Abstract

Wide-field microscopy (WFM) is broadly used in experimental studies of biological specimens. However, combining the out-of-focus signals with the in-focus plane reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore, structured illumination microscopy (SIM) with white light illumination has been used to obtain full-color 3D images, which can capture high SNR optically-sectioned images with improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FC-SIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-of-field, especially for thick samples such as insects and large-scale 3D imaging using stitching techniques. In this paper, we propose a deep-learning-based method for full-color WFM, i.e., FC-WFM-Deep, which can reconstruct high-quality full-color 3D images with an extended optical sectioning capability directly from the FC-WFM z-stack data. Case studies of different specimens with a specific imaging system are used to illustrate this method. Consequently, the image quality achievable with this FC-WFM-Deep method is comparable to the FC-SIM method in terms of 3D information and spatial resolution, while the reconstruction data size is 21-fold smaller and the in-focus depth is doubled. This technique significantly reduces the 3D data acquisition requirements without losing detail and improves the 3D imaging speed by extracting the optical sectioning in the depth-of-field. This cost-effective and convenient method offers a promising tool to observe high-precision color 3D spatial distributions of biological samples.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (6)

S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
[Crossref]

J. Qian, S. Dang, Z. Wang, X. Zhou, D. Dan, B. Yao, Y. Tong, H. Yang, Y. Lu, Y. Chen, X. Yang, M. Bai, and M. Lei, “Large-scale 3D imaging of insects with natural color,” Opt. Express 27(4), 4845–4857 (2019).
[Crossref]

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[Crossref]

H. Pinkard, Z. Phillips, A. Babakhani, D. A. Fletcher, and L. Waller, “Deep learning for single-shot autofocus microscopy,” Optica 6(6), 794–797 (2019).
[Crossref]

Z. Xie, Y. Tang, X. Liu, J. Liu, Y. He, and S. Hu, “Profilometry with enhanced accuracy using differential structured illumination microscopy,” IEEE Photonics Technol. Lett. 31(13), 1017–1020 (2019).
[Crossref]

U. Sara, M. Akter, and M. S. Uddin, “Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study,” J. Comput. Commun. 07(03), 8–18 (2019).
[Crossref]

2018 (2)

2017 (2)

Z. Wang, Y. Cai, Y. Liang, X. Zhou, S. Yan, D. Dan, P. R. Bianco, M. Lei, and B. Yao, “Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function,” Opt. Express 8(12), 5493–5506 (2017).
[Crossref]

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising,” IEEE Trans. on Image Process. 26(7), 3142–3155 (2017).
[Crossref]

2015 (1)

J. Qian, M. Lei, D. Dan, B. Yao, X. Zhou, Y. Yang, S. Yan, J. Min, and X. Yu, “Full-color structured illumination optical sectioning microscopy,” Sci. Rep. 5(1), 14531 (2015).
[Crossref]

2014 (1)

D. Dan, B. Yao, and M. Lei, “Structured illumination microscopy for super-resolution and optical sectioning,” Chin. Sci. Bull. 59(12), 1291–1307 (2014).
[Crossref]

2013 (1)

D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
[Crossref]

2011 (2)

E. Shevtsova, C. Hansson, D. H. Janzen, and J. Kjærandsen, “Stable structural color patterns displayed on transparent insect wings,” Proc. Natl. Acad. Sci. U. S. A. 108(2), 668–673 (2011).
[Crossref]

M. B. A. Haghighat, A. Aghagolzadeh, and H. Seyedarabi, “Multi-focus image fusion for visual sensor networks in DCT domain,” Comput. Electr. Eng. 37(5), 789–797 (2011).
[Crossref]

2009 (1)

2006 (1)

P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Process. Mag. 23(3), 32–45 (2006).
[Crossref]

2005 (1)

O. V. Michailovich and D. Adam, “A novel approach to the 2-D blind deconvolution problem in medical ultrasound,” IEEE Trans. Med. Imaging 24(1), 86–104 (2005).
[Crossref]

2004 (2)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

B. Forster, D. V. D. Ville, J. Berent, D. Sage, and M. Unser, “Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images,” Microsc. Res. Tech. 65(1-2), 33–42 (2004).
[Crossref]

2001 (1)

H. D. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34(12), 2259–2281 (2001).
[Crossref]

2000 (1)

M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. 198(2), 82–87 (2000).
[Crossref]

1998 (1)

M. A. A. Neil, T. Wilson, and R. Juškaitis, “A light efficient optically sectioning microscope,” J. Microsc. 189(2), 114–117 (1998).
[Crossref]

1994 (1)

T. Wilson, R. Juškaitis, and J. B. Tan, “Differential imaging in confocal microscopy,” J. Microsc. 175(1), 1–9 (1994).
[Crossref]

Adam, D.

O. V. Michailovich and D. Adam, “A novel approach to the 2-D blind deconvolution problem in medical ultrasound,” IEEE Trans. Med. Imaging 24(1), 86–104 (2005).
[Crossref]

Aghagolzadeh, A.

M. B. A. Haghighat, A. Aghagolzadeh, and H. Seyedarabi, “Multi-focus image fusion for visual sensor networks in DCT domain,” Comput. Electr. Eng. 37(5), 789–797 (2011).
[Crossref]

Akter, M.

U. Sara, M. Akter, and M. S. Uddin, “Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study,” J. Comput. Commun. 07(03), 8–18 (2019).
[Crossref]

Ananth, M.

S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
[Crossref]

Babakhani, A.

Bai, M.

Ben-David, E.

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
[Crossref]

Bentolila, L. A.

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
[Crossref]

Berent, J.

B. Forster, D. V. D. Ville, J. Berent, D. Sage, and M. Unser, “Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images,” Microsc. Res. Tech. 65(1-2), 33–42 (2004).
[Crossref]

Bianco, P. R.

Z. Wang, Y. Cai, Y. Liang, X. Zhou, S. Yan, D. Dan, P. R. Bianco, M. Lei, and B. Yao, “Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function,” Opt. Express 8(12), 5493–5506 (2017).
[Crossref]

Boorboor, S.

S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
[Crossref]

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

Cai, Y.

Z. Wang, Y. Cai, Y. Liang, X. Zhou, S. Yan, D. Dan, P. R. Bianco, M. Lei, and B. Yao, “Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function,” Opt. Express 8(12), 5493–5506 (2017).
[Crossref]

Carrington, W. A.

W. A. Carrington, K. E. Fogarty, L. Lifschitz, and F. S. Fay, “Three-dimensional Imaging on Confocal and Wide-field Microscopes,” In Handbook of Biological Confocal Microscopy, 151–161. Springer (1990).

Chen, Y.

Cheng, H. D.

H. D. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34(12), 2259–2281 (2001).
[Crossref]

Dan, D.

J. Qian, S. Dang, Z. Wang, X. Zhou, D. Dan, B. Yao, Y. Tong, H. Yang, Y. Lu, Y. Chen, X. Yang, M. Bai, and M. Lei, “Large-scale 3D imaging of insects with natural color,” Opt. Express 27(4), 4845–4857 (2019).
[Crossref]

Z. Wang, Y. Cai, Y. Liang, X. Zhou, S. Yan, D. Dan, P. R. Bianco, M. Lei, and B. Yao, “Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function,” Opt. Express 8(12), 5493–5506 (2017).
[Crossref]

J. Qian, M. Lei, D. Dan, B. Yao, X. Zhou, Y. Yang, S. Yan, J. Min, and X. Yu, “Full-color structured illumination optical sectioning microscopy,” Sci. Rep. 5(1), 14531 (2015).
[Crossref]

D. Dan, B. Yao, and M. Lei, “Structured illumination microscopy for super-resolution and optical sectioning,” Chin. Sci. Bull. 59(12), 1291–1307 (2014).
[Crossref]

D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
[Crossref]

Dang, S.

Davidson, M. W.

K. R. Spring and M. W. Davidson, “Depth of field and depth of focus,” MicroscopyU of Nikon, https://www.microscopyu.com/microscopy-basics/depth-of-field-and-depth-of-focus .

Dong, B. Q.

Fay, F. S.

W. A. Carrington, K. E. Fogarty, L. Lifschitz, and F. S. Fay, “Three-dimensional Imaging on Confocal and Wide-field Microscopes,” In Handbook of Biological Confocal Microscopy, 151–161. Springer (1990).

Fletcher, D. A.

Fogarty, K. E.

W. A. Carrington, K. E. Fogarty, L. Lifschitz, and F. S. Fay, “Three-dimensional Imaging on Confocal and Wide-field Microscopes,” In Handbook of Biological Confocal Microscopy, 151–161. Springer (1990).

Forster, B.

B. Forster, D. V. D. Ville, J. Berent, D. Sage, and M. Unser, “Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images,” Microsc. Res. Tech. 65(1-2), 33–42 (2004).
[Crossref]

Gao, P.

D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
[Crossref]

Gong, H.

Günaydin, H.

Gustafsson, M. G. L.

M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. 198(2), 82–87 (2000).
[Crossref]

Haghighat, M. B. A.

M. B. A. Haghighat, A. Aghagolzadeh, and H. Seyedarabi, “Multi-focus image fusion for visual sensor networks in DCT domain,” Comput. Electr. Eng. 37(5), 789–797 (2011).
[Crossref]

Han, Y.

Hansson, C.

E. Shevtsova, C. Hansson, D. H. Janzen, and J. Kjærandsen, “Stable structural color patterns displayed on transparent insect wings,” Proc. Natl. Acad. Sci. U. S. A. 108(2), 668–673 (2011).
[Crossref]

He, K.

K. He, X. Zhang, S. Ren, J. Sun, and M. Research, “Deep residual learning for image recognition,” Conf. Comput. Vis. Pattern Recognit. (CVPR), (IEEE, 2016).

He, Y.

Z. Xie, Y. Tang, X. Liu, J. Liu, Y. He, and S. Hu, “Profilometry with enhanced accuracy using differential structured illumination microscopy,” IEEE Photonics Technol. Lett. 31(13), 1017–1020 (2019).
[Crossref]

Hu, S.

Z. Xie, Y. Tang, X. Liu, J. Liu, Y. He, and S. Hu, “Profilometry with enhanced accuracy using differential structured illumination microscopy,” IEEE Photonics Technol. Lett. 31(13), 1017–1020 (2019).
[Crossref]

Jadhav,

S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
[Crossref]

Janzen, D. H.

E. Shevtsova, C. Hansson, D. H. Janzen, and J. Kjærandsen, “Stable structural color patterns displayed on transparent insect wings,” Proc. Natl. Acad. Sci. U. S. A. 108(2), 668–673 (2011).
[Crossref]

Jiang, X.

H. D. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34(12), 2259–2281 (2001).
[Crossref]

Juškaitis, R.

M. A. A. Neil, T. Wilson, and R. Juškaitis, “A light efficient optically sectioning microscope,” J. Microsc. 189(2), 114–117 (1998).
[Crossref]

T. Wilson, R. Juškaitis, and J. B. Tan, “Differential imaging in confocal microscopy,” J. Microsc. 175(1), 1–9 (1994).
[Crossref]

Kaufman, A.

S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
[Crossref]

Kim, J.

J. Kim, J. Lee, and K. Lee, “Accurate image super-resolution using very deep convolutional networks,” Conf. Comput. Vis. Pattern Recognit. (CVPR), (IEEE, 2016).

Kjærandsen, J.

E. Shevtsova, C. Hansson, D. H. Janzen, and J. Kjærandsen, “Stable structural color patterns displayed on transparent insect wings,” Proc. Natl. Acad. Sci. U. S. A. 108(2), 668–673 (2011).
[Crossref]

Lam, E. Y. M.

Z. Ren, Z. Ren, and E. Y. M. Lam, “Autofocusing in digital holography using deep learning,” Three-dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV (SPIE, 2018).

Lee, J.

J. Kim, J. Lee, and K. Lee, “Accurate image super-resolution using very deep convolutional networks,” Conf. Comput. Vis. Pattern Recognit. (CVPR), (IEEE, 2016).

Lee, K.

J. Kim, J. Lee, and K. Lee, “Accurate image super-resolution using very deep convolutional networks,” Conf. Comput. Vis. Pattern Recognit. (CVPR), (IEEE, 2016).

Lei, M.

J. Qian, S. Dang, Z. Wang, X. Zhou, D. Dan, B. Yao, Y. Tong, H. Yang, Y. Lu, Y. Chen, X. Yang, M. Bai, and M. Lei, “Large-scale 3D imaging of insects with natural color,” Opt. Express 27(4), 4845–4857 (2019).
[Crossref]

Z. Wang, Y. Cai, Y. Liang, X. Zhou, S. Yan, D. Dan, P. R. Bianco, M. Lei, and B. Yao, “Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function,” Opt. Express 8(12), 5493–5506 (2017).
[Crossref]

J. Qian, M. Lei, D. Dan, B. Yao, X. Zhou, Y. Yang, S. Yan, J. Min, and X. Yu, “Full-color structured illumination optical sectioning microscopy,” Sci. Rep. 5(1), 14531 (2015).
[Crossref]

D. Dan, B. Yao, and M. Lei, “Structured illumination microscopy for super-resolution and optical sectioning,” Chin. Sci. Bull. 59(12), 1291–1307 (2014).
[Crossref]

D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
[Crossref]

Liang, Y.

Z. Wang, Y. Cai, Y. Liang, X. Zhou, S. Yan, D. Dan, P. R. Bianco, M. Lei, and B. Yao, “Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function,” Opt. Express 8(12), 5493–5506 (2017).
[Crossref]

Lifschitz, L.

W. A. Carrington, K. E. Fogarty, L. Lifschitz, and F. S. Fay, “Three-dimensional Imaging on Confocal and Wide-field Microscopes,” In Handbook of Biological Confocal Microscopy, 151–161. Springer (1990).

Lin, X.

Liu, F.

Liu, J.

Z. Xie, Y. Tang, X. Liu, J. Liu, Y. He, and S. Hu, “Profilometry with enhanced accuracy using differential structured illumination microscopy,” IEEE Photonics Technol. Lett. 31(13), 1017–1020 (2019).
[Crossref]

Liu, X.

Z. Xie, Y. Tang, X. Liu, J. Liu, Y. He, and S. Hu, “Profilometry with enhanced accuracy using differential structured illumination microscopy,” IEEE Photonics Technol. Lett. 31(13), 1017–1020 (2019).
[Crossref]

Liu, X. H.

Lu, Y.

Luo, Y.

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
[Crossref]

Meng, D.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising,” IEEE Trans. on Image Process. 26(7), 3142–3155 (2017).
[Crossref]

Michailovich, O. V.

O. V. Michailovich and D. Adam, “A novel approach to the 2-D blind deconvolution problem in medical ultrasound,” IEEE Trans. Med. Imaging 24(1), 86–104 (2005).
[Crossref]

Min, J.

J. Qian, M. Lei, D. Dan, B. Yao, X. Zhou, Y. Yang, S. Yan, J. Min, and X. Yu, “Full-color structured illumination optical sectioning microscopy,” Sci. Rep. 5(1), 14531 (2015).
[Crossref]

Nehorai, A.

P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Process. Mag. 23(3), 32–45 (2006).
[Crossref]

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M. A. A. Neil, T. Wilson, and R. Juškaitis, “A light efficient optically sectioning microscope,” J. Microsc. 189(2), 114–117 (1998).
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Ning, K.

Ozcan, A.

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
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Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Günaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery,” Optica 5(6), 704–710 (2018).
[Crossref]

Phillips, Z.

Pinkard, H.

Pritz, C.

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
[Crossref]

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D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
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J. Qian, S. Dang, Z. Wang, X. Zhou, D. Dan, B. Yao, Y. Tong, H. Yang, Y. Lu, Y. Chen, X. Yang, M. Bai, and M. Lei, “Large-scale 3D imaging of insects with natural color,” Opt. Express 27(4), 4845–4857 (2019).
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Z. Ren, Z. Ren, and E. Y. M. Lam, “Autofocusing in digital holography using deep learning,” Three-dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV (SPIE, 2018).

Z. Ren, Z. Ren, and E. Y. M. Lam, “Autofocusing in digital holography using deep learning,” Three-dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV (SPIE, 2018).

Research, M.

K. He, X. Zhang, S. Ren, J. Sun, and M. Research, “Deep residual learning for image recognition,” Conf. Comput. Vis. Pattern Recognit. (CVPR), (IEEE, 2016).

Rivenson, Y.

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
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E. Shevtsova, C. Hansson, D. H. Janzen, and J. Kjærandsen, “Stable structural color patterns displayed on transparent insect wings,” Proc. Natl. Acad. Sci. U. S. A. 108(2), 668–673 (2011).
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K. He, X. Zhang, S. Ren, J. Sun, and M. Research, “Deep residual learning for image recognition,” Conf. Comput. Vis. Pattern Recognit. (CVPR), (IEEE, 2016).

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H. D. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34(12), 2259–2281 (2001).
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S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
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D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
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Yang, X.

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J. Qian, M. Lei, D. Dan, B. Yao, X. Zhou, Y. Yang, S. Yan, J. Min, and X. Yu, “Full-color structured illumination optical sectioning microscopy,” Sci. Rep. 5(1), 14531 (2015).
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D. Dan, M. Lei, B. Yao, W. Wang, M. Winterhalder, A. Zumbusch, Y. Qi, L. Xia, S. Yan, Y. Yang, P. Gao, T. Ye, and W. Zhao, “DMD-based LED-illumination super-resolution and optical sectioning microscopy,” Sci. Rep. 3(1), 1116 (2013).
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Z. Xie, Y. Tang, X. Liu, J. Liu, Y. He, and S. Hu, “Profilometry with enhanced accuracy using differential structured illumination microscopy,” IEEE Photonics Technol. Lett. 31(13), 1017–1020 (2019).
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K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising,” IEEE Trans. on Image Process. 26(7), 3142–3155 (2017).
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IEEE Trans. Visual. Comput. Graphics (1)

S. Boorboor, Jadhav, M. Ananth, D. Talmage, Role, and A. Kaufman, “Visualization of neuronal structures in wide-field microscopy brain images,” IEEE Trans. Visual. Comput. Graphics 25(1), 1018–1028 (2019).
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J. Comput. Commun. (1)

U. Sara, M. Akter, and M. S. Uddin, “Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study,” J. Comput. Commun. 07(03), 8–18 (2019).
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Microsc. Res. Tech. (1)

B. Forster, D. V. D. Ville, J. Berent, D. Sage, and M. Unser, “Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images,” Microsc. Res. Tech. 65(1-2), 33–42 (2004).
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Nat. Methods (1)

Y. Wu, Y. Rivenson, H. Wang, Y. Luo, E. Ben-David, L. A. Bentolila, C. Pritz, and A. Ozcan, “Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning,” Nat. Methods 16(12), 1323–1331 (2019).
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Opt. Express (4)

Optica (2)

Pattern Recogn. (1)

H. D. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34(12), 2259–2281 (2001).
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Proc. Natl. Acad. Sci. U. S. A. (1)

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J. Qian, M. Lei, D. Dan, B. Yao, X. Zhou, Y. Yang, S. Yan, J. Min, and X. Yu, “Full-color structured illumination optical sectioning microscopy,” Sci. Rep. 5(1), 14531 (2015).
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Supplementary Material (2)

NameDescription
» Visualization 1       Visualization 1 presents the “3D color images” after 3D reconstruction of the compound eye.
» Visualization 2       Visualization 2 presents the “3D color images” after 3D reconstruction of the shining leaf chafer.

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Figures (9)

Fig. 1.
Fig. 1. (a) Light path diagram for the FC-WFM and FC-SIM microscope. (b) Intensity distribution with respect to the defocused range, which measures the averaged OS strength in the FC-SIM and the WFM intensity.
Fig. 2.
Fig. 2. Schematics of data acquisition with corresponding in-focus depth and scanning strategies for different optical imaging modes: (a) FC-WFM imaging, (b) FC-SIM imaging and (c) the proposed FC-WFM-Deep imaging, respectively. It should be noted that the FC-WFM images and the FC-SIM images of the sample were obtained from the same raw data to conveniently provide the theoretical analysis and equal comparison.
Fig. 3.
Fig. 3. Schematics of the FC-WFM-Deep imaging system. (a) Training the network using a WF image and a corresponding composite OS reference image, and (b) reconstructing the high-quality output image consisting of the OS in DOF using the trained network. The 3D imaging result can then be acquired with a reduced data acquisition requirement.
Fig. 4.
Fig. 4. Imaging of a compound eye at different depths of (a) 14 µm and (b) 28 µm acquired with FC-WFM method, OS with FC-SIM method and the OS in DOF with proposed FC-WFM-Deep, from left to right, respectively.
Fig. 5.
Fig. 5. Comparison of the 3D reconstruction form (a) FC-SIM and (f) FC-WFM-Deep methods, where two x-z slices are randomly re-selected from the corresponding red (b), (g) and yellow (c), (h) boxes in 3D imaging, respectively. In addition, to show the details in the x-z planes better, two profiles along the white and blue dash lines are respectively shown in (d), (e), (i) and (j) as well.
Fig. 6.
Fig. 6. The MIP imaging results of the compound eye using the three methods: (a) original FC-WFM, (d) FC-SIM, and (g) the proposed FC-WFM-Deep. (b), (e), (h) Corresponding 3D height map in the region of interest randomly selected within the red dashed box and (c), (f), (i) the profile along the white dashed lines are also compared. It should be noted that the reconstruction MIP of FC-WFM uses the same number of slices as the FC-SIM results, i.e., 161 slices, to ensure the comparison is convenient and fair.
Fig. 7.
Fig. 7. The estimated errors between the FC-SIM and the FC-WFM-Deep of (a) the profiles, i.e., the difference along the white dashed line in Fig. 6, and (b) the entire 3D height maps approximated with different imaging results.
Fig. 8.
Fig. 8. Results of MIP images of two types of beetles, a shining leaf chafer in the top row and a leaf beetle in the bottom row, with (a), (d) FC-WFM, (b), (e) FC-SIM and (c), (f) the proposed FC-WFM-Deep, respectively. FC-WFM and FC-SIM used the same number of slices, i.e., 160 slices, to reconstruct their MIP results.
Fig. 9.
Fig. 9. Corresponding 3D height maps for the randomly selected ROI with the white dashed box in Fig. 8 for (a), (d) FC-SIM and (b), (e) FC-WFM-Deep. The estimated errors between the FC-SIM and the FC-WFM-Deep of the entire 3D height maps approximated with different imaging results, respectively of (c) the leaf chafer in the top row and (f) the leaf beetle in the bottom row.

Tables (2)

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Table 1. Dataset in FC-WFM-Deep imaging

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Table 2. Quantitative Comparison between the FC-SIM and FC-WFM-Deep Methods

Equations (6)

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S i n , p = ( 2 S π / π 2 , p 2 , p S 0 , p S π , p ) 2 + ( S 0 , p S π , p ) 2 2 ,
I ( z , v ) = 2 g ( v ) | J 1 [ 4 u v ( 1 v ) ] 4 u v ( 1 v ) | ,
S w f , p = 1 2 ( S 0 , p + S π , p ) .
d DOF = λ n N A 2 + n M NA e ,
D ( m ~ , n ~ ) = O j ( m ~ , n ~ ; arg max z | σ j 2 ( m ~ , n ~ ; z ) | ) ,
( Θ ) = 1 2 T i = 1 T | | f ( X i ; Θ ) R i | | 2 .

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