General Inverse Design of Layered Thin-Film Materials with Convolutional Neural Networks

ACS Photonics ◽  
2021 ◽  
Author(s):  
Andrew Lininger ◽  
Michael Hinczewski ◽  
Giuseppe Strangi
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Inverse Design ◽  
Layered Thin Film

scholarly journals Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification

2019 ◽  
Vol 9 (02) ◽  
pp. 586-592 ◽  
Author(s):  
Shuai Liu ◽  
Charles N. Melton ◽  
Singanallur Venkatakrishnan ◽  
Ronald J. Pandolfi ◽  
Guillaume Freychet ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Grazing Incidence ◽  
Film Structure ◽  
Structure Identification ◽  
X Ray ◽  
Thin Film Structure ◽  
X Ray Scattering ◽  
Image Position

Abstract

Using Wave Propagation Simulations and Convolutional Neural Networks to Retrieve Thin Film Thickness from Hyperspectral Images

2021 ◽  
pp. 261-275
Author(s):  
Anna-Leena Erkkilä ◽  
Jukka Räbinä ◽  
Ilkka Pölönen ◽  
Timo Sajavaara ◽  
Esa Alakoski ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Wave Propagation ◽  
Film Thickness ◽  
Convolutional Neural Networks ◽  
Hyperspectral Images ◽  
Thin Film Thickness

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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