Deep learning in photonics: introduction

Gao, Li; Chai, Yang; Zibar, Darko; Yu, Zongfu

doi:10.1364/prj.428702

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…Machine learning is a statistics technology that trains a machine by telling it what to do, and has proven to be particularly good at solving the problems of classification and regression [22,23]. In the field of nanophotonics, machine learning has made great progress in many applications such as pattern recognition, optical imaging, and structure design [24][25][26][27]. Most recently, machine learning techniques have been utilized to inversely design the structure and material to achieve the desired optical and color properties [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Wang

et al. 2021

Nanomaterials

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Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles’ structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.

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Section: Introductionmentioning

confidence: 99%

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Wang

et al. 2021

Nanomaterials

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“…Is there a one-to-one mapping between the design space and the target space? If not, consider looking into different one-to-many solutions that are available [168]. This set of questions should assist you in finding the appropriate architecture for your needs.…”

Section: Other Emerging Fieldsmentioning

confidence: 99%

Deep learning in optics—a tutorial

Hadad,

Froim,

Yosef

et al. 2023

J. Opt.

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In recent years, machine learning and deep neural networks applications have experienced a remarkable surge in the field of physics, with optics being no exception. This tutorial aims to offer a fundamental introduction to the utilization of deep learning in optics, catering specifically to newcomers. Within this tutorial, we cover essential concepts, survey the field, and provide guidelines for the creation and deployment of artificial neural network architectures tailored to optical problems.

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“…Recently, deep learning techniques have wildly been used in various research fields. They have achieved outstanding results in fields such as photonics [7], biomedical engineering [8], material science [9], and computational photography [10]. To solve the problem of iris segmentation of noisy iris images, various deep learning-based segmentation methods [11][12][13] have been proposed and developed in recent years, and they have achieved better segmentation results compared with traditional methods.…”

Section: Introductionmentioning

confidence: 99%

RAG‐Net: ResNet‐50 attention gate network for accurate iris segmentation

Wei

Zeng

Zhang

et al. 2022

IET Image Processing

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Iris segmentation is an important step in the process of iris recognition. Iris images collected under non-cooperative conditions always contain various noise, which is a challenge for iris segmentation. Most U-Net-based methods have made great achievements in iris segmentation. However, this architecture lacks of focusing on target structures of varying shapes, and robustness in segmenting objects with significant shape variations. In this paper, we propose RAG-Net: an efficient iris segmentation method based on deep learning. In contrast to many previous convolutional neural network (CNN)-based iris segmentation methods, we adopted the attention gate (AG) mechanism and ResNet-50 in the U-Net architecture to improve iris segmentation accuracy, the AG module was included in the skip connection part of the RAG-Net architecture to further identify salient feature regions and prune feature responses, which preserve only the activations relevant to the required information, and the ResNet-50 module was used to improve the robustness of the segmentation performance. Using this model, efficient iris segmentation in a non-cooperative environment can be realized. The proposed method was trained and evaluated using the CASIA.v4-distance, CASIA.v4-thousand, UBIRIS.v2, and MICHE-I databases. From the view of the segmentation results, the proposed RAG-Net is one of effective architecture in iris segmentation methods.

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Deep learning in photonics: introduction

Cited by 12 publications

References 28 publications

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Deep learning in optics—a tutorial

RAG‐Net: ResNet‐50 attention gate network for accurate iris segmentation

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