Abstract

This Letter aims to propose a dynamic-range compression and decompression scheme for digital holograms that uses a deep neural network (DNN). The proposed scheme uses simple thresholding to compress the dynamic range of holograms with 8-bit gradation to binary holograms. Although this can decrease the amount of data by one-eighth, the binarization strongly degrades the image quality of the reconstructed images. The proposed scheme uses a DNN to predict the original gradation holograms from the binary holograms, and the error-diffusion algorithm of the binarization process contributes significantly to training the DNN. The performance of the scheme exceeds that of modern compression techniques such as JPEG 2000 and high-efficiency video coding.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
High-speed computer-generated holography using an autoencoder-based deep neural network

Jiachen Wu, Kexuan Liu, Xiaomeng Sui, and Liangcai Cao
Opt. Lett. 46(12) 2908-2911 (2021)

Compressive imaging for defending deep neural networks from adversarial attacks

Vladislav Kravets, Bahram Javidi, and Adrian Stern
Opt. Lett. 46(8) 1951-1954 (2021)

Digital holographic particle volume reconstruction using a deep neural network

Tomoyoshi Shimobaba, Takayuki Takahashi, Yota Yamamoto, Yutaka Endo, Atsushi Shiraki, Takashi Nishitsuji, Naoto Hoshikawa, Takashi Kakue, and Tomoyosh Ito
Appl. Opt. 58(8) 1900-1906 (2019)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (7)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (1)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription