Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks

Wonseok Jeon; Wooyoung Jeong; Kyungchan Son; Hyunseok Yang

doi:10.1364/OL.43.004240

Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks

Wonseok Jeon, Wooyoung Jeong, Kyungchan Son, Hyunseok Yang

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

In this Letter, we propose a fast speckle noise reduction method with only a single reconstructed image based on convolutional neural networks. The proposed network has multi-sized kernels that can capture the speckle noise component effectively from digital holographic images. For robust noise reduction performance, the network is trained with a large noisy image dataset that has object-dependent noise and a wide range of noise levels. The experimental results show the fast, robust, and outstanding speckle noise reduction performance of the proposed approach.

Original language	English
Pages (from-to)	4240-4243
Number of pages	4
Journal	Optics Letters
Volume	43
Issue number	17
DOIs	https://doi.org/10.1364/OL.43.004240
Publication status	Published - 2018 Sep 1

Bibliographical note

Funding Information:
Ministry of Education (MOE) (NRF-2016R1D1A1B03930730). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education.

Funding Information:
Acknowledgment. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education.

Publisher Copyright:
© 2018 Optical Society of America.

All Science Journal Classification (ASJC) codes

Atomic and Molecular Physics, and Optics

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abstract = "In this Letter, we propose a fast speckle noise reduction method with only a single reconstructed image based on convolutional neural networks. The proposed network has multi-sized kernels that can capture the speckle noise component effectively from digital holographic images. For robust noise reduction performance, the network is trained with a large noisy image dataset that has object-dependent noise and a wide range of noise levels. The experimental results show the fast, robust, and outstanding speckle noise reduction performance of the proposed approach.",

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N1 - Funding Information: Ministry of Education (MOE) (NRF-2016R1D1A1B03930730). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education. Funding Information: Acknowledgment. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education. Publisher Copyright: © 2018 Optical Society of America.

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Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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