Autoencoder-based holographic image restoration
Tomoyoshi Shimobaba, Yutaka Endo, Ryuji Hirayama, Yuki Nagahama,, Takayuki Takahashi, Takashi Nishitsuji, Takashi Kakue, Atsushi Shiraki, Naoki, Takada, Nobuyuki Masuda, Tomoyoshi Ito
TL;DR
This paper introduces an autoencoder-based neural network approach for restoring holographic images contaminated by noise, improving the clarity of images from holographic memory and QR code reconstructions.
Contribution
The paper presents a novel autoencoder method specifically designed for holographic image restoration, addressing noise issues in reconstructed images.
Findings
Effective noise reduction in holographic images
Improved image clarity in holographic memory applications
Successful restoration of QR code images
Abstract
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.
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