Abstract

The angular resolution of many commercial imaging systems is limited, not by diffraction or optical aberrations, but by pixilation effects. Multiaperture imaging has previously demonstrated the potential for super-resolution (SR) imaging using a lenslet array and single detector array. We describe the practical demonstration of SR imaging using an array of 25 independent commercial-off-the-shelf cameras. This technique demonstrates the potential for increasing the angular resolution toward the diffraction limit, but without the limit on angular resolution imposed by the use of a single detector array.

© 2014 Optical Society of America

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References

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2012

2009

2008

2007

2006

2004

D. G. Lowe, Int. J. Comput. Vis. 60, 91 (2004).
[CrossRef]

2003

S. C. Park, M. K. Park, and M. G. Kang, IEEE Signal Process. Mag. 20(3), 21 (2003).
[CrossRef]

2001

1997

M. Elad and A. Feuer, IEEE Trans. Image Process. 6, 1646 (1997).
[CrossRef]

1981

M. A. Fisher and R. C. Bolles, Commun. ACM 24, 381 (1981).
[CrossRef]

Ackerman, J.

Bolles, R. C.

M. A. Fisher and R. C. Bolles, Commun. ACM 24, 381 (1981).
[CrossRef]

Brady, D.

Carriere, J.

Chen, C.

Choi, K.

Dillon, T.

Downing, J.

Driggers, R. G.

Elad, M.

M. Elad and A. Feuer, IEEE Trans. Image Process. 6, 1646 (1997).
[CrossRef]

Feuer, A.

M. Elad and A. Feuer, IEEE Trans. Image Process. 6, 1646 (1997).
[CrossRef]

Findlay, E.

Fisher, M. A.

M. A. Fisher and R. C. Bolles, Commun. ACM 24, 381 (1981).
[CrossRef]

Fleet, E.

Gibbons, R.

Hartley, R. I.

R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd edition (Cambridge University, 2004).

Harvey, A. R.

Ichioka, Y.

Ishida, K.

Kanaev, A.

Kang, M. G.

S. C. Park, M. K. Park, and M. G. Kang, IEEE Signal Process. Mag. 20(3), 21 (2003).
[CrossRef]

Kondou, N.

Kumagai, T.

Li, L.

Lowe, D. G.

D. G. Lowe, Int. J. Comput. Vis. 60, 91 (2004).
[CrossRef]

Miyatake, S.

Miyazaki, D.

Morimoto, T.

Muyo, G.

Park, M. K.

S. C. Park, M. K. Park, and M. G. Kang, IEEE Signal Process. Mag. 20(3), 21 (2003).
[CrossRef]

Park, S. C.

S. C. Park, M. K. Park, and M. G. Kang, IEEE Signal Process. Mag. 20(3), 21 (2003).
[CrossRef]

Pitsianis, N.

Portnoy, A.

Prather, D.

Schulz, T.

Scribner, D.

Shankar, M.

Silver, A.

Sun, X.

Tanida, J.

Te Kolste, R.

Willett, R.

Yamada, K.

Yi, A.

Young, S. S.

Zisserman, A.

R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd edition (Cambridge University, 2004).

Appl. Opt.

Commun. ACM

M. A. Fisher and R. C. Bolles, Commun. ACM 24, 381 (1981).
[CrossRef]

IEEE Signal Process. Mag.

S. C. Park, M. K. Park, and M. G. Kang, IEEE Signal Process. Mag. 20(3), 21 (2003).
[CrossRef]

IEEE Trans. Image Process.

M. Elad and A. Feuer, IEEE Trans. Image Process. 6, 1646 (1997).
[CrossRef]

Int. J. Comput. Vis.

D. G. Lowe, Int. J. Comput. Vis. 60, 91 (2004).
[CrossRef]

J. Opt. A

G. Muyo and A. R. Harvey, J. Opt. A 11, 54002 (2009).

J. Opt. Soc. Am. A

Other

R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd edition (Cambridge University, 2004).

International Standard ISO 12233:2000(E).

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Figures (6)

Fig. 1.
Fig. 1.

Assembled device comprising a 5×5 camera array.

Fig. 2.
Fig. 2.

SIFT point detections from two green-channel images. Red points are discarded because their SIFT descriptor was not matched, blue points are discarded by RANSAC deemed to be outliers, green points are the matched points used to calculate the geometrical transformation.

Fig. 3.
Fig. 3.

From top to bottom: image of USAF target; portion of ISO12233 chart with edges; outdoor color scene; and detail of its central region. Images in the left are from a single camera and images in the right are super-resolved.

Fig. 4.
Fig. 4.

Metric of aliasing against number of cameras, m, used in reconstruction. Mean and standard deviation (in red) for 100 randomly selected camera combinations are plotted. The blue point indicates bi-cubic interpolation. Reconstructed images corresponding to encircled points are shown in Fig. 5.

Fig. 5.
Fig. 5.

Reconstructed images and their pseudo-colored error map compared to the 25 cameras reconstruction. Each column shows worst-case (upper two) and best-case (lower two) images out of 100 cases for 1, 4, 9, 16 and 23 cameras.

Fig. 6.
Fig. 6.

SFR for low-resolution and super-resolved images.

Equations (5)

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

[uvw]T=T[xy1]T,
yi=DWix+ei,
M=[DW1DW2DW25],
y=Mx+e,
xn+1=diag(xn)MT(diag(Mxn))1y,

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