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End-to-end deep learning framework for digital holographic reconstruction

End-to-end deep learning framework for digital holographic reconstruction

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Abstract

Digital holography records the entire wavefront of an object, including amplitude and phase. To reconstruct the object numerically, we can backpropagate the hologram with Fresnel–Kirchhoff integral-based algorithms such as the angular spectrum method and the convolution method. Although effective, these techniques require prior knowledge, such as the object distance, the incident angle between the two beams, and the source wavelength. Undesirable zero-order and twin images have to be removed by an additional filtering operation, which is usually manual and consumes more time in off-axis configuration. In addition, for phase imaging, the phase aberration has to be compensated, and subsequently an unwrapping step is needed to recover the true object thickness. The former either requires additional hardware or strong assumptions, whereas the phase unwrapping algorithms are often sensitive to noise and distortion. Furthermore, for a multisectional object, an all-in-focus image and depth map are desired for many applications, but current approaches tend to be computationally demanding. We propose an end-to-end deep learning framework, called a holographic reconstruction network, to tackle these holographic reconstruction problems. Through this data-driven approach, we show that it is possible to reconstruct a noise-free image that does not require any prior knowledge and can handle phase imaging as well as depth map generation.

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DOI:10.1117/1.ap.1.1.016004

所属栏目:Research Articles

基金项目:The authors thank Nan Meng at the University of Hong Kong for fruitful discussions, Dr. Ping Su at the Graduate School at Shenzhen, Tsinghua University for providing some samples, and Yong Wu at University of Electronic Science and Technology of China for help in experiments. The authors gratefully acknowledge the following funding: (1) University of Hong Kong (104004582, 104005009); (2) Research Grants Council, University Grants Committee (RGC, UGC) (17203217). The authors declare that there are no conflicts of interest related to this article.

收稿日期:2018-06-06

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作者单位    点击查看

Zhenbo Ren:University of Hong Kong, Department of Electrical and Electronic Engineering, Pokfulam, Hong Kong, ChinaNorthwestern Polytechnical University, School of Natural and Applied Sciences, Xi’an, China
Zhimin Xu:SharpSight Limited, Hong Kong, China
Edmund Y. Lam:University of Hong Kong, Department of Electrical and Electronic Engineering, Pokfulam, Hong Kong, China

联系人作者:Edmund Y. Lam(elam@eee.hku.hk)

【1】D.Gabor, “A new microscopic principle,” Nature161(4098), 777–778 (1948).

【2】U.Schnarset al., Digital Holography and Wavefront Sensing, Springer, Heidelberg (2015).

【3】A. C.Chan, K. K.Tsia and E. Y.Lam, “Subsampled scanning holographic imaging (SuSHI) for fast, non-adaptive recording of three-dimensional objects,” Optica3(8), 911–917 (2016).

【4】F.Merolaet al., “Tomographic flow cytometry by digital holography,” Light Sci. Appl.6(4), e16241 (2017).

【5】Y.Wuet al., “Air quality monitoring using mobile microscopy and machine learning,” Light Sci. Appl.6(9), e17046 (2017).

【6】E.Cuche, P.Marquet and C.Depeursinge, “Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt.38(34), 6994–7001 (1999).0003-6935

【7】Y.Pourvaiset al., “Microstructural surface characterization of stainless and plain carbon steel using digital holographic microscopy,” J. Opt. Soc. Am. B34(5), B36–B41 (2017).0740-3224

【8】M.Born and E.Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, 7th ed., Cambridge University Press, Cambridge (1999).

【9】P.Picart and J.Leval, “General theoretical formulation of image formation in digital Fresnel holography,” J. Opt. Soc. Am. A25(7), 1744–1761 (2008).0740-3232

【10】J. W.Goodman, Introduction to Fourier Optics, 4th ed., W.H. Freeman, New York (2017).

【11】T. M.Kreis, M.Adams and W. P.Jüptner, “Methods of digital holography: a comparison,” Proc. SPIE3098, 224–233 (1997).0277-786X

【12】D. J.Bradyet al., “Compressive holography,” Opt. Express17(15), 13040–13049 (2009).1094-4087

【13】Z.Ren, N.Chen and E. Y.Lam, “Automatic focusing for multisectional objects in digital holography using the structure tensor,” Opt. Lett.42(9), 1720–1723 (2017).0146-9592

【14】H. A.Ilhan, M.Do?ar and M.?zcan, “Digital holographic microscopy and focusing methods based on image sharpness,” J. Microsc.255(3), 138–149 (2014).0022-2720

【15】I.Yamaguchi and T.Zhang, “Phase-shifting digital holography,” Opt. Lett.22(16), 1268–1270 (1997).0146-9592

【16】E.Cuche, P.Marquet and C.Depeursinge, “Spatial filtering for zero-order and twin-image elimination in digital off-axis holography,” Appl. Opt.39(23), 4070–4075 (2000).0003-6935

【17】T.Colombet al., “Total aberrations compensation in digital holographic microscopy with a reference conjugated hologram,” Opt. Express14(10), 4300–4306 (2006).1094-4087

【18】D. C.Ghiglia and L. A.Romero, “Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods,” J. Opt. Soc. Am. A11(1), 107–117 (1994).0740-3232

【19】R. M.Goldstein, H. A.Zebker and C. L.Werner, “Satellite radar interferometry: two-dimensional phase unwrapping,” Radio Sci.23(4), 713–720 (1988).

【20】M.Zhaoet al., “Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies,” Appl. Opt.50(33), 6214–6224 (2011).0003-6935

【21】D. C.Ghiglia and M. D.Pritt, Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software, Vol.?4, Wiley, New York (1998).

【22】D.Parshall and M. K.Kim, “Digital holographic microscopy with dual-wavelength phase unwrapping,” Appl. Opt.45(3), 451–459 (2006).0003-6935

【23】W.Quet al., “Digital holographic microscopy with physical phase compensation,” Opt. Lett.34(8), 1276–1278 (2009).0146-9592

【24】L.Miccioet al., “Direct full compensation of the aberrations in quantitative phase microscopy of thin objects by a single digital hologram,” Appl. Phys. Lett.90(4), 041104 (2007).0003-6951

【25】C.Zuoet al., “Phase aberration compensation in digital holographic microscopy based on principal component analysis,” Opt. Lett.38(10), 1724–1726 (2013).

【26】Z.Ren, N.Chen and E. Y.Lam, “Extended focused imaging and depth map reconstruction in optical scanning holography,” Appl. Opt.55(5), 1040–1047 (2016).0003-6935

【27】P.Ferraroet al., “Extended focused image in microscopy by digital holography,” Opt. Express13(18), 6738–6749 (2005).1094-4087

【28】X.Zhang, E. Y.Lam and T.-C.Poon, “Reconstruction of sectional images in holography using inverse imaging,” Opt. Express16(22), 17215–17226 (2008).1094-4087

【29】M. K.Kim, “Wavelength-scanning digital interference holography for optical section imaging,” Opt. Lett.24(23), 1693–1695 (1999).0146-9592

【30】Y.LeCun, Y.Bengio and G.Hinton, “Deep learning,” Nature521(7553), 436–444 (2015).

【31】D.Shen, G.Wu and H.-I.Suk, “Deep learning in medical image analysis,” Annu. Rev. Biomed. Eng.19(1), 221–248 (2017).1523-9829

【32】T.Nguyenet al., “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express25(13), 15043–15057 (2017).1094-4087

【33】Y.Rivensonet al., “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl.7, 17141 (2018).

【34】Y.Wuet al., “Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery,” Optica5(6), 704–710 (2018).

【35】Y.Zhanget al., “Edge sparsity criterion for robust holographic autofocusing,” Opt. Lett.42(19), 3824–3827 (2017).0146-9592

【36】Z.Ren, Z.Xu and E. Y.Lam, “Autofocusing in digital holography using deep learning,” Proc. SPIE10499, 104991V (2018).0277-786X

【37】Z.Ren, Z.Xu and E. Y.Lam, “Learning-based nonparametric autofocusing for digital holography,” Optica5(4), 337–344 (2018).

【38】G.Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Control Signals Syst.2(4), 303–314 (1989).0932-4194

【39】K.Heet al., “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. and Pattern Recogn., pp.?770–778 (2016).

【40】I.Goodfellow, Y.Bengio and A.Courville, Deep Learning, MIT Press, Cambridge, Massachusetts (2016).

【41】W.Shiet al., “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in Proc. IEEE Conf. Comput. Vis. and Pattern Recogn., pp.?1874–1883 (2016).

【42】Z.Wanget al., “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process.13(4), 600–612 (2004).1057-7149

【43】J.Bioucas-Diaset al., “Absolute phase estimation: adaptive local denoising and global unwrapping,” Appl. Opt.47(29), 5358–5369 (2008).

【44】A.Anandet al., “Automatic identification of malaria-infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J.4(5), 1456–1464 (2012).1041-1135

【45】M. A.Schulzeet al., “Semiconductor wafer defect detection using digital holography,” Proc. SPIE5041, 183–193 (2003).0277-786X

【46】M.Miret al., “Quantitative phase imaging,” Prog. Opt.57, 133–217 (2012).0079-6638

引用该论文

Zhenbo Ren,Zhimin Xu,Edmund Y. Lam. End-to-end deep learning framework for digital holographic reconstruction[J]. Advanced Photonics, 2019, 1(1): 016004

Zhenbo Ren,Zhimin Xu,Edmund Y. Lam. End-to-end deep learning framework for digital holographic reconstruction[J]. Advanced Photonics, 2019, 1(1): 016004

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