Abstract

A high-resolution image reconstruction algorithm previously used to improve undersampled infrared airborne imagery was applied to two different sets of digital microscopy images. One set is that of medical pap smear images, and the second set contains metallurgical micrographs. Both the pap smear images and the metallurgical micrographs are undersampled, thus causing loss of detail and aliasing artifacts. The algorithm minimizes the effects of aliasing and restores detail unobtainable through simple interpolation techniques. Both applications demonstrate improvement by use of the image reconstruction algorithm.

© 1999 Optical Society of America

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References

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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
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1998

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

1997

A. J. Patti, M. I. Sezan, A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).
[CrossRef] [PubMed]

R. C. Hardie, K. J. Barnard, E. E. Armstrong, “Joint map registration and high resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1633 (1997).
[CrossRef]

1996

R. R. Schultz, R. L. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Process. 5, 996–1011 (1996).
[CrossRef] [PubMed]

1991

M. Irani, S. Peleg, “Improving resolution by image registration,” CVGIP: Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

1989

Armstrong, E. E.

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

R. C. Hardie, K. J. Barnard, E. E. Armstrong, “Joint map registration and high resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1633 (1997).
[CrossRef]

Barnard, K. J.

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

R. C. Hardie, K. J. Barnard, E. E. Armstrong, “Joint map registration and high resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1633 (1997).
[CrossRef]

Bognar, J. G.

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

Cheeseman, P.

P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, R. Hanson, “Super-resolved surface reconstruction from multiple images,” (NASA Ames Research Center, Moffett Field, Calif., 1994).

Hanson, R.

P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, R. Hanson, “Super-resolved surface reconstruction from multiple images,” (NASA Ames Research Center, Moffett Field, Calif., 1994).

Hardie, R. C.

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

R. C. Hardie, K. J. Barnard, E. E. Armstrong, “Joint map registration and high resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1633 (1997).
[CrossRef]

Irani, M.

M. Irani, S. Peleg, “Improving resolution by image registration,” CVGIP: Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

Jain, A. K.

A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, N.J., 1989).

Kanefsky, B.

P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, R. Hanson, “Super-resolved surface reconstruction from multiple images,” (NASA Ames Research Center, Moffett Field, Calif., 1994).

Kraft, R.

P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, R. Hanson, “Super-resolved surface reconstruction from multiple images,” (NASA Ames Research Center, Moffett Field, Calif., 1994).

Luenberger, D. G.

D. G. Luenberger, Linear and Nonlinear Programming (Addison-Wesley, Reading, Mass., 1984).

Oskoui, P.

Patti, A. J.

A. J. Patti, M. I. Sezan, A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).
[CrossRef] [PubMed]

Peleg, S.

M. Irani, S. Peleg, “Improving resolution by image registration,” CVGIP: Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

Schultz, R. R.

R. R. Schultz, R. L. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Process. 5, 996–1011 (1996).
[CrossRef] [PubMed]

Sezan, M. I.

A. J. Patti, M. I. Sezan, A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).
[CrossRef] [PubMed]

Stark, H.

Stevenson, R. L.

R. R. Schultz, R. L. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Process. 5, 996–1011 (1996).
[CrossRef] [PubMed]

Stutz, J.

P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, R. Hanson, “Super-resolved surface reconstruction from multiple images,” (NASA Ames Research Center, Moffett Field, Calif., 1994).

Tekalp, A. M.

A. J. Patti, M. I. Sezan, A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).
[CrossRef] [PubMed]

Watson, E. A.

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

CVGIP: Graph. Models Image Process.

M. Irani, S. Peleg, “Improving resolution by image registration,” CVGIP: Graph. Models Image Process. 53, 231–239 (1991).
[CrossRef]

IEEE Trans. Image Process

R. C. Hardie, K. J. Barnard, E. E. Armstrong, “Joint map registration and high resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621–1633 (1997).
[CrossRef]

IEEE Trans. Image Process.

A. J. Patti, M. I. Sezan, A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).
[CrossRef] [PubMed]

R. R. Schultz, R. L. Stevenson, “Extraction of high-resolution frames from video sequences,” IEEE Trans. Image Process. 5, 996–1011 (1996).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A

Opt. Eng.

R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, “High resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. 37, 247–260 (1998).
[CrossRef]

Other

D. G. Luenberger, Linear and Nonlinear Programming (Addison-Wesley, Reading, Mass., 1984).

A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, N.J., 1989).

P. Cheeseman, B. Kanefsky, R. Kraft, J. Stutz, R. Hanson, “Super-resolved surface reconstruction from multiple images,” (NASA Ames Research Center, Moffett Field, Calif., 1994).

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Figures (7)

Fig. 1
Fig. 1

Low-resolution image formation model: (a) original undegraded high-resolution image, (b) image with PSF applied (optical blurring), (c) downsampled image shifted by 3 pixels, (d) sampled image with noise added.

Fig. 2
Fig. 2

Medical slide images: (a) full field of view, (b) selected ROI for processing (observed frame 1), (c) bilinear interpolation of frame 1 ROI, (d) multiframe reconstruction of frame 1 ROI.

Fig. 3
Fig. 3

Cost as a function of iteration for the medical slide data.

Fig. 4
Fig. 4

Estimated subpixel frame shifts for the medical slide data.

Fig. 5
Fig. 5

Metallurgical images: (a) full field of view (frame 1), (b) selected region (ROI) for processing (observed frame), (c) bilinear interpolation of frame 1 ROI, (d) multiframe reconstruction of frame 1 ROI.

Fig. 6
Fig. 6

Cost as a function of iteration for the metallurgical image data.

Fig. 7
Fig. 7

Estimated subpixel frame shifts for the metallurgical image data.

Equations (11)

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

ρsampling=4NAMλ0n2-NA21/2.
y=y1T,y2T,,ypTT=y1,y2,,ypMT.
ym=r=1N wm,rzr+ηm
zˆ=zarg minCz,
Cz=12m=1pMym-r=1N wm,rzr2+λ2i=1Nj=1N αi,jzj2
αi,j=1for i=j-1/4for j:zj is a cardinal neighbor of zi.
gkz=Czzk=m=1pM wm,kr=1N wm,rzr-ym+λ i=1N αi,kj=1N αi,jzj.
zˆkn+1=zˆkn-ngkzˆn
n=m=1pM γmr=1N wm,rzˆrn-ym+λ i=1N g¯ij=1N αi,jzˆjnm=1pM γm2+λ i=1N g¯i2,
γm=r=1N wm,rgrzˆn
g¯i=j=1N αi,jgjzˆn

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