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

Optics Letters
Wonseok JeonHyunseok 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.

References

Jan 31, 2006·Optics Letters·Florian CharrièreChristian Depeursinge
Jul 1, 2010·Optics Express·Melania PaturzoPietro Ferraro
Jan 1, 2012·Nature Photonics·Brandon ReddingHui Cao
Nov 18, 2014·Optics Express·Pasquale MemmoloPietro Ferraro
Nov 15, 2016·Optics Letters·Vittorio BiancoPietro Ferraro
Feb 7, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Kai ZhangLei Zhang
Sep 9, 2016·Light, Science & Applications·Vittorio BiancoPietro Ferraro

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Citations

Oct 28, 2019·Light, Science & Applications·Yair RivensonAydogan Ozcan
Oct 28, 2019·Scientific Reports·DongHun RyuYongKeun Park

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