A 4F optical diffuser system with spatial light modulators for image data augmentation

2021 ◽  
Vol 488 ◽  
pp. 126859
Author(s):  
Baopeng Li ◽  
Okan K. Ersoy ◽  
Caiwen Ma ◽  
Zhibin Pan ◽  
Wansha Wen ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Image Data ◽  
Spatial Light Modulators ◽  
Light Modulators ◽  
Optical Diffuser

Giant Anisotropy of Conductivity in Hydrogenated Nanocrystalline Silicon Thin Films

1998 ◽  
Vol 536 ◽  
Author(s):  
A. B. Pevtsov ◽  
N. A. Feoktistov ◽  
V. G. Golubev
Keyword(s):  
Thin Films ◽  
Film Thickness ◽  
Percolation Theory ◽  
Nanocrystalline Silicon ◽  
Spatial Light Modulators ◽  
Light Emitting ◽  
Light Modulators ◽  
Silicon Thin Films ◽  
Longitudinal Conductivity ◽  
Transverse Conductivity

AbstractThin (<1000 Å) hydrogenated nanocrystalline silicon films are widely used in solar cells, light emitting diodes, and spatial light modulators. In this work the conductivity of doped and undoped amorphous-nanocrystalline silicon thin films is studied as a function of film thickness: a giant anisotropy of conductivity is established. The longitudinal conductivity decreases dramatically (by a factor of 109 − 1010) as the layer thickness is reduced from 1500 Å to 200 Å, while the transverse conductivity remains close to that of a doped a- Si:H. The data obtained are interpreted in terms of the percolation theory.

scholarly journals Ag-Yb Alloy-Novel Tunable Plasmonic Material

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 288
Author(s):  
Suetying Ching ◽  
Chakming Chan ◽  
Jack Ng ◽  
Kokwai Cheah
Keyword(s):  
Beam Steering ◽  
Volume Ratio ◽  
Response Strength ◽  
Transmission Efficiency ◽  
Spatial Light Modulators ◽  
Alloy Thin Film ◽  
Ellipsometric Measurement ◽  
Plasmonic Material ◽  
Light Modulators ◽  
Surface Plasmon Coupling

Metals are commonly used in plasmonic devices because of their strong plasmonic property. However, such properties are not easily tuned. For applications such as spatial light modulators and beam steering, tunable plasmonic properties are essential, and neither metals nor other plasmonic materials possess truly tunable plasmonic properties. In this work, we show that the silver alloy silver–ytterbium (Ag-Yb) possesses tunable plasmonic properties; its plasmonic response strength can be adjusted as a function of Yb concentration. Such tunability can be explained in terms of the influence of Yb on bound charge and interaction of its dielectric with the dielectric of Ag. The change in transition characteristics progressively weakens Ag’s plasmonic properties. With a spectral ellipsometric measurement, it was shown that the Ag-Yb alloy thin film retains the properties of Ag with high transmission efficiency. The weakened surface plasmon coupling strength without dramatic change in the coupling wavelengths implies that the tunability of the Ag-Yb alloy is related to its volume ratio. The principle mechanism of the plasmonic change is theoretically explained using a model. This work points to a potential new type of tunable plasmonic material.

scholarly journals Holographic Display System to Suppress Speckle Noise Based on Beam Shaping

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 204
Author(s):  
Di Wang ◽  
Yi-Wei Zheng ◽  
Nan-Nan Li ◽  
Qiong-Hua Wang
Keyword(s):  
Optical Element ◽  
Speckle Noise ◽  
Beam Shaping ◽  
Diffractive Optical Element ◽  
The Other ◽  
Spatial Light Modulators ◽  
Viewing Angle ◽  
Display System ◽  
Holographic Display ◽  
Light Modulators

In this paper, a holographic system to suppress the speckle noise is proposed. Two spatial light modulators (SLMs) are used in the system, one of which is used for beam shaping, and the other is used for reproducing the image. By calculating the effective viewing angle of the reconstructed image, the effective hologram and the effective region of the SLM are calculated accordingly. Then, the size of the diffractive optical element (DOE) is calculated accordingly. The dynamic DOEs and effective hologram are loaded on the effective regions of the two SLMs, respectively, while the wasted areas of the two SLMs are performed with zero-padded operations. When the laser passes through the first SLM, the light can be modulated by the effective DOEs. When the modulated beam illuminates the second SLM which is loaded with the effective hologram, the image is reconstructed with better quality and lower speckle noise. Moreover, the calculation time of the hologram is reduced. Experiments indicate the validity of the proposed system.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Diffraction Efficiency Characteristics for MEMS-Based Phase-Only Spatial Light Modulator with Nonlinear Phase Distribution

Photonics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 62
Author(s):  
Remington S. Ketchum ◽  
Pierre-Alexandre Blanche
Keyword(s):  
Diffraction Efficiency ◽  
High Speed ◽  
Phase Distribution ◽  
Complex Structure ◽  
Spatial Light Modulators ◽  
Light Modulator ◽  
Micro Electro Mechanical Systems ◽  
Light Modulators ◽  
Nonlinear Phase ◽  
Liquid Crystal On Silicon

Micro-electro mechanical systems (MEMS)-based phase-only spatial light modulators (PLMs) have the potential to overcome the limited speed of liquid crystal on silicon (LCoS) spatial light modulators (SLMs) and operate at speeds faster than 10 kHz. This expands the practicality of PLMs to several applications, including communications, sensing, and high-speed displays. The complex structure and fabrication requirements for large, 2D MEMS arrays with vertical actuation have kept MEMS-based PLMs out of the market in favor of LCoS SLMs. Recently, Texas Instruments has adapted its existing DMD technology for fabricating MEMS-based PLMs. Here, we characterize the diffraction efficiency for one of these PLMs and examine the effect of a nonlinear distribution of addressable phase states across a range of wavelengths and illumination angles.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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