Abstract

Although widely recognized as a promising candidate for the next generation of data storage devices, holographic data storage systems (HDSS) incur adverse effects such as noise, misalignment, and aberration. Therefore, based on the structural similarity (SSIM) concept, this work presents a more accurate locating approach than the gray level weighting method (GLWM). Three case studies demonstrate the effectiveness of the proposed approach. Case 1 focuses on achieving a high performance of a Fourier lens in HDSS, Cases 2 and 3 replace the Fourier lens with a normal lens to decrease the quality of the HDSS, and Case 3 demonstrates the feasibility of a defocus system in the worst-case scenario. Moreover, the bit error rate (BER) is evaluated in several average matrices extended from the located position. Experimental results demonstrate that the proposed SSIM method renders a more accurate centering and a lower BER, lower BER of 2 dB than those of the GLWM in Cases 1 and 2, and BER of 1.5 dB in Case 3.

© 2012 Optical Society of America

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References

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2011 (1)

M. Ou-Yang and Y. T. Chen, “A gray level weighting method to reduce optical aberration effect in holographic data storage system,” IEEE Trans. Magn. 47, 546–550 (2011).
[CrossRef]

2009 (2)

Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26, 98–117 (2009).
[CrossRef]

D. Park and J. Lee, “Holographic data storage channel model with intensity factor,” IEEE Trans. Magn. 45, 2268–2271 (2009).
[CrossRef]

2007 (1)

H. Horimai and X. D. Tan, “Holographic information storage system: Today and future,” IEEE Trans. Magn. 43, 943–947 (2007).
[CrossRef]

2006 (1)

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. Image Process. 13, 600–612 (2004).
[CrossRef]

S. S. Orlov, W. Phillips, E. Bjornson, Y. Takashima, P. Sundaram, L. Hesselink, R. Okas, D. Kwan, and R. Snyder, “High-transfer-rate high-capacity holographic disk data-storage system,” Appl. Opt. 43, 4902–4914 (2004).
[CrossRef]

2002 (1)

2001 (1)

1998 (4)

1997 (1)

1996 (2)

1993 (1)

Asthana, P.

Ayres, M.

Bjornson, E.

Bovik, A. C.

Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26, 98–117 (2009).
[CrossRef]

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

Burr, G. W.

Chen, Y. T.

M. Ou-Yang and Y. T. Chen, “A gray level weighting method to reduce optical aberration effect in holographic data storage system,” IEEE Trans. Magn. 47, 546–550 (2011).
[CrossRef]

Chou, W. C.

Coufal, H.

Curtis, K.

Grygier, R. K.

Gurkan, K.

Heanue, J. F.

Hesselink, L.

Hoffnagle, J. A.

Horimai, H.

H. Horimai and X. D. Tan, “Holographic information storage system: Today and future,” IEEE Trans. Magn. 43, 943–947 (2007).
[CrossRef]

Hoskins, A.

Jefferson, C. M.

Kwan, D.

Lee, J.

D. Park and J. Lee, “Holographic data storage channel model with intensity factor,” IEEE Trans. Magn. 45, 2268–2271 (2009).
[CrossRef]

McDonald, M.

Neifeld, M. A.

Nordin, G. P.

Okas, R.

Orlov, S. S.

Ou-Yang, M.

M. Ou-Yang and Y. T. Chen, “A gray level weighting method to reduce optical aberration effect in holographic data storage system,” IEEE Trans. Magn. 47, 546–550 (2011).
[CrossRef]

Park, D.

D. Park and J. Lee, “Holographic data storage channel model with intensity factor,” IEEE Trans. Magn. 45, 2268–2271 (2009).
[CrossRef]

Phillips, W.

Sheikh, H. R.

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

Simoncelli, E. P.

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

Snyder, R.

Sundaram, P.

Takashima, Y.

Tan, X. D.

H. Horimai and X. D. Tan, “Holographic information storage system: Today and future,” IEEE Trans. Magn. 43, 943–947 (2007).
[CrossRef]

Wang, Z.

Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26, 98–117 (2009).
[CrossRef]

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

Weiss, T.

Appl. Opt. (7)

IEEE Signal Process. Mag. (1)

Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag. 26, 98–117 (2009).
[CrossRef]

IEEE Trans. Image Process. (1)

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

IEEE Trans. Magn. (3)

M. Ou-Yang and Y. T. Chen, “A gray level weighting method to reduce optical aberration effect in holographic data storage system,” IEEE Trans. Magn. 47, 546–550 (2011).
[CrossRef]

D. Park and J. Lee, “Holographic data storage channel model with intensity factor,” IEEE Trans. Magn. 45, 2268–2271 (2009).
[CrossRef]

H. Horimai and X. D. Tan, “Holographic information storage system: Today and future,” IEEE Trans. Magn. 43, 943–947 (2007).
[CrossRef]

Opt. Lett. (5)

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Figures (15)

Fig. 1.
Fig. 1.

Procedure of fiducial points identifying with GLWM.

Fig. 2.
Fig. 2.

Fiducial points identifying with GLWM.

Fig. 3.
Fig. 3.

Plots of the comparison between Pearson’s correlation method and SSIM method. (a) Swept checkerboard image comparison. (b) Comparison of correlation index in Pearson’s correlation method and SSIM method.

Fig. 4.
Fig. 4.

Sample of database matrices.

Fig. 5.
Fig. 5.

Example of fiducial points identifying with SSIM method.

Fig. 6.
Fig. 6.

Edge effect in swept checkerboard image by using SSIM method.

Fig. 7.
Fig. 7.

Summary of the variables varying with the determined value of SSIM formula (Eq. 1). (a) Standard deviation versus the value of the right part of SSIM formula. (b) Mean value versus the value of the left part of SSIM formula.

Fig. 8.
Fig. 8.

Sketch of HDSS and a block diagram of the fiducial point finding.

Fig. 9.
Fig. 9.

Image commonly received in three cases, that is, (a) Flens, (b) Nlens, (c) 0.05mm shifting, (d) 0.1mm shifting, (e) +0.05mm shifting, (f) +0.1mm shifting in descending order.

Fig. 10.
Fig. 10.

Plot of differential value versus gray level for a database matrix sweeping over the checkerboard image in Case 1.

Fig. 11.
Fig. 11.

Plot of differential value versus gray level for a database matrix sweeping over the checkerboard image in Case 2.

Fig. 12.
Fig. 12.

Comparison result of matrix size versus BER in Case 1.

Fig. 13.
Fig. 13.

Comparison result of matrix size versus BER in Case 2.

Fig. 14.
Fig. 14.

Comparison result of matrix size versus BER in Case 3.

Fig. 15.
Fig. 15.

Summary of shifting distance effects in HDSS.

Tables (1)

Tables Icon

Table 1. Summary of the SNR in All Cases

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

SSIM(r,t)=(2μrμt+C1)(μr2+μt2+C1)×(2σrt+C2)(σr2+σt2+C2),
BER=p0×p(1|0)+p1×p(0|1),
SNR=μ0+μ1(σ02+σ12)1/2,
σt2=[at0(σt02+μt02)+at1(σt12+μt12)][at0μt0+at1μt1]2=at0(σt02+μt02at0μt02)+at1(σt12+μt12at1μt12)2at0μt0at1μt1,
σr2=ar0(σr02+μr02ar0μr02)+ar1(σr12+μr12ar1μr12)2ar0μr0ar1μr1,
σrt=1(N1)x,y=1N(Grayr(x,y)μr)×(Grayt(x,y)μt)=1(N1)x,y=1NGrayr(x,y)×Grayt(x,y)Nμrμt,
μr=1N2i,j=1NGrayr(i,j),
μt=1N2i,j=1NGrayt(i,j),
σt2=1(N1)2i,j=1N[Grayt(i,j)μt]2,
σr2=1(N1)2i,j=1N[Grayr(i,j)μr]2,
σrt=1N1i,j=1N[Grayr(i,j)μr]×[Grayt(i,j)μt],
SSIMμr=BAAB[(μt2+μr2+C1)(σt2+σr2+C2)]2,A=(2μtμr+C1)(2σtr+C2),B=(μt2+μr2+C1)(σt2+σr2+C2),A=2μt(2σtr+C2)+(2μtμr+C1)[2N1x,y=1N(Grayt(x,y)μt)],B=2μr(σt2+σr2+C2)+(μt2+μr2+C1)[2(N1)2x,y=1N(Grayr(x,y)μr)],

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