Abstract

High-resolution retinal imaging requires dilating the pupil, and therefore exposing more aberrations that blur the image. We developed an image processing technique that takes advantage of the natural movement of the eye to average out some of the high-order aberrations and to oversample the retina. This method was implemented on a long sequence of retinal images of subjects with normal vision. We were able to resolve the structures of the size of single cells in the living human retina. The improvement of resolution is independent of the acquisition method, as long as the image is not warped during scanning. Consequently, even better results can be expected by implementing this technique on higher-resolution images.

© 2011 Optical Society of America

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2010

C. J. Wolsley, K. J. Saunders, G. Silvestri, and R. S. Anderson, “Comparing mfERGs with estimates of cone density from in vivo imaging of the photoreceptor mosaic using a modified Heidelberg retina tomograph,” Vision Res. 50, 1462–1468 (2010).
[CrossRef] [PubMed]

H. Li, J. Lu, G. Shi, and Y. Zhang, “Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm,” Biomedical Opt. Exp. 1, 31–40 (2010).
[CrossRef]

A. M. Labin and E. N. Ribak, “Retinal glial cells enhance human vision acuity,” Phys. Rev. Lett. 104, 158102 (2010).
[CrossRef] [PubMed]

2009

2008

2007

2006

2005

2004

2002

2001

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient superresolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573–583 (2001).
[CrossRef]

M. Elad and Y. H. Or, “A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur,” IEEE Trans. Image Process. 10, 1187–1193(2001).
[CrossRef]

M. Dubbelman and V. D. Heijde, “The shape of the aging human lens: curvature, equivalent refractive index and the lens paradox,” Vision Res. 41, 1867–1877 (2001).
[CrossRef] [PubMed]

1998

1997

1996

D. Miller, D. Williams, G. Morris, and J. Liang, “Images of cone photoreceptors in the living human eye,” Vision Res. 36, 1067–1079 (1996).
[CrossRef] [PubMed]

S. Marcos, R. Navarro, and P. Artal, “Coherent imaging of the cone mosaic in the living human eye,” J. Opt. Soc. Am. A 13, 897–905 (1996).
[CrossRef]

1992

A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, “High resolution image reconstruction from lower resolution image sequence and space-varying image restoration,” Proc. IEEE 3, 169–172 (1992).

1991

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

S. S. Gleason, M. A. Hunt, and W. B. Jatko, “Subpixel measurements of image features based on paraboloid surface fit,” Proc. SPIE 1386, 135–144 (1991).
[CrossRef]

C. A. Curcio and K. R. Sloan, “Packing geometry of human cone photoreceptors: variation with eccentricity and evidence for local anisotropy,” Vis. Neurosci. 9, 169–180 (1991).
[CrossRef]

1990

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).
[CrossRef] [PubMed]

1989

1988

D. R. Williams, “Topography of the foveal cone mosaic in the living human eye,” Vision Res. 28, 433–454 (1988).
[CrossRef] [PubMed]

1987

1986

1983

J. I. Yellott, “Spectral consequences of photoreceptor Sampling in the rhesus retina,” Science 221, 382–385 (1983).
[CrossRef] [PubMed]

Anderson, R. S.

C. J. Wolsley, K. J. Saunders, G. Silvestri, and R. S. Anderson, “Comparing mfERGs with estimates of cone density from in vivo imaging of the photoreceptor mosaic using a modified Heidelberg retina tomograph,” Vision Res. 50, 1462–1468 (2010).
[CrossRef] [PubMed]

Arathorn, D. W.

Artal, P.

Bigelow, C. E.

Bower, B.

Bracewell, R. N.

R. N. Bracewell, Fourier Analysis and Imaging (Springer, 2006).

Bueno, J. M.

Burns, S. A.

Campbell, M. C. W.

Catlin, D.

Choi, S.

Christou, J. C.

Chui, Y. P.

Coletta, N. J.

Curcio, C. A.

C. A. Curcio and K. R. Sloan, “Packing geometry of human cone photoreceptors: variation with eccentricity and evidence for local anisotropy,” Vis. Neurosci. 9, 169–180 (1991).
[CrossRef]

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).
[CrossRef] [PubMed]

Dainty, C.

DeHoog, E.

Donnelly, W. J.

Doucet, C.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Dubbelman, M.

M. Dubbelman and V. D. Heijde, “The shape of the aging human lens: curvature, equivalent refractive index and the lens paradox,” Vision Res. 41, 1867–1877 (2001).
[CrossRef] [PubMed]

Dubra, A.

A. Dubra and Z. Harvey, “Registration of 2D images from fast scanning ophthalmic instruments,” in Biomedical Image Registration, Lecture Notes in Computer Science (Springer, 2010), Vol. 6204, pp. 60–71.
[CrossRef]

Duchêne, G.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Dullemond, C.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Elad, M.

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J. Imaging Syst. Technol. 14, 47–57 (2004).
[CrossRef]

M. Elad and Y. H. Or, “A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur,” IEEE Trans. Image Process. 10, 1187–1193(2001).
[CrossRef]

Farsiu, S.

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J. Imaging Syst. Technol. 14, 47–57 (2004).
[CrossRef]

Ferguson, D. R.

Fitzke, F.

Gleason, S. S.

S. S. Gleason, M. A. Hunt, and W. B. Jatko, “Subpixel measurements of image features based on paraboloid surface fit,” Proc. SPIE 1386, 135–144 (1991).
[CrossRef]

Golub, G.

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient superresolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573–583 (2001).
[CrossRef]

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing(Prentice Hall, 2007).

Goodman, J. W.

J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, 1968).

J. W. Goodman, Speckle Phenomena in Optics Theory and Applications (Roberts, 2006).

Habart, E.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Hammer, D. X.

Harvey, Z.

A. Dubra and Z. Harvey, “Registration of 2D images from fast scanning ophthalmic instruments,” in Biomedical Image Registration, Lecture Notes in Computer Science (Springer, 2010), Vol. 6204, pp. 60–71.
[CrossRef]

Hebert, T. J.

Heijde, V. D.

M. Dubbelman and V. D. Heijde, “The shape of the aging human lens: curvature, equivalent refractive index and the lens paradox,” Vision Res. 41, 1867–1877 (2001).
[CrossRef] [PubMed]

Hendrickson, A. E.

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).
[CrossRef] [PubMed]

Hunt, M. A.

S. S. Gleason, M. A. Hunt, and W. B. Jatko, “Subpixel measurements of image features based on paraboloid surface fit,” Proc. SPIE 1386, 135–144 (1991).
[CrossRef]

Iftimia, N.

Iglesias, I.

Irani, M.

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

Izatt, J.

Jatko, W. B.

S. S. Gleason, M. A. Hunt, and W. B. Jatko, “Subpixel measurements of image features based on paraboloid surface fit,” Proc. SPIE 1386, 135–144 (1991).
[CrossRef]

Jonnal, R. S.

Kalina, R. E.

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).
[CrossRef] [PubMed]

Kasprzak, H.

Keren, D.

S. Peleg, D. Keren, and L. Schweitzer, “Improving image resolution using subpixel motion,” Pattern Recog. Lett. 5, 223–226(1987).
[CrossRef]

Labin, A. M.

A. M. Labin and E. N. Ribak, “Retinal glial cells enhance human vision acuity,” Phys. Rev. Lett. 104, 158102 (2010).
[CrossRef] [PubMed]

Lagage, P. O.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Laut, S.

Li, H.

H. Li, J. Lu, G. Shi, and Y. Zhang, “Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm,” Biomedical Opt. Exp. 1, 31–40 (2010).
[CrossRef]

Li, K. Y.

Liang, J.

Lu, J.

H. Li, J. Lu, G. Shi, and Y. Zhang, “Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm,” Biomedical Opt. Exp. 1, 31–40 (2010).
[CrossRef]

Marcos, S.

Meitav, N.

N. Meitav and E. N. Ribak (eribak@physics.technion.ac.il) are preparing a manuscript to be called “Measuring the fraction and spacing of closely-packed photoreceptors.”

Ménard, F.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Milanfar, P.

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J. Imaging Syst. Technol. 14, 47–57 (2004).
[CrossRef]

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient superresolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573–583 (2001).
[CrossRef]

Miller, D.

D. Miller, D. Williams, G. Morris, and J. Liang, “Images of cone photoreceptors in the living human eye,” Vision Res. 36, 1067–1079 (1996).
[CrossRef] [PubMed]

Miller, D. T.

Morris, G.

D. Miller, D. Williams, G. Morris, and J. Liang, “Images of cone photoreceptors in the living human eye,” Vision Res. 36, 1067–1079 (1996).
[CrossRef] [PubMed]

Navarro, R.

Nguyen, N.

N. Nguyen, P. Milanfar, and G. Golub, “A computationally efficient superresolution image reconstruction algorithm,” IEEE Trans. Image Process. 10, 573–583 (2001).
[CrossRef]

Nourrit, V.

Olivier, S.

Or, Y. H.

M. Elad and Y. H. Or, “A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur,” IEEE Trans. Image Process. 10, 1187–1193(2001).
[CrossRef]

Ozkan, M. K.

A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, “High resolution image reconstruction from lower resolution image sequence and space-varying image restoration,” Proc. IEEE 3, 169–172 (1992).

Pantin, E.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Parker, A.

Peleg, S.

M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

S. Peleg, D. Keren, and L. Schweitzer, “Improving image resolution using subpixel motion,” Pattern Recog. Lett. 5, 223–226(1987).
[CrossRef]

Pierscionek, B. K.

Pinte, C.

C. Doucet, E. Habart, E. Pantin, C. Dullemond, P. O. Lagage, C. Pinte, G. Duchêne, and F. Ménard, “HD 97048: a closer look at the disk,” Astron. Astrophys. 470, 625–631 (2007).
[CrossRef]

Qu, J.

Queener, H.

Rha, J.

Ribak, E.

Ribak, E. N.

A. M. Labin and E. N. Ribak, “Retinal glial cells enhance human vision acuity,” Phys. Rev. Lett. 104, 158102 (2010).
[CrossRef] [PubMed]

N. Meitav and E. N. Ribak (eribak@physics.technion.ac.il) are preparing a manuscript to be called “Measuring the fraction and spacing of closely-packed photoreceptors.”

Robinson, D.

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J. Imaging Syst. Technol. 14, 47–57 (2004).
[CrossRef]

Romero-Borja, F.

Roorda, A.

Saunders, K. J.

C. J. Wolsley, K. J. Saunders, G. Silvestri, and R. S. Anderson, “Comparing mfERGs with estimates of cone density from in vivo imaging of the photoreceptor mosaic using a modified Heidelberg retina tomograph,” Vision Res. 50, 1462–1468 (2010).
[CrossRef] [PubMed]

Schweitzer, L.

S. Peleg, D. Keren, and L. Schweitzer, “Improving image resolution using subpixel motion,” Pattern Recog. Lett. 5, 223–226(1987).
[CrossRef]

Schwiegerling, J.

Sezan, M. I.

A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, “High resolution image reconstruction from lower resolution image sequence and space-varying image restoration,” Proc. IEEE 3, 169–172 (1992).

Shi, G.

H. Li, J. Lu, G. Shi, and Y. Zhang, “Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm,” Biomedical Opt. Exp. 1, 31–40 (2010).
[CrossRef]

Siedlecki, D.

Silvestri, G.

C. J. Wolsley, K. J. Saunders, G. Silvestri, and R. S. Anderson, “Comparing mfERGs with estimates of cone density from in vivo imaging of the photoreceptor mosaic using a modified Heidelberg retina tomograph,” Vision Res. 50, 1462–1468 (2010).
[CrossRef] [PubMed]

Sloan, K. R.

C. A. Curcio and K. R. Sloan, “Packing geometry of human cone photoreceptors: variation with eccentricity and evidence for local anisotropy,” Vis. Neurosci. 9, 169–180 (1991).
[CrossRef]

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292, 497–523 (1990).
[CrossRef] [PubMed]

Song, H.

Tekalp, A. M.

A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, “High resolution image reconstruction from lower resolution image sequence and space-varying image restoration,” Proc. IEEE 3, 169–172 (1992).

Thorn, K. E.

Ustun, T.

Vogel, C.

Vohnsen, B.

Wade, A.

Werner, J.

Williams, D.

D. Miller, D. Williams, G. Morris, and J. Liang, “Images of cone photoreceptors in the living human eye,” Vision Res. 36, 1067–1079 (1996).
[CrossRef] [PubMed]

Williams, D. R.

Wolsley, C. J.

C. J. Wolsley, K. J. Saunders, G. Silvestri, and R. S. Anderson, “Comparing mfERGs with estimates of cone density from in vivo imaging of the photoreceptor mosaic using a modified Heidelberg retina tomograph,” Vision Res. 50, 1462–1468 (2010).
[CrossRef] [PubMed]

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing(Prentice Hall, 2007).

Yellott, J. I.

J. I. Yellott, “Spectral consequences of photoreceptor Sampling in the rhesus retina,” Science 221, 382–385 (1983).
[CrossRef] [PubMed]

Zawadzki, R. J.

Zhang, Y.

H. Li, J. Lu, G. Shi, and Y. Zhang, “Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm,” Biomedical Opt. Exp. 1, 31–40 (2010).
[CrossRef]

J. Rha, R. S. Jonnal, K. E. Thorn, J. Qu, Y. Zhang, and D. T. Miller, “Adaptive optics flood illumination camera for high speed retinal imaging,” Opt. Express 14, 4552–4569 (2006).
[CrossRef] [PubMed]

Zhao, M.

Appl. Opt.

Astron. Astrophys.

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Biomedical Opt. Exp.

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CVGIP Graph. Models Image Process.

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IEEE Trans. Image Process.

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[CrossRef]

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[CrossRef]

Int. J. Imaging Syst. Technol.

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J. Imaging Syst. Technol. 14, 47–57 (2004).
[CrossRef]

J. Comp. Neurol.

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J. Opt. Soc. Am. A

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[CrossRef]

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[CrossRef]

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[CrossRef]

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[CrossRef]

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[CrossRef]

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Proc. SPIE

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[CrossRef]

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[CrossRef]

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[CrossRef] [PubMed]

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[CrossRef]

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R. N. Bracewell, Fourier Analysis and Imaging (Springer, 2006).

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Figures (13)

Fig. 1
Fig. 1

Averaging of high-order aberrations is exemplified with PSFs from three frames (left). By accurately shifting and summing the frames, weighted according to quality, the central peak is increased while the variable sidelobes average out (right).

Fig. 2
Fig. 2

Weighted shift and add through Fourier cross correlation. The dashed line marks the second iteration.

Fig. 3
Fig. 3

SNR improvement with more frames, for two different subjects. Adding more than 200 frames did not improve the signal to noise significantly.

Fig. 4
Fig. 4

One frame (left) and 100 added frames (right). Notice the improvement in contrast and detail and the drop in noise.

Fig. 5
Fig. 5

High-resolution retinal camera. The fixation target for the other eye (not shown) was the monitor of the pupil camera.

Fig. 6
Fig. 6

Artificial eye (left), the magnified image of the resolution target with 12 μm period (center), and through the artificial eye (right). Because of the incidence angle only the horizontal lines were visible.

Fig. 7
Fig. 7

(a) Single retinal frame compared to (b) the final image. The image is 4.5 ° across, with its right side at 14 ° nasal eccentricity. Blood vessel boundaries are not sharp due to averaging over many heart beats. Scale bar, 100 μm ; image area 1.6 mm × 1.08 mm ( 1400 × 900 pixels). The rectangles are the magnified ROIs in Fig. 8.

Fig. 8
Fig. 8

(a) Magnified ROI of one frame, (b) after registration and summation of 240 frames, and (c) after a one-time application of a high pass filter (blocking features over 20 μm ) to reduce the background fluctuations. Scale bar: 25 μm , images size 216 μm × 216 μm ( 1.2 μm / pixel ). Similar results were obtained at various eccentricities ( 1 ° 20 ° ).

Fig. 9
Fig. 9

Intensity histogram of Fig. 8a (left). Average of ten sequential frames showing the same statistics (right). Both histograms exclude the possibility of speckle statistics. The minimal value of each frame was deducted before calculating the histograms.

Fig. 10
Fig. 10

Averaged radial power spectrum for one frame (dashed red curve) and for the final image (continuous blue curve). The data were taken from a 120 μm × 120 μm ROI, without the use of any filter.

Fig. 11
Fig. 11

Average radial power spectrum at eccentricities of 1 ° (blue solid curve), 7 ° (red dashed curve), and 14 ° or 16 ° (black dash-dot curve) for two different subjects, along the nasal horizontal meridian. Each of the graphs is an average of nine adjacent 72 μm × 72 μm segments taken from final images of this method (without the use of any filter). At 1 ° eccentricity, where the cells are highly dense, the power spectrum has peaks at a smaller period (higher frequencies). At higher eccentricities, where the cones’ density is decreasing, the highest peaks are shifted to greater distances (lower frequencies).

Fig. 12
Fig. 12

Fourier spectrum (log scale) of a small section of the retina in one frame (top) and in 240 frames (bottom). The hexagonal pattern, caused by the spatial arrangement of the cells, is somewhat clearer on the processed image (unfiltered data).

Fig. 13
Fig. 13

Pathology repeats nearly perfectly in two mutually exclusive sets, processed each from 100 different frames, with different ROIs. Scale bar, 100 μm . Images size 1.3 mm × 0.8 mm ( 1100 × 700 pixels).

Equations (3)

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PSF ( ξ , t ) = | ( λ z ) 1 P ( r ) exp i k [ r · ξ / z + w ( r , t ) ] d 2 r | 2 ,
s ( x , y ) = i j f ( i , j ) p ( x i , y j ) ,
F { r g } = F { r } * × F { g } ,

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