Abstract

Retinal imaging often suffers from blurring aberrations. With knowledge of the blurring point spread function (PSF), better images can be reconstructed by deconvolution techniques. We demonstrate a method to enhance the contrast of retinal cells by estimating the ocular PSF. This is done by finding the cells’ positions and their intensity distributions and using these as a model for the image. The feasibility of this method is demonstrated by Wiener deconvolution both for adaptively and nonadaptively corrected images.

© 2012 Optical Society of America

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References

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2011 (6)

2004 (1)

2002 (2)

2001 (2)

1997 (1)

1996 (1)

1988 (1)

1974 (1)

L. B. Lucy, Astron. J. 79, 745 (1974).
[CrossRef]

1972 (1)

Artal, P.

Ayers, G. R.

Blanco, L.

Bradley, A.

Burns, S. A.

Carroll, J.

Catlin, D.

Cheng, X.

Choi, S. S.

Christou, J.

Christou, J. C.

Codona, J. L.

Cooper, R. F.

Cox, I. G.

Dainty, C.

Dainty, J. C.

Doble, N.

Dubis, A. M.

Dubra, A.

Enoch, J. M.

Fernández, E. J.

Goncharov, A. V.

N. Meitav, E. N. Ribak, and A. V. Goncharov, “High-resolution retinal imaging by corneal immersion,” in preparation.

Goodman, J. W.

J. W. Goodman, Statistical Optics (Wiley-Interscience, 2000).

Guirao, A.

Hong, X.

Iglesias, I.

Liang, J.

Lucy, L. B.

L. B. Lucy, Astron. J. 79, 745 (1974).
[CrossRef]

Marcos, S.

Meitav, N.

N. Meitav and E. N. Ribak, J. Opt. Soc. Am. A 28, 1395 (2011).
[CrossRef]

N. Meitav and E. N. Ribak, Appl. Phys. Lett. 99, 221910 (2011).
[CrossRef]

N. Meitav, E. N. Ribak, and A. V. Goncharov, “High-resolution retinal imaging by corneal immersion,” in preparation.

Miller, D. T.

Mugnier, L. M.

Navarro, R.

Norris, J. L.

Porter, J.

Qi, X.

Ribak, E. N.

N. Meitav and E. N. Ribak, J. Opt. Soc. Am. A 28, 1395 (2011).
[CrossRef]

N. Meitav and E. N. Ribak, Appl. Phys. Lett. 99, 221910 (2011).
[CrossRef]

N. Meitav, E. N. Ribak, and A. V. Goncharov, “High-resolution retinal imaging by corneal immersion,” in preparation.

Richardson, W. H.

Roorda, A.

Sulai, Y.

Thibos, L. N.

Williams, D. R.

Zou, W.

Appl. Phys. Lett. (1)

N. Meitav and E. N. Ribak, Appl. Phys. Lett. 99, 221910 (2011).
[CrossRef]

Astron. J. (1)

L. B. Lucy, Astron. J. 79, 745 (1974).
[CrossRef]

Biomed. Opt. Express (2)

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (7)

Opt. Express (1)

Opt. Lett. (3)

Other (2)

J. W. Goodman, Statistical Optics (Wiley-Interscience, 2000).

N. Meitav, E. N. Ribak, and A. V. Goncharov, “High-resolution retinal imaging by corneal immersion,” in preparation.

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Figures (5)

Fig. 1.
Fig. 1.

Cells’ model construction. By using the image as an input (left), each cell’s position is found (center) and is replaced by a fixed circular disk of its averaged radial intensity (right).

Fig. 2.
Fig. 2.

Testing the method with an artificial image. (a) First, the method is applied to an ideal image. (b) The PSF estimate is similar to the Dirac delta function. (c) A blurring core is formed. (d) The image after convolution with the core and addition of Gaussian noise. (e) The estimated blurring core to be compared with (c).

Fig. 3.
Fig. 3.

(a) PSF estimation from an adaptive optics retinal image, courtesy of Dr. Laurent Vabre, Imagine Eye Ltd., France (470×470pixels, scale bar ~15 μm). (b) The estimated PSF. (c) The reconstructed image after Wiener filtering by the PSF, showing marginal improvement. The dashed circles are the area used for the intensity profile in Fig. 4(b).

Fig. 4.
Fig. 4.

(a) Comparison between the radially averaged power spectrum of an AO image section (solid line) and the reconstructed image (dashed). (b) Intensity profile of two adjacent cells on the verge of the resolution limit.

Fig. 5.
Fig. 5.

PSF estimation process and Wiener image reconstruction. Top row: image and estimated PSF. Bottom row: reconstructed image and power spectra of the image (solid) and the reconstruction (dashed). Scale bars, 30 µm and 15 µm for the large and small fields of view.

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