Abstract

Traditional approaches to wide field of view (FoV) imager design usually lead to overly complex optics with high optical mass and/or pan-tilt mechanisms that incur significant mechanical/weight penalties, which limit their applications, especially on mobile platforms such as unmanned aerial vehicles (UAVs). We describe a compact wide FoV imager design based on superposition imaging that employs thin film shutters and multiple beamsplitters to reduce system weight and eliminate mechanical pointing. The performance of the superposition wide FoV imager is quantified using a simulation study and is experimentally demonstrated. Here, a threefold increase in the FoV relative to the narrow FoV imaging optics employed imager design is realized. The performance of a superposition wide FoV imager is analyzed relative to a traditional wide FoV imager and we find that it can offer comparable performance.

© 2010 Optical Society of America

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References

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    [CrossRef]
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2009 (2)

2008 (1)

2006 (1)

2002 (1)

2001 (1)

R. D. Fiete, and T. Tantalo, “Comparison of SNR image quality metrics for remote sensing systems,” Opt. Eng. 40, 574–585 (2001).
[CrossRef]

1989 (1)

1971 (1)

1968 (1)

Bhagavatula, V. K.

Brady, D.

Brady, D. J.

Decker, J. A.

Eldeniz, C.

Fiete, R. D.

R. D. Fiete, and T. Tantalo, “Comparison of SNR image quality metrics for remote sensing systems,” Opt. Eng. 40, 574–585 (2001).
[CrossRef]

Gehm, M. E.

Goodman, N. A.

Haberfelde, T.

Harwitt, M. O.

John, R.

Kim, C.

Kim, J.

Lohmann, A. W.

Mahalanobis, A.

Marcia, R. F.

McCain, S. T.

Neifeld, M.

Neifeld, M. A.

Pitsianis, N. P.

Potuluri, P.

Sullivan, M. E.

Tantalo, T.

R. D. Fiete, and T. Tantalo, “Comparison of SNR image quality metrics for remote sensing systems,” Opt. Eng. 40, 574–585 (2001).
[CrossRef]

Uttam, S.

Willett, R. M.

Appl. Opt. (5)

Opt. Eng. (1)

R. D. Fiete, and T. Tantalo, “Comparison of SNR image quality metrics for remote sensing systems,” Opt. Eng. 40, 574–585 (2001).
[CrossRef]

Opt. Express (2)

Opt. Lett. (1)

Other (4)

K. M. Douglass, T. Kohlgraf-Owens, J. Ellis, C. Toma, A. Mahalanobis, and A. Dogariu, “Expanded field of view using polarization multiplexing,” in Computational Optical Sensing and Imaging, OSA Technical Digest (CD) (Optical Society of America, 2009), paper CWA5.

S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Prentice-Hall, 1993).

M. D. Stenner, P. Shankar, and M. A. Neifeld, “Wide-field feature-specific imaging,” in Frontiers in Optics, (Optical Society of America, 2007), paper FMJ2.

H. H. Barrett, and K. J. Myers, Foundations of Image Science (Wiley, 2004).

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Figures (11)

Fig. 1
Fig. 1

Diagram of superposition imaging, Σjxjp = mip

Fig. 2
Fig. 2

Conceptual 3-FoV system top view: (a) diagram (b) system parameters

Fig. 3
Fig. 3

Simulated composite image measurements with σn=0.01; (a) 〈00〉 : X1 + X2 + X3, (b) 〈01〉 : X2 + X3 + leakage, (c) 〈11〉 : X3 + leakage

Fig. 4
Fig. 4

Simulated LMMSE reconstruction; (a) X̂1: “ECE”, (b) X̂2: “Old Main”, (c) X̂3: “OSC”

Fig. 5
Fig. 5

Simulated conventional imager with σn = 0.01; (a) “ECE”, (b) “Old Main”, (c) “OSC”

Fig. 6
Fig. 6

Average reconstruction SNR vs number of fields of view

Fig. 7
Fig. 7

Prototype optical multiplexer

Fig. 8
Fig. 8

Low resolution wide field view of the object space. The “1”, “2”, and “3” marker dimensions are 14 cm × 21.6 cm

Fig. 9
Fig. 9

High resolution “soda straw” view of the object space (a) FoV 1, (b) FoV 2, (c) FoV 3

Fig. 10
Fig. 10

Prototype optical multiplexer composite image measurements: (a) 〈00〉 : X1 + X2 + X3, (b) 〈01〉 : X2 + X3+ leakage, (c) 〈11〉 : X3+ leakage

Fig. 11
Fig. 11

LMMSE reconstruction (a) X̂1 (b) X̂2 (c) X̂3

Tables (1)

Tables Icon

Table 1 Conventional and superposition SNR comparison for σn = 0.03

Equations (14)

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SNR = ( η Q τ / I ) 2 ( η Q τ / I ) + I σ n 2 .
SNR = ( η Q τ J / I ) 2 ( η Q τ J / I ) + I σ n 2 = J ( η Q τ / I ) 2 ( η Q τ / I ) + ( I σ n 2 / J ) .
m i p = j = 1 J ( h i j x j p ) + n
h 11 = α 3 T 2 α 2 T 2 α 1 R k 2 2 k 3 2 h 12 = α 3 T 2 α 2 R k 3 2 h 13 = α 3 R
[ m 1 m 2 m 3 ] = [ h 11 h 12 h 13 h 21 h 22 h 23 h 31 h 32 h 33 ] [ x 1 x 2 x 3 ] + [ n 1 n 2 n 3 ]
m = Hx + n
H = [ 1 1 1 0 1 1 0 0 1 ] .
mSNR i = ( j = 1 J h i j η Q τ / I ) 2 ( j = 1 J h i j η Q τ / 1 ) + I σ n 2 .
x ^ = C x H T ( H C x H T + C n ) 1 m = Wm
rSNR j = ( i = 1 I w j i j = 1 J h i j η Q τ / I ) 2 i = 1 I w j i 2 ( j = 1 J ( h i j η Q τ / I ) + I σ n 2 ) .
rSNR j = ( i = 1 I w j i j = 1 J h i j η Q τ / I ) 2 i = 1 I w j i 2 j = 1 J h i j η Q τ / I .
rSNR j = ( i = 1 I w j i j = 1 J h i j η Q τ ) 2 I 3 i = 1 I w j i 2 σ n 2 .
H MSE * = arg min H j = 1 J { E [ | | X j exp X ^ j | | 2 2 ] } s . t . h 13 = h 23 = h 33 h 12 = h 22
H MSE * = [ 0.8577 1.0000 0.9963 0.3164 1.0000 0.9963 0.3015 0.3109 0.9963 ] .

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