Digital holographic phase unwrapping based on UMnet

CHEN Cuiru; WANG Huaying; ZHAO Baoqun; WANG Xue; ZHU Qiaofen; WANG Jieyu; WANG Wenjian; LEI Jialiang

doi:10.7510/jgjs.issn.1001-3806.2023.01.011

LASER TECHNOLOGY > 2023 > 47(1): 73-79. > DOI: 10.7510/jgjs.issn.1001-3806.2023.01.011

CHEN Cuiru, WANG Huaying, ZHAO Baoqun, WANG Xue, ZHU Qiaofen, WANG Jieyu, WANG Wenjian, LEI Jialiang. Digital holographic phase unwrapping based on UMnet[J]. LASER TECHNOLOGY, 2023, 47(1): 73-79. DOI: 10.7510/jgjs.issn.1001-3806.2023.01.011

Citation:

PDF (8451 KB)

Digital holographic phase unwrapping based on UMnet

1.
College of Mathematical Sciences and Engineering, Hebei University of Engineering, Handan 056038, China
2.
Hebei Pro-vince Computational Optical Imaging and Photoelectric Detection Technology Innovation Center, Handan 056038, China

More Information

Received Date: January 05, 2022
Revised Date: March 24, 2022
Published Date: January 24, 2023

Graphical Abstract

Abstract

Abstract

In order to realize high-precision digital holographic phase unwrapping, a lightweight deep learning network based on MobilenetV3 integrated in the Unet network framework adopted, and the UMnet network designed to realize accurate unwrapping of holographic phase. The network lightweight attention mechanism and multi-scale convolution to enhance the network accuracy and generalization ability. At the same time, the hard-Swish activation function improve the network learning ability. The simulated data set used for network training, and the noise reduction ability of the generated network model tested, which verified by the test of the hologram of the actual sample. The results show that the structural similarity index of UMnet is 6.6% higher than that of deep learning phase unwrapping network. UMnet can realize digital holographic phase unwrapping simply, quickly and efficiently.
- holography,
- phase unwrapping,
- attention mechanism,
- multi-scale convolution

FullText(HTML)

References (25)

References

[1]	DEPEURDINGE C D, CUCHE E, MARQUET P, et al. Digital holography applied to microscopy[J]. Proceedings of the SPIE, 2002, 4659: 30-34. DOI: 10.1117/12.469267
[2]	GOLDSTEIN R M, ZEBKER H A, WERNER C L. Satellite radar interferometry: Two dimensional phase unwrapping[J]. Radio Science, 2016, 23(4): 713-720.
[3]	AREYALILLO-HERRAEZ M, VILLATORO F R, GDEISAT M A. A robust and simple measure for quality guided 2D phase unwrapping algorithms[J]. IEEE Transactions on Image Processing, 2016, 25(6): 2601-2609. DOI: 10.1109/TIP.2016.2551370
[4]	张睿, 严利平, 张海燕, 等. 基于枝切线和可靠度的区域增长相位解包裹算法[J]. 浙江理工大学学报(自然科学版), 2019, 41(4): 474-481. DOI: 10.3969/j.issn.1673-3851(n).2019.04.010 ZHANG R, YAN L P, ZHANG H Y, et al. Region-growing phase unwrapping algorithm based on branch-cuts and reliability[J]. Journal of Zhejiang Sci-Tech University(Natural Sciences Edition), 2019, 41(4): 474-481 (in Chinese). DOI: 10.3969/j.issn.1673-3851(n).2019.04.010
[5]	WANG X, FANG S, ZHU X. Weighted least-squares phase unwrapping algorithm based on a non-interfering image of an object[J]. Applied Optics, 2017, 56(15): 4543-4550. DOI: 10.1364/AO.56.004543
[6]	PATIL P, VYASARAYANI C P, RAMJI M. Linear least squares approach for evaluating crack tip fracture parameters using isochromatic and isoclinic data from digital photoelasticity[J]. Optics and Lasers in Engineering, 2017, 93(1): 182-194.
[7]	马树军, 方锐. 数字全息显微中的相位像差自动补偿算法[J]. 东北大学学报(自然科学版), 2020, 41(11): 1591-1595. DOI: 10.12068/j.issn.1005-3026.2020.11.011 MA Sh J, FANG R. Automatic compensation algorithm of phase aberration in digital holographic microscopy[J]. Journal of Northeastern University(Natural Science Edition), 2020, 41(11): 1591-1595 (in Chinese). DOI: 10.12068/j.issn.1005-3026.2020.11.011
[8]	王华英, 刘佐强, 廖薇, 等. 基于最小范数的四种相位解包裹算法比较[J]. 中国激光, 2014, 41(2): 209016. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201402017.htm WANG H Y, LIU Z Q, LIAO W, et al. Compensation of four unwrapping algorithm based on method of minimum norm[J]. Chinese Journal of Lasers, 2014, 41(2): 209016 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201402017.htm
[9]	李盛林, 邓小芳, 王华英. 数字全息显微术中三种相位展开算法的比较研究[J]. 光学技术, 2020, 46(1): 56-60. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202001009.htm LI Sh L, DENG X F, WANG H Y. Comparison of three kinds of phase unwrapping algorithm in digital holographic microscopy[J]. Optical Technique, 2020, 46(1): 56-60 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202001009.htm
[10]	李芹, 刘维, 王道档, 等. 基于二阶质量权的四向剪切相位解包裹算法[J]. 光电子·激光, 2018, 29(6): 618-626. https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ201806008.htm LI Q, LIU W, WANG D D, et al. Four-direction least-square phase unwrapping algorithm based on shearing interferometry and second derivative of quality weight[J]. Journal of Optoelectronics · Lasers, 2018, 29(6): 618-626 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ201806008.htm
[11]	左超, 冯世杰, 张翔宇, 等. 深度学习下的计算成像: 现状、挑战与未来[J]. 光学学报, 2020, 40(1): 0111003. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB202001004.htm ZUO Ch, FENG Sh J, ZHANG X Y, et al. Deep learning based computational imaging: Status, challenges, and future[J]. Acta Optica Sinica, 2020, 40(1): 0111003 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB202001004.htm
[12]	邸江磊, 唐雎, 吴计, 等. 卷积神经网络在光学信息处理中的应用研究进展[J]. 激光与光电子学进展, 2021, 58(16): 1600001. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202116002.htm DI J L, TANG S, WU J, et al. Research progress in the applications of convolutional neural networks in optical information processing[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1600001 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202116002.htm
[13]	WANG K Q, LI Y, QIAO K, et al. One-step robust deep learning phase unwrapping[J]. Optics Express, 2019, 27(10): 15100-15115.
[14]	常江, 管声启, 师红宇, 等. 基于改进生成对抗网络和MobileNetV3的带钢缺陷分类[J]. 激光与光电子学进展, 2021, 58(4): 0410016. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202104026.htm CHANG J, GUAN Sh Q, SHI H Y, et al. Strip defect classification based on improved generative adversarial networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410016(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202104026.htm
[15]	郑少佳, 邱崧, 李庆利, 等. 傅里叶变换通道注意力网络的胆管癌高光谱图像分割[J]. 中国图象图形学报, 2021, 26(8): 1836-1846. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202108009.htm ZHENG Sh J, QIU S, LI Q L, et al. Fourier transform channel a-ttention network for cholangiocarcinoma hyperspectral image segmentation[J]. Journal of Image and Graphics, 2021, 26(8): 1836-1846 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202108009.htm
[16]	吴杰, 周皓, 吴丹, 等. 欠采样下相位解包裹算法的研究[J]. 激光与光电子学进展, 2016, 53(5): 051003. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201605016.htm WU J, ZHOU H, WU D, et al. Study of phase unwrapping algorithm from the undersampled phase[J]. Laser & Optoelectronics Progress, 2016, 53(5): 051003 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201605016.htm
[17]	PANDEY N, GHOSH A, KHARE K. Two-dimensional phase unwrapping using the transport of intensity equation[J]. Applied Optics, 2016, 55(9): 2418-2425.
[18]	CHENG H, DENG H L, SHEN C, et al. Phase retrieval based on transport of intensity equation and image interpolation[J]. Infrared and Laser Engineering, 2018, 47(10): 356-362.
[19]	陈妮, 左超, Byoungho LEE. 基于深度测量的三维成像技术[J]. 红外与激光工程, 2019, 48(6): 199-233. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201906014.htm CHEN N, ZUO Ch, LEE B. 3D imaging based on depth measurement[J]. Infrared and Laser Engineering, 2019, 48(6): 199-223 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201906014.htm
[20]	刘桂雄, 黄坚, 刘思洋, 等. 面向语义分割机器视觉的AutoML方法[J]. 激光杂志, 2019, 40(6): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ201906001.htm LIU G X, HUANG J, LIU S Y, et al. AutoML method for semantic segmentation of machine vision[J]. Laser Journal, 2019, 40(6): 1-9(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ201906001.htm
[21]	JIA W, XIA W, ZHAO Y, et al. 2D and 3D palmprint and palm vein recognition based on neural architecture search[J]. International Journal of Automation and Computing, 2021, 18(3): 377-409.
[22]	肖文, 李解, 潘锋, 等. 基于USENet实现数字全息细胞再现相位像超分辨重构[J]. 光子学报, 2020, 49(6): 0610001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202006020.htm XIAO W, LI J, PAN F, et al. Super-revolution in digital holographic phase cell image based on USENet[J]. Acta Photonica Sinica, 2020, 49(6): 0610001 (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202006020.htm
[23]	李宇豪, 吕晓琪, 谷宇, 等. 基于改进S3FD网络的人脸检测算法[J]. 激光技术, 2021, 45(6): 722-728. DOI: 10.7510/jgjs.issn.1001-3806.2021.06.008 LI Y H, LV X Q, GU Y, et al. Face detection algorithm based on improved S3FD network[J]. Laser Technology, 2021, 45(6): 722-728(in Chinese). DOI: 10.7510/jgjs.issn.1001-3806.2021.06.008
[24]	王鑫. 基于深度学习的语义分割算法研究[D]. 上海: 上海海洋大学, 2021: 30-42. WANG X. Research on semantic segmentation algorithm based on deep learning[D]. Shanghai: Shanghai Ocean University, 2021: 30-42(in Chinese).
[25]	吴育民, 宋国庆, 冯云鹏, 等. 数字全息显微在医学影像中的发展与最新应用[J]. 影像科学与光化学, 2016, 34(1): 23-29. https://www.cnki.com.cn/Article/CJFDTOTAL-GKGH201601004.htm WU Y M, SONG G Q, FENG Y P, et al. Development and latest application of digital holographic microscopy in medical imaging[J]. Imaging Science and Photochemistry, 2016, 34(1): 23-29(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GKGH201601004.htm

Cited By

Cited by

Periodical cited type(0)

Other cited types(4)

Get Citation

PDF

XML

Article views (569) PDF downloads (5) Cited by(4)

Turn off MathJax

Article Contents

Abstract

References

Digital holographic phase unwrapping based on UMnet

Abstract

References

Cited by

Periodical cited type(0)

Other cited types(4)

Catalog

Links：

Digital holographic phase unwrapping based on UMnet

Abstract

References

Cited by

Periodical cited type(0)

Other cited types(4)

Catalog

Links：

Export File

Citation

Format

Content