Perspectives on oblique angle deposition of thin films: From fundamentals to devices

Abstract With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined. 

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Deep learning in optical metrology: a review

Global terrorist threats and illegal migration have intensified concerns for the security of citizens, and every effort is made to exploit all available technological advances to prevent adverse events and protect people and their property. Due to the ability to use at night and in weather conditions where RGB cameras do not perform well, thermal cameras have become an important component of sophisticated video surveillance systems. In this paper, we investigate the task of automatic person detection in thermal images using convolutional neural network models originally intended for detection in RGB images. We compare the performance of the standard state-of-the-art object detectors such as Faster R-CNN, SSD, Cascade R-CNN, and YOLOv3, that were retrained on a dataset of thermal images extracted from videos that simulate illegal movements around the border and in protected areas. Videos are recorded at night in clear weather, rain, and in the fog, at different ranges, and with different movement types. YOLOv3 was significantly faster than other detectors while achieving performance comparable with the best, so it was used in further experiments. We experimented with different training dataset settings in order to determine the minimum number of images needed to achieve good detection results on test datasets. We achieved excellent detection results with respect to average accuracy for all test scenarios although a modest set of thermal images was used for training. We test our trained model on different well known and widely used thermal imaging datasets as well. In addition, we present the results of the recognition of humans and animals in thermal images, which is particularly important in the case of sneaking around objects and illegal border crossings. Also, we present our original thermal dataset used for experimentation that contains surveillance videos recorded at different weather and shooting conditions.

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Thermal Object Detection in Difficult Weather Conditions Using YOLO

https://www.tandfonline.com/doi/pdf/10.1088/1468-6996/12/6/064709

Material design and structural color inspired by biomimetic approach

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To accurately and easily design the diffraction characteristics of a rectangular transmission grating under the illumination of Littrow mounting, the validity and limitation of the simplified modal method is evaluated by a comparison of diffraction efficiencies predicted by the modal approach to exact results calculated with rigorous coupled-wave analysis. The influence of the grating normalized period, the normalized groove depth, and the fill factor on the accuracy of the modal method is quantitatively determined. More importantly, the reflection effect of two propagating grating modes with the optical thin-film model and the nonsymmetrical Fabry-Perot model is proposed and applied in the modal method to improve the accuracy of the calculated diffraction efficiencies. Generally, it is found that the thin-film model of reflection loss is valid at the smaller normalized period, but the Fabry-Perot model can exactly calculate the reflection loss of grating modes at the larger normalized period. Based on the fact that the validity of the modal approach is determined independently of the incident wavelength, the exact design and analysis of grating diffraction elements can be implemented at different wavelengths by simply scaling the grating parameters. Moreover, the polarization effect of diffraction properties on the limitation of the modal method without and with the reflection loss of grating modes is clearly demonstrated.

Improvement of the validity of the simplified modal method for designing a subwavelength dielectric transmission grating

The validity and accuracy of the simplified modal method for a highly efficient transmission subwavelength triangular grating are fully and quantitatively evaluated by a comparison of diffraction efficiencies predicted from the modal method to exact results calculated by a rigorous coupled wave analysis. The larger errors are revealed in smaller periods and in lower groove depths. More importantly, with the consideration of the reflection loss of the two propagating modes, the accuracy of the simplified modal method is significantly enhanced. The calculated diffraction efficiencies are in good agreement with the results of the vector method. This enhanced simplified modal method can be effectively used in the design of a shallower subwavelength grating. It is important to note that the consideration of the modal reflection loss can be applicable to any dielectric diffraction structure, e.g., the rectangular grating, in which the accuracy of the simplified modal method could be excellently improved to more exactly design a grating.

Enhancement of the accuracy of the simplified modal method for designing a subwavelength triangular grooves grating

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed

https://www.mdpi.com/1424-8220/21/11/3703/pdf

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Synthetic aperture radar (SAR) images are inherently degraded by the speckle noise due to the coherent imaging, which may affect the performance of subsequent image analysis task. To address this problem, a weighted sparse representation-based method is proposed in this article for SAR image despeckling. The homomorphic transformation is first adopted to convert multiplicative noise into additive one. Second, similar patches are grouped together to learn the adaptive dictionaries and sparse coefficients based on nonlocal self-similarity constraint. Moreover, weighted regularizations are adopted for coefficients to boost the performance. Finally, despeckling images are obtained via exponential transformation. Experimental results on synthetic and real-world SAR images demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

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Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

A methodology for the fabrication of composite (Nb2O5)1−x(SiO2)x thin-film rugate filters by using pulsed direct current magnetron sputtering is presented. The two materials are mixed using rapidly alternating deposition technology. The optical properties of the composite films varying with the composition of the material are studied in detail. Refractive indices between 1.50 and 2.14 can be realized in our coating system. Two designed rugate filters with a reflection band at the wavelength of 532 nm are fabricated using an automatic deposition process. The microstructure of the rugate filter is investigated by using scanning electron microscopy. The calculated and the measured transmittance spectra are in good agreement with each other. The causes of the slight differences between them are also analysed.

Junchao Zhang

Papers

Improvement of the validity of the simplified modal method for designing a subwavelength dielectric transmission grating

Enhancement of the accuracy of the simplified modal method for designing a subwavelength triangular grooves grating

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Rugate filters prepared by rapidly alternating deposition