Literature DB >> 30160761

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

Wonseok Jeon, Wooyoung Jeong, Kyungchan Son, Hyunseok Yang.   

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.

Year:  2018        PMID: 30160761     DOI: 10.1364/OL.43.004240

Source DB:  PubMed          Journal:  Opt Lett        ISSN: 0146-9592            Impact factor:   3.776


  7 in total

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2.  Deep learning in holography and coherent imaging.

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Journal:  Light Sci Appl       Date:  2019-09-11       Impact factor: 17.782

3.  Deep learning-based optical field screening for robust optical diffraction tomography.

Authors:  DongHun Ryu; YoungJu Jo; Jihyeong Yoo; Taean Chang; Daewoong Ahn; Young Seo Kim; Geon Kim; Hyun-Seok Min; YongKeun Park
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Journal:  Sensors (Basel)       Date:  2020-08-31       Impact factor: 3.576

5.  Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.

Authors:  Andrey V Belashov; Anna A Zhikhoreva; Tatiana N Belyaeva; Anna V Salova; Elena S Kornilova; Irina V Semenova; Oleg S Vasyutinskii
Journal:  Cells       Date:  2021-09-29       Impact factor: 6.600

6.  Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection.

Authors:  Jinglei Wang; Yixuan Li; Yifan Ji; Jiaming Qian; Yuxuan Che; Chao Zuo; Qian Chen; Shijie Feng
Journal:  Sensors (Basel)       Date:  2022-08-27       Impact factor: 3.847

7.  Polychromatic digital holographic microscopy: a quasicoherent-noise-free imaging technique to explore the connectivity of living neuronal networks.

Authors:  Céline Larivière-Loiselle; Erik Bélanger; Pierre Marquet
Journal:  Neurophotonics       Date:  2020-10-16       Impact factor: 3.593

  7 in total

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