Y
Yuki Nagahama
Researcher at Chiba University
Publications - 25
Citations - 393
Yuki Nagahama is an academic researcher from Chiba University. The author has contributed to research in topics: Holography & Image quality. The author has an hindex of 11, co-authored 23 publications receiving 308 citations. Previous affiliations of Yuki Nagahama include Japan Society for the Promotion of Science.
Papers
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Journal ArticleDOI
Computational ghost imaging using deep learning
Tomoyoshi Shimobaba,Yutaka Endo,Takashi Nishitsuji,Takayuki Takahashi,Yuki Nagahama,Satoki Hasegawa,Marie Sano,Ryuji Hirayama,Takashi Kakue,Atsushi Shiraki,Tomoyoshi Ito +10 more
TL;DR: A deep neural network is used to automatically learn the features of noise-contaminated CGI images and is able to predict low-noise images from new noise- Contamination CGI images.
Journal ArticleDOI
Random phase-free kinoform for large objects.
Tomoyoshi Shimobaba,Takashi Kakue,Yutaka Endo,Ryuji Hirayama,Daisuke Hiyama,Satoki Hasegawa,Yuki Nagahama,Marie Sano,Minoru Oikawa,Takashige Sugie,Tomoyoshi Ito +10 more
TL;DR: In this article, the authors proposed a random phase-free kinoform for large objects and used the random phase free method and error diffusion method to overcome the speckle noise.
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Convolutional neural network-based data page classification for holographic memory
Tomoyoshi Shimobaba,Naoki Kuwata,Mizuha Homma,Takayuki Takahashi,Yuki Nagahama,Marie Sano,Satoki Hasegawa,Ryuji Hirayama,Takashi Kakue,Atsushi Shiraki,Naoki Takada,Tomoyoshi Ito +11 more
TL;DR: This work numerically investigated the classification performance of a conventional multilayer perceptron (MLP) and a deep neural network under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted.
Posted Content
Random phase-free kinoform for large objects
Tomoyoshi Shimobaba,Takashi Kakue,Yutaka Endo,Ryuji Hirayama,Daisuke Hiyama,Satoki Hasegawa,Yuki Nagahama,Marie Sano,Takashige Sugie,Tomoyoshi Ito +9 more
TL;DR: In this article, the authors proposed a random phase-free kinoform for large objects and used the random phase free method and error diffusion method to overcome the speckle noise.
Journal ArticleDOI
Improvement of the image quality of random phase-free holography using an iterative method
Tomoyoshi Shimobaba,Takashi Kakue,Yutaka Endo,Ryuji Hirayama,Daisuke Hiyama,Satoki Hasegawa,Yuki Nagahama,Marie Sano,Minoru Oikawa,Takashige Sugie,Tomoyoshi Ito +10 more
TL;DR: In this article, an iterative random phase-free method with virtual convergence light was proposed to obtain large reconstructed images exceeding the size of the hologram, without the assistance of random phase.