Denoising in spatial particle tomography on multi-layer holography reconstruction by deep learning

Jiaxing Li; Xiaoyan Wu; Ketao Yan; Yingjie Yu

doi:10.1117/12.2541651

16 October 2019 Denoising in spatial particle tomography on multi-layer holography reconstruction by deep learning

Jiaxing Li, Xiaoyan Wu, Ketao Yan, Yingjie Yu

Proceedings Volume 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019); 112051B (2019) https://doi.org/10.1117/12.2541651
Event: Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 2019, Phuket, Thailand

Abstract

Spatial particle distribution can be recorded by holography technology and can be constructed from multi-layer hologram. Due to the influence of holographic recording and reconstruction process, each tomography of multi-layer reconstruction from holography also contains noise in addition to containing spatial particle distribution information. How to denoise each tomography is a key problem. The existing methods either have a long operation time or the noise reduction effect is not obvious. In order to solve the above problems, we proposed a denoising method based on deep learning in this paper. A deep neural network is built to train and test with simulated spatial particle tomography on multi-layer holography reconstruction. According to the simulation results, the method proposed in this paper is effective in denoising the reconstruction results of spatial particles. The proposed method has the advantages of rapidity and high efficiency.

Citation Download Citation

Jiaxing Li, Xiaoyan Wu, Ketao Yan, and Yingjie Yu "Denoising in spatial particle tomography on multi-layer holography reconstruction by deep learning", Proc. SPIE 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 112051B (16 October 2019); https://doi.org/10.1117/12.2541651

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KEYWORDS

Holography

Denoising

Particles

Neural networks

Convolution

3D image reconstruction

Tomography

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