Satoshi Hashimoto, Yonghoon Ji, Kenichi Kudo et al.
This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology.
Tomoyoshi Shimobaba, Yutaka Endo, Takashi Nishitsuji et al.
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 (or reflected) by an object. Although CGI can obtain two- or three- dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.
Tomoyoshi Shimobaba, Yutaka Endo, Ryuji Hirayama et al.
We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and QR codes by using the proposed method.