Abstract

We study the performance of a hybrid technique for high resolution imaging of the eye fundus, in which the main part of the ocular aberration is compensated by an optical device (phase plate, deformable mirror, etc.) and the remaining aberration is compensated by deconvolution from wavefront sensing. A comparison among imaging with partial compensation, deconvolution from wavefront sensing and the presented hybrid technique is made using numerical simulations based on actual aberration data.

© 2003 Optical Society of America

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References

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Appl. Opt.

J. Opt. (Paris)

J.C. Fontanella, �??Analyse de surface d�??onde, déconvolution et optique active,�?? J. Opt. (Paris) 16, 257-268 (1985).
[CrossRef]

J. Opt. Soc. Am

J. Primot., et.al., �??Deconvolution from wavefront sensing: a new technique for compensating turbulencedegraded image,�?? J. Opt. Soc. Am 7 1598-1608 (1990).
[CrossRef]

J. Opt. Soc. Am. A

Opt. Eng.

J. Arines, S. Bará, �??Significance of the recovery filter in deconvolution from wavefront sensing,�?? Opt. Eng. 39, 2789-2796 (2000).
[CrossRef]

Opt. Lett.

Optom Vision Sci.

R. Navarro, M.A. Losada,. �??Aberrations and relative efficiency of light pencils in the living human eye,�?? Optom Vision Sci. 74, 540 (1997)
[CrossRef]

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Figures (6)

Fig.1.
Fig.1.

Modulus of the aberrated OTF (SB-blue, SM-red) and optically partial corrected OTF (SB-blue squared, SM-red circled). f is the spatial frequency normalized to the cutoff frequency of the unaberrated eye.

Fig. 2.
Fig. 2.

Simulated images degraded by the eye optics for (a) SB, (b) SM. Images after phase plate partial compensation: (c) SB, (d) SM.

Fig. 3.
Fig. 3.

Simulated images restored by DWFS and the hybrid technique PCDWFS for subjects SB and SM, using the vector Wiener filter with 30 samples.

Fig. 4.
Fig. 4.

Simulated images restored by DWFS and the hybrid technique PCDWFS for subjects SB and SM using the vector Wiener filter with only one image and aberration measurement pair.

Fig. 5.
Fig. 5.

Plots of the gain G (DWFS-blue; PC-red; PCDWFS-black) in function of the noise in the sensor (σ SH ), and image (SNRI) channels, subject SB. Vector Wiener filters with (a). k=30 and (b) k=1 (expected values).

Fig. 6.
Fig. 6.

SNRÔ for DWFS (blue) and PCDWFS (black).

Equations (3)

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O ˆ = j = 1 k I j H j * j = 1 k H j 2 + γ
G = 10 log { n , m ( o i ) 2 n , m ( o o ˆ ) 2 }
SNR O ˆ = O ˆ O ˆ 2 O ˆ 2

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