Abstract

We describe a multiple-aperture long-wave infrared camera built on an uncooled microbolometer array with the objective of decreasing camera thickness. The 5mm thick optical system is an f/1.2 design with a 6.15mm effective focal length. An integrated image is formed from the subapertures using correlation-based registration and a least gradient reconstruction algorithm. We measure a 131mK NETD. The system’s spatial frequency is analyzed with 4  bar targets. With proper calibration, our multichannel interpolation results recover contrast for targets at frequencies beyond the aliasing limit of the individual subimages.

© 2009 Optical Society of America

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References

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2008

2006

M. W. Haney, “Performance scaling in flat imagers,” Appl. Opt. 45, 2901-2910 (2006).
[CrossRef] [PubMed]

D. J. Brady, M. E. Gehm, N. Pitsianis, and X. Sun, “Compressive sampling strategies for integrated microspectrometers,” Proc. SPIE 6232, 62320C (2006).
[CrossRef]

Y. C. Eldar and M. Unser, “Nonideal sampling and interpolation from noisy observations in shift-invariant spaces,” IEEE Trans. Signal Process. 54, 2636-2651 (2006).
[CrossRef]

2005

M. Ben-Ezra, A. Zomet, and S. K. Nayar, “Video super-resolution using controlled subpixel detector shifts,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 977-987 (2005).
[CrossRef] [PubMed]

2003

Sung Cheol Park, Min Kyu Park, and Moon Gi Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21-36 (2003).
[CrossRef]

2001

1999

1995

A. J. Coulson, “A generalization of nonuniform bandpass sampling,” IEEE Trans. Signal Process. 43, 694-704 (1995).
[CrossRef]

1989

Aldroubi, A.

A. Aldroubi and K. Grochenig, “Nonuniform sampling and reconstruction in shift-invariant spaces,” SIAM Rev. 43, 585-620 (2001).
[CrossRef]

Ben-Ezra, M.

M. Ben-Ezra, A. Zomet, and S. K. Nayar, “Video super-resolution using controlled subpixel detector shifts,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 977-987 (2005).
[CrossRef] [PubMed]

Boreman, G. D.

E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, 1996).

Brady, D.

Brady, D. J.

A. D. Portnoy, N. P. Pitsianis, X. Sun, and D. J. Brady, “Multichannel sampling schemes for optical imaging systems,” Appl. Opt. 47, B76-B85 (2008).
[CrossRef] [PubMed]

D. J. Brady, M. E. Gehm, N. Pitsianis, and X. Sun, “Compressive sampling strategies for integrated microspectrometers,” Proc. SPIE 6232, 62320C (2006).
[CrossRef]

Carriere, J.

Chellappa, R.

Chen, C.

Coulson, A. J.

A. J. Coulson, “A generalization of nonuniform bandpass sampling,” IEEE Trans. Signal Process. 43, 694-704 (1995).
[CrossRef]

Dereniak, E. L.

E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, 1996).

Eldar, Y. C.

Y. C. Eldar and M. Unser, “Nonideal sampling and interpolation from noisy observations in shift-invariant spaces,” IEEE Trans. Signal Process. 54, 2636-2651 (2006).
[CrossRef]

Gehm, M. E.

D. J. Brady, M. E. Gehm, N. Pitsianis, and X. Sun, “Compressive sampling strategies for integrated microspectrometers,” Proc. SPIE 6232, 62320C (2006).
[CrossRef]

Gibbons, R.

Grochenig, K.

A. Aldroubi and K. Grochenig, “Nonuniform sampling and reconstruction in shift-invariant spaces,” SIAM Rev. 43, 585-620 (2001).
[CrossRef]

Haney, M. W.

Ichioka, Y.

Ishida, K.

Kang, Moon Gi

Sung Cheol Park, Min Kyu Park, and Moon Gi Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21-36 (2003).
[CrossRef]

Kolste, R. T.

Kondou, N.

Kumagai, T.

Lohmann, A. W.

Miyatake, S.

Miyazaki, D.

Morimoto, T.

Nayar, S. K.

M. Ben-Ezra, A. Zomet, and S. K. Nayar, “Video super-resolution using controlled subpixel detector shifts,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 977-987 (2005).
[CrossRef] [PubMed]

Park, Min Kyu

Sung Cheol Park, Min Kyu Park, and Moon Gi Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21-36 (2003).
[CrossRef]

Park, Sung Cheol

Sung Cheol Park, Min Kyu Park, and Moon Gi Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21-36 (2003).
[CrossRef]

Pitsianis, N.

Pitsianis, N. P.

Portnoy, A. D.

Prather, D.

Schulz, T.

Shankar, M.

Shekarforoush, H.

Sun, X.

A. D. Portnoy, N. P. Pitsianis, X. Sun, and D. J. Brady, “Multichannel sampling schemes for optical imaging systems,” Appl. Opt. 47, B76-B85 (2008).
[CrossRef] [PubMed]

D. J. Brady, M. E. Gehm, N. Pitsianis, and X. Sun, “Compressive sampling strategies for integrated microspectrometers,” Proc. SPIE 6232, 62320C (2006).
[CrossRef]

Tanida, J.

Unser, M.

Y. C. Eldar and M. Unser, “Nonideal sampling and interpolation from noisy observations in shift-invariant spaces,” IEEE Trans. Signal Process. 54, 2636-2651 (2006).
[CrossRef]

Willett, R.

Yamada, K.

Zomet, A.

M. Ben-Ezra, A. Zomet, and S. K. Nayar, “Video super-resolution using controlled subpixel detector shifts,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 977-987 (2005).
[CrossRef] [PubMed]

Appl. Opt.

IEEE Signal Process. Mag.

Sung Cheol Park, Min Kyu Park, and Moon Gi Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21-36 (2003).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

M. Ben-Ezra, A. Zomet, and S. K. Nayar, “Video super-resolution using controlled subpixel detector shifts,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 977-987 (2005).
[CrossRef] [PubMed]

IEEE Trans. Signal Process.

Y. C. Eldar and M. Unser, “Nonideal sampling and interpolation from noisy observations in shift-invariant spaces,” IEEE Trans. Signal Process. 54, 2636-2651 (2006).
[CrossRef]

A. J. Coulson, “A generalization of nonuniform bandpass sampling,” IEEE Trans. Signal Process. 43, 694-704 (1995).
[CrossRef]

J. Opt. Soc. Am. A

Proc. SPIE

D. J. Brady, M. E. Gehm, N. Pitsianis, and X. Sun, “Compressive sampling strategies for integrated microspectrometers,” Proc. SPIE 6232, 62320C (2006).
[CrossRef]

SIAM Rev.

A. Aldroubi and K. Grochenig, “Nonuniform sampling and reconstruction in shift-invariant spaces,” SIAM Rev. 43, 585-620 (2001).
[CrossRef]

Other

E. L. Dereniak and G. D. Boreman, Infrared Detectors and Systems (Wiley, 1996).

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Figures (14)

Fig. 1
Fig. 1

Comparison in STFs between a conventional system and a multiple aperture imager. The vertical line at 0.2 depicts the aliasing limits for each sampling strategy.

Fig. 2
Fig. 2

Ratio of the Wiener filter error for the multiple and single aperture systems of Fig. 1 across the nonaliased spatial bandwidth for various alias signal strengths.

Fig. 3
Fig. 3

Designed optical layout of a single lenslet.

Fig. 4
Fig. 4

Polychromatic square wave modulation transfer function performance of each lens in the multichannel lenslet array as designed.

Fig. 5
Fig. 5

Front and back (inset) surfaces of the diamond-turned germanium element.

Fig. 6
Fig. 6

Comparison of three different reconstruction approaches from the same raw data. The top image uses the LG approach detailed in this paper and shows the most contrast. The middle and bottom image were formed using traditional linear and bicubic interpolation methods, respectively.

Fig. 7
Fig. 7

Side by side comparison between conventional and multichannel cameras. The person is at a distance of 3 meters; the hand is at approximately 0.7 meters. Both objects appear in focus with the CIRC as opposed to the conventional system due to the multichannel camera’s increased depth of field. The images were taken simultaneously, so some parallax is visible.

Fig. 8
Fig. 8

Side by side comparison between conventional and multichannel cameras. Target distance is approximately 42 m .

Fig. 9
Fig. 9

Top view of the experimental setup used for NETD and spatial frequency response measurements. A blackbody source illuminates a copper target that is then collimated. The camera under test is positioned in front of the output aperture of the projector.

Fig. 10
Fig. 10

SNR comparison as a function of target temperature difference. The circles and plus signs correspond to the conventional and multichannel data points, respectively.

Fig. 11
Fig. 11

Registered responses of a pixel in each aperture for a rotating target scene.

Fig. 12
Fig. 12

Data (a) and corresponding interpolation (b) for a 4   bar target with spatial frequency of 0.120   cycles / mrad . The raw samples are from one channel of the multiaperture camera. The bar target frequency is approximately equal to the critical frequency. The interpolation is performed by combining data from three subapertures to improve contrast in the horizontal dimension.

Fig. 13
Fig. 13

Data (a) and corresponding interpolation (b) for a 4   bar target with spatial frequency of 0.192   cycles / mrad . The raw samples are from one channel of the multiaperture camera. Aliased data are measured because the bar target frequency exceeds the critical frequency. The three-channel interpolation demonstrates resolution improvement in the horizontal dimension.

Fig. 14
Fig. 14

Cross sectional intensity plot from the fine 4   bar target reconstruction shown in Fig. 13. The solid line shows interpolated result. Data points from one channel are indicated by circles. Data from the conventional system (dotted line) show that the target frequency approaches the aliasing limit.

Tables (1)

Tables Icon

Table 1 Experimentally Calculated Contrast, V, for 4 bar Targets at Five Spatial Frequencies

Equations (18)

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

g n m k = f ( x , y ) h k ( x x , y y ) p k ( x n Δ , y m Δ ) d x d y d x d y = f ( x , y ) t ( x n Δ , y m Δ ) d x d y ,
t ( x , y ) = h k ( x , y ) p ( x x , y y ) d x d y
| t ^ ( u , v ) | = | h ^ ( u , v ) p ^ ( u , v ) | .
ϵ ( u , v ) = S f ( u , v ) 1 + | STF ( u , v ) | 2 S f ( u , v ) S n ( u , v ) + | STF a ( u , v ) | 2 S a ( u , v ) ,
M = Ω V S δ θ 2 ,
M MA M SA 1 n 3 ( 1 + σ 2 ) 2 ,
M MA M SA 1 n ( 1 + σ 2 ) .
H k f = g k , k = 1 , 2 , , 9 ,
H k = ( D 2 , k B 2 , k S 2 , k ) ( D 1 , k B 1 , k S 1 , k ) ,
f LG = arg min f f 2 s . t . H k f = g k , k = 1 , 2 , , 9 ,
f LG = arg min d N ( f p d ) 2 ,
H k f p = g k , k = 1 , 2 , , 9
f LG = f p N ( N T T N ) 1 ( N ) T f p .
f k , LG = arg min f k f k 2 , s . t . H k f k = g k ,
NETD = Δ T SNR = ( T H T B ) mean ( data | T H ) mean ( data | T B ) std ( data | T B ) .
f ( x ) = n = f ( n 2 B ) sinc ( 2 B x n ) .
f ( x ) = k = 1 K n = N / 2 N / 2 m k [ n ] sinc ( 2 B x n + δ k ) .
V = I ¯ max I ¯ min I ¯ max + I ¯ min ,

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