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 |
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Pages (from-to) | 4240-4243 |
Number of pages | 4 |
Journal | Optics Letters |
Volume | 43 |
Issue number | 17 |
DOIs | |
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.
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics