Training digital hologram watermarking deep neural network considering hologram distributions

2021 ◽  
Author(s):  
Ju Won Lee ◽  
Jae Eun Lee ◽  
Young Ho Seo ◽  
Dong Wook Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Digital Hologram

scholarly journals Dynamic-range compression scheme for digital hologram using a deep neural network

2019 ◽  
Vol 44 (12) ◽  
pp. 3038 ◽  
Author(s):  
Tomoyoshi Shimobaba ◽  
David Blinder ◽  
Michal Makowski ◽  
Peter Schelkens ◽  
Yota Yamamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Dynamic Range ◽  
Compression Scheme ◽  
Digital Hologram ◽  
Dynamic Range Compression

scholarly journals Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4977
Author(s):  
Ji-Won Kang ◽  
Jae-Eun Lee ◽  
Jang-Hwan Choi ◽  
Woosuk Kim ◽  
Jin-Kyum Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Training Method ◽  
Digital Hologram ◽  
Network Training ◽  
Digital Holograms ◽  
Holographic Reconstruction

This paper proposes a method to embed and extract a watermark on a digital hologram using a deep neural network. The entire algorithm for watermarking digital holograms consists of three sub-networks. For the robustness of watermarking, an attack simulation is inserted inside the deep neural network. By including attack simulation and holographic reconstruction in the network, the deep neural network for watermarking can simultaneously train invisibility and robustness. We propose a network training method using hologram and reconstruction. After training the proposed network, we analyze the robustness of each attack and perform re-training according to this result to propose a method to improve the robustness. We quantitatively evaluate the results of robustness against various attacks and show the reliability of the proposed technique.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Underwater Fish Images Classification by Deep Neural Network

2020 ◽  
Author(s):  
Ala Supriya ◽  
Chiluka Venkat ◽  
Aliketti Deepak ◽  
GV Hari Prasad
Keyword(s):  
Neural Network ◽  
Deep Neural Network
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