164 citations · 170 across the 2 of their papers we have counts for
6 papers
Simple complex amplitude encoding of a phase-only hologram using binarized amplitude
Tomoyoshi Shimobaba, Takayuki Takahashi, Yota Yamamoto +4
For this work, we introduced the use of binary amplitude for our proposed complex amplitude encoding of a phase-only hologram. By principle, a complex amplitude in a hologram plane…
Digital holographic particle volume reconstruction using a deep neural network
Tomoyoshi Shimobaba, Takayuki Takahashi, Yota Yamamoto +6
This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network. Digital holographic volume reconstruction conventionally uses mu…
Computational ghost imaging using deep learning
Tomoyoshi Shimobaba, Yutaka Endo, Takashi Nishitsuji +8
Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (o…
Fast, large-scale hologram calculation in wavelet domain
Tomoyoshi Shimobaba, Kyoji Matsushima, Takayuki Takahashi +6
We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the…
Deep-learning-based data page classification for holographic memory
Tomoyoshi Shimobaba, Naoki Kuwata, Mizuha Homma +9
We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multi-layer p…
Autoencoder-based holographic image restoration
Tomoyoshi Shimobaba, Yutaka Endo, Ryuji Hirayama +8
We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by di…