometric microscope system with a large slope range.

We propose an off-axis deflectometric microscope system for microscopic surface testing with both high measurement accuracy and a large slope dynamic range. A high-luminance liquid crystal display directly illuminates the tested sample with coded fringes, and then the reflected fringes passing through a microscope objective are captured by a pinhole camera, from which the deflectometric microscopic testing with a large slope range can be achieved. The accuracy of the proposed system is validated numerically and experimentally, and a large measurable slope dynamic range is also demonstrated. The proposed system provides a feasible way with the slope range in the order of sub-radians and sag resolution better than 0.05 nm.

3D printing optics with hybrid material.

Three-dimensional (3D) printing technology has evolved tremendously in recent years, but due to stringent requirements on surface finish and limited material selection for optical performance purposes, 3D printing optics is still lagging behind. This paper reports on a quantitative study on the printing process of optical lenses using acrylic and cationic combined commercial hybrid material. By utilizing its unique curing property with digital light process technology, we demonstrate the concept of continuous printing in the top-down light projection setup. Also, an equal thickness and equal arc combo method has been proposed and evaluated to further help smooth the surface finish. Millimeter-level spherical lenses have been successfully fabricated, and their optical performance has also been discussed.

Calibration of geometrical aberration in transmitted wavefront testing of refractive optics with deflectometry.

Deflectometry, with its noticeable advantages such as simple structure, large dynamic range, and high accuracy comparable to interferometry, has been one of the powerful metrological techniques for optical surfaces in recent years. In the "null" deflectometric transmitted wavefront testing of refractive optics, ray tracing of the test system model is required, in which both the miscalibration of system geometrical parameters and optical tolerances on tested optics could introduce significant geometrical aberrations in the testing results. In this paper, the geometrical aberration introduced by a system modeling error in the transmitted wavefront testing is discussed. Besides, a calibration method based on polynomial optimization of geometrical aberration is presented for the geometrical aberration calibration. Both simulation and experiment have been performed to validate the feasibility of the proposed calibration method. The proposed method can calibrate the optical tolerances on tested optics effectively, and it is feasible even with a large geometric error, providing a viable way to address the uncertainty in system modeling in transmitted wavefront testing of freeform refractive optics with large dynamic range.

Random two-frame interferometry based on deep learning.

A two-frame phase-shifting interferometric wavefront reconstruction method based on deep learning is proposed. By learning from a large number of simulation data based on a physical model, the wrapped phase can be calculated accurately from two interferograms with an unknown phase step. The phase step can be any value excluding the integral multiples of π and the size of interferograms can be flexible. This method does not need a pre-filtering to subtract the direct-current term, but only needs a simple normalization. Comparing with other two-frame methods in both simulations and experiments, the proposed method can achieve better performance.

New testing and calculation method for determination viscoelasticity of optical glass.

Viscoelastic properties of glass within molding temperatures, such as shear relaxation modulus and bulk relaxation modulus, are key factors to build successful numerical model, predict forming process, and determine optimal process parameters for precision glass molding. However, traditional uniaxial compression creep tests with large strains are very limited in obtaining high-accuracy viscoelastic data of glass, due to the declining compressive stress caused by the increasing cross-sectional area of specimen in testing process. Besides, existing calculation method has limitation in transforming creep data to viscoelasticity data, especially when Poisson's ratio is unknown at molding temperature, which further induces a block to characterize viscoelastic parameter. This study proposes a systematic acquisition method for high-precision viscoelastic data, including creep testing, viscoelasticity calculation, and finite element verification. A minimal uniaxial creep testing (MUCT) method based on thermo-mechanical analysis (TMA) instrument is first built to obtain ideal and accurate creep data, by keeping compressive stress as a constant. A new calculation method on viscoelasticity determination is then proposed to derive shear relaxation modulus without the need of knowing bulk modulus or Poisson's ratio, which, compared with traditional method, extends the application range of viscoelasticity calculation. After determination, the obtained viscoelastic data are further incorporated into a numerical simulation model of MUCT to verify the accuracy of the determined viscoelasticity. Base on the great consistence between simulated and measured results (uniaxial creep displacement), the proposed systematic acquisition method can be used as a high accuracy viscoelasticity determination method.

Snapshot phase-shifting diffraction phase microscope.

We propose a novel and simple snapshot phase-shifting diffraction phase microscope with a polarization grating and spatial phase-shifting technology. Polarization grating separates the incident beam into left and right circular polarization beams, one of which is used as the reference beam after passing through a pinhole. Four phase-shifted interferograms can be captured simultaneously from the polarization camera to reconstruct the high spatial resolution phase map. The principle is presented in this Letter, and the performance of the proposed system is demonstrated experimentally. Due to the near-common-path configuration and snapshot feature, the proposed system provides a feasible way for real-time quantitative phase measurement with minimal sensitivity to vibration and thermal disturbance.

Deep-learning-based hyperspectral recovery from a single RGB image.

Commercial hyperspectral imaging devices are expensive and tend to suffer from the degradation of spatial, spectral, or temporal resolution. To address these problems, we propose a deep-learning-based method to recover hyperspectral images from a single RGB image. The proposed method learns an end-to-end mapping between an RGB image and corresponding hyperspectral images. Moreover, a customized loss function is proposed to boost the performance. Experimental results on a variety of hyperspectral datasets demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and perceptual quality.

PFNet: an unsupervised deep network for polarization image fusion.

Image fusion is the key step to improve the performance of object detection in polarization images. We propose an unsupervised deep network to address the polarization image fusion issue. The network learns end-to-end mapping for fused images from intensity and degree of linear polarization images, without the ground truth of fused images. Customized architecture and loss function are designed to boost performance. Experimental results show that our proposed network outperforms other state-of-the-art methods in terms of visual quality and quantitative measurement.

Precision glass molding of diffractive optical elements with high surface quality.

Diffractive optical surfaces have attractive properties for use in optical systems, like reducing weight and correcting for chromatic aberrations, but fabrication of high-quality glass diffractive optics is challenging, preventing it from being widely adopted in commercial applications. In this Letter, we report on a fabrication method to address molding challenges for high-surface-quality diffractive glass optics at molding temperatures up to 550°C, including selection of mold material, mold fabrication, precision glass molding, durability, and stability of the mold. To enable optimal mold machining and easy mold release, nickel phosphorous (NiP) is chosen as the plating material for its cutting performance and anti-adhesion properties, and copper-nickel C71500 (CuNi) is selected as the mold substrate because its coefficient of thermal expansion (CTE) is close to NiP. By the proposed method, diffractive glass optics with 2 nm Sa surface roughness is demonstrated.