Abstract

The evolution of experimental superresolution microscopy has been accompanied by the development of advanced computational imaging capabilities. Recently introduced, quantum image scanning microscopy (Q-ISM) has successfully harnessed quantum correlations of light to establish an improved viable imaging modality that builds upon the preceding image scanning microscopy (ISM) superresolution method. While offering improved resolution, at present the inherently weak signal demands exhaustively long acquisition periods. Here we exploit the fact that the correlation measurement in Q-ISM is complementary to the standard ISM data, acquired simultaneously, and demonstrate joint sparse recovery from Q-ISM and ISM images. Reconstructions from images of fluorescent quantum dots are validated through correlative electron microscope measurements, and exhibit superior resolution enhancement as compared to Q-ISM images. In addition, the algorithmic fusion facilitates a drastic reduction in the requisite measurement duration, since low signal-to-noise-ratio Q-ISM measurements suffice for augmenting ISM images. Finally, we obtain enhanced superresolved reconstructions from short scans of a biological sample labeled with quantum dots, demonstrating the potential of our method for quantum imaging in life science microscopy.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2019 (3)

R. Tenne, U. Rossman, B. Rephael, Y. Israel, A. Krupinski-Ptaszek, R. Lapkiewicz, Y. Silberberg, and D. Oron, “Super-resolution enhancement by quantum image scanning microscopy,” Nat. Photonics 13, 116–122 (2019).
[Crossref]

O. Solomon, Y. C. Eldar, M. Mutzafi, and M. Segev, “SPARCOM: sparsity based super-resolution correlation microscopy,” SIAM J. Imaging Sci. 12, 392–419 (2019).
[Crossref]

E. Toninelli, P.-A. Moreau, T. Gregory, A. Mihalyi, M. Edgar, N. Radwell, and M. Padgett, “Resolution-enhanced quantum imaging by centroid estimation of biphotons,” Optica 6, 347–353 (2019).
[Crossref]

2018 (3)

2017 (3)

Y. Israel, R. Tenne, D. Oron, and Y. Silberberg, “Quantum correlation enhanced super-resolution localization microscopy enabled by a fibre bundle camera,” Nat. Commun. 8, 14786 (2017).
[Crossref]

K. S. Grußmayer and D.-P. Herten, “Time-resolved molecule counting by photon statistics across the visible spectrum,” Phys. Chem. Chem. Phys. 19, 8962–8969 (2017).
[Crossref]

A. Classen, J. von Zanthier, M. O. Scully, and G. S. Agarwal, “Superresolution via structured illumination quantum correlation microscopy,” Optica 4, 580–587 (2017).
[Crossref]

2015 (2)

H. Ta, J. Keller, M. Haltmeier, S. K. Saka, J. Schmied, F. Opazo, P. Tinnefeld, A. Munk, and S. W. Hell, “Mapping molecules in scanning far-field fluorescence nanoscopy,” Nat. Commun. 6, 7977 (2015).
[Crossref]

J. Huff, “The Airyscan detector from ZEISS: confocal imaging with improved signal-to-noise ratio and super-resolution,” Nat. Methods 12, 1205 (2015).
[Crossref]

2014 (2)

D. Gatto Monticone, K. Katamadze, P. Traina, E. Moreva, J. Forneris, I. Ruo-Berchera, P. Olivero, I. P. Degiovanni, G. Brida, and M. Genovese, “Beating the Abbe diffraction limit in confocal microscopy via nonclassical photon statistics,” Phys. Rev. Lett. 113, 143602 (2014).
[Crossref]

T. Chernyakova and Y. C. Eldar, “Fourier-domain beamforming: the path to compressed ultrasound imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61, 1252–1267 (2014).
[Crossref]

2013 (1)

O. Schwartz, J. M. Levitt, R. Tenne, S. Itzhakov, Z. Deutsch, and D. Oron, “Superresolution microscopy with quantum emitters,” Nano Lett. 13, 5832–5836 (2013).
[Crossref]

2012 (4)

O. Schwartz and D. Oron, “Improved resolution in fluorescence microscopy using quantum correlations,” Phys. Rev. A 85, 033812 (2012).
[Crossref]

N. Wagner, Y. C. Eldar, and Z. Friedman, “Compressed beamforming in ultrasound imaging,” IEEE Trans. Signal Process. 60, 4643–4657 (2012).
[Crossref]

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nat. Mater. 11, 455–459 (2012).
[Crossref]

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nat. Methods 9, 721–723 (2012).
[Crossref]

2011 (1)

G. Vicidomini, G. Moneron, K. Y. Han, V. Westphal, H. Ta, M. Reuss, J. Engelhardt, C. Eggeling, and S. W. Hell, “Sharper low-power STED nanoscopy by time gating,” Nat. Methods 8, 571–573 (2011).
[Crossref]

2010 (4)

L. C. Potter, E. Ertin, J. T. Parker, and M. Cetin, “Sparsity and compressed sensing in radar imaging,” Proc. IEEE 98, 1006–1020 (2010).
[Crossref]

Y. Shechtman, S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse images carried by incoherent light,” Opt. Lett. 35, 1148–1150 (2010).
[Crossref]

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).
[Crossref]

C. B. Müller and J. Enderlein, “Image scanning microscopy,” Phys. Rev. Lett. 104, 198101 (2010).
[Crossref]

2009 (4)

T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. USA 106, 22287–22292 (2009).
[Crossref]

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95, 131110 (2009).
[Crossref]

S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse sub-wavelength images,” Opt. Express 17, 23920–23946 (2009).
[Crossref]

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2, 183–202 (2009).
[Crossref]

2008 (2)

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. 47, 6172–6176 (2008).
[Crossref]

M. Mishali and Y. C. Eldar, “Reduce and boost: recovering arbitrary sets of jointly sparse vectors,” IEEE Trans. Signal Process. 56, 4692–4702 (2008).
[Crossref]

2007 (2)

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: the application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58, 1182–1195 (2007).
[Crossref]

M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007).
[Crossref]

2006 (6)

J. A. Tropp, “Just relax: convex programming methods for identifying sparse signals in noise,” IEEE Trans. Inf. Theory 52, 1030–1051 (2006).
[Crossref]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[Crossref]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[Crossref]

E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?” IEEE Trans. Inf. Theory 52, 5406–5425 (2006).
[Crossref]

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3, 793–795 (2006).
[Crossref]

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

2001 (1)

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Rev. 43, 129–151 (2001).
[Crossref]

2000 (1)

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

1996 (1)

R. Tibshirani, “Regression shrinkage and selection via the lasso,” J. R. Stat. Soc. B 58, 267–288 (1996).
[Crossref]

1994 (1)

1988 (1)

C. J. R. Sheppard, “Superresolution in confocal imaging,” Optik 80, 53–54 (1988).

1896 (1)

Lord Rayleigh, “XV. On the theory of optical images, with special reference to the microscope,” London, Edinburgh, Dublin Philos. Mag. J. Sci. 42(255), 167–195 (1896).
[Crossref]

1873 (1)

E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Arch. Mikrosk. Anat. 9, 413–418 (1873).
[Crossref]

Abbe, E.

E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Arch. Mikrosk. Anat. 9, 413–418 (1873).
[Crossref]

Agarwal, G. S.

Baraniuk, R.

R. Baraniuk and P. Steeghs, “Compressive radar imaging,” in IEEE National Radar Conference (2007), pp. 128–133.

Bates, M.

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3, 793–795 (2006).
[Crossref]

Beck, A.

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2, 183–202 (2009).
[Crossref]

Betzig, E.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

Bonifacino, J. S.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

Brida, G.

D. Gatto Monticone, K. Katamadze, P. Traina, E. Moreva, J. Forneris, I. Ruo-Berchera, P. Olivero, I. P. Degiovanni, G. Brida, and M. Genovese, “Beating the Abbe diffraction limit in confocal microscopy via nonclassical photon statistics,” Phys. Rev. Lett. 113, 143602 (2014).
[Crossref]

Bromberg, Y.

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95, 131110 (2009).
[Crossref]

Bullkich, E.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nat. Mater. 11, 455–459 (2012).
[Crossref]

Candes, E. J.

E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?” IEEE Trans. Inf. Theory 52, 5406–5425 (2006).
[Crossref]

Candès, E. J.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[Crossref]

Cetin, M.

L. C. Potter, E. Ertin, J. T. Parker, and M. Cetin, “Sparsity and compressed sensing in radar imaging,” Proc. IEEE 98, 1006–1020 (2010).
[Crossref]

Chen, S. S.

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Rev. 43, 129–151 (2001).
[Crossref]

Chernyakova, T.

T. Chernyakova and Y. C. Eldar, “Fourier-domain beamforming: the path to compressed ultrasound imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61, 1252–1267 (2014).
[Crossref]

Classen, A.

Cohen, D.

D. Cohen and Y. C. Eldar, “Sub-Nyquist radar systems: temporal, spectral, and spatial compression,” IEEE Signal Process. Mag. 35(6), 35–58 (2018).
[Crossref]

Cohen, O.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nat. Mater. 11, 455–459 (2012).
[Crossref]

Cohen-Hyams, T.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nat. Mater. 11, 455–459 (2012).
[Crossref]

Colyer, R.

T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. USA 106, 22287–22292 (2009).
[Crossref]

Dana, H.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nat. Mater. 11, 455–459 (2012).
[Crossref]

Davidson, M. W.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

Degiovanni, I. P.

D. Gatto Monticone, K. Katamadze, P. Traina, E. Moreva, J. Forneris, I. Ruo-Berchera, P. Olivero, I. P. Degiovanni, G. Brida, and M. Genovese, “Beating the Abbe diffraction limit in confocal microscopy via nonclassical photon statistics,” Phys. Rev. Lett. 113, 143602 (2014).
[Crossref]

Dertinger, T.

T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. USA 106, 22287–22292 (2009).
[Crossref]

Deutsch, Z.

O. Schwartz, J. M. Levitt, R. Tenne, S. Itzhakov, Z. Deutsch, and D. Oron, “Superresolution microscopy with quantum emitters,” Nano Lett. 13, 5832–5836 (2013).
[Crossref]

Donoho, D.

M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: the application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58, 1182–1195 (2007).
[Crossref]

Donoho, D. L.

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[Crossref]

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Rev. 43, 129–151 (2001).
[Crossref]

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Supplementary Material (1)

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Figures (4)

Fig. 1.
Fig. 1. Generation and synthesis of ISM and Q-ISM images. (a) Optical setup. The standard pinhole in a confocal microscope is replaced by a fiber bundle (FB), which guides the impinging fluorescent light to 14 individual SPADs. Exc, excitation laser; L, lens; DM, dichroic mirror; Obj, objective lens; (b) schematics of resolution enhancement in ISM and Q-ISM; (i) laser excitation profile, hexc; (ii) detection probability distribution, hdet, shown for two point-like detectors (det number 2 and 4) with unity magnification; (iii) ISM PSF, hISM, for each detector is the product of its detection distribution, and the excitation profile; (iv) effective Q-ISM PSF, hQISM, is the product of the two ISM PSFs of the two detectors. For simplicity, we use 1D illustrations and approximate the PSFs to be Gaussian. (c) ISM and Q-ISM images are integrated to generate a joint SR image (experimental results).
Fig. 2.
Fig. 2. Advantage of joint SR, simulation results; (a) ground truth of a scene of single photon emitters. Left-hand side, (b)–(f) optical images and reconstruction results from a scan featuring a 50 nm step size and 100 ms pixel dwell time; (b) ISM image; (c) Q-ISM image; (d) ISM SR; (e) Q-ISM SR; (f) JSR. Right-hand side, (g)–(k) optical images and reconstruction results from a scan featuring a 50 nm step size and 10 ms pixel dwell time; (g) ISM image; (h) Q-ISM image; (i) ISM SR; (j) Q-ISM SR; (k) JSR. Color bars of (b) and (g) represent detected photon counts. Color bars of (c) and (h) represent the number of missing detected photon pairs. Scale bar, 0.25 μm.
Fig. 3.
Fig. 3. SRs and correlative electron microscopy. (a) and (b) Images processed from a confocal scan (50 nm step size, using 10 ms segment from the pixel dwell time) of a cluster of fluorescent QDs; (a) ISM image; color bar represents number of detected photons; (b) Q-ISM image; color bar represents missing detected photon pairs; (c) SEM image of the same cluster that was optically measured. (d)–(f) Algorithmic reconstructions of the scene. (d) ISM SR; (e) Q-ISM SR; (f) JSR. (g)–(i) Refined correlative SEM image overlaid with each of the reconstructions. (g) ISM SR; (h) Q-ISM SR; (i) JSR. Reconstructions are in green, refined SEM image is in purple, and white represents the spatial overlap between them. Scale bar, 0.25 μm.
Fig. 4.
Fig. 4. JSR of labeled microtubule cell samples. Left-hand side, (a)–(e), optical images and reconstruction results from a scan (50 nm step size, 100 ms pixel dwell time) of microtubules in a fixed 3T3 cell labeled with fluorescent QDs (QDot 625, Thermo Fisher). (a) ISM image; (b) Q-ISM image; (c) ISM SR; (d) Q-ISM SR; (e) JSR. Right-hand side, (f)–(j), optical images and reconstruction results from the same scan in (a) and (b), with a digitally cropped 10 ms pixel dwell time; (f) ISM image; (g) Q-ISM image; (h) ISM SR; (i) Q-ISM SR; (j) JSR. Color bars of (a) and (f) represent detected photon counts. Color bars of (b) and (g) represent the number of missing detected photon pairs. Scale bar, 0.5 μm.

Equations (2)

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min{xAxy22+λx1},
minx1,x2{A1x1y122+ηA2x2y222+λX2,1}.