C. L. Matson, “Fourier spectrum extrapolation and enhancement using support constraints,” IEEE Trans. Acoust. Speech, Signal Process. 42, 156–163 (1994).

[CrossRef]

C. L. Matson, “Variance reduction in Fourier spectra and their corresponding images with the use of support constraints,” J. Opt. Soc. Am. A 11, 97–106 (1994).

[CrossRef]

C. L. Matson, “Error reduction in images using high-quality prior knowledge,” Opt. Eng. 33, 3233–3236 (1994).

[CrossRef]

M. I. Sezan, A. M. Tekalp, “Survey of recent developments in digital image restoration,” Opt. Eng. 29, 393–404 (1990).

[CrossRef]

G. Demoment, “Image reconstruction and restoration: overview of common estimation structures and problems,” IEEE Trans. Acoust. Speech Signal Process. 37, 2024–2036 (1989).

[CrossRef]

E. M. Haacke, Z. Liang, S. H. Izen, “Superresolution reconstruction through object modeling and parameter estimation,” IEEE Trans. Acoust. Speech Signal Process. 37, 592–595 (1989).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. I. Partial deconvolution and spectral extrapolation with limited field,” J. Mod. Opt. 34, 161–226 (1987).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. II. Iterative reconstruction with and without constraint—interactive implementation,” J. Mod. Opt. 34, 321–370 (1987).

[CrossRef]

F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proc. IEEE 66, 51–83 (1978).

[CrossRef]

G. E. B. Archer, D. M. Titterington, “The iterative image space reconstruction algorithm (ISRA) as an alternative to the EM algorithm for solving positive linear inverse problems,” Stat. Sin. 5, 77–96 (1995).

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. II. Iterative reconstruction with and without constraint—interactive implementation,” J. Mod. Opt. 34, 321–370 (1987).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. I. Partial deconvolution and spectral extrapolation with limited field,” J. Mod. Opt. 34, 161–226 (1987).

[CrossRef]

G. Demoment, “Image reconstruction and restoration: overview of common estimation structures and problems,” IEEE Trans. Acoust. Speech Signal Process. 37, 2024–2036 (1989).

[CrossRef]

J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, San Francisco, Calif., 1968).

E. M. Haacke, Z. Liang, S. H. Izen, “Superresolution reconstruction through object modeling and parameter estimation,” IEEE Trans. Acoust. Speech Signal Process. 37, 592–595 (1989).

[CrossRef]

F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proc. IEEE 66, 51–83 (1978).

[CrossRef]

E. M. Haacke, Z. Liang, S. H. Izen, “Superresolution reconstruction through object modeling and parameter estimation,” IEEE Trans. Acoust. Speech Signal Process. 37, 592–595 (1989).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. I. Partial deconvolution and spectral extrapolation with limited field,” J. Mod. Opt. 34, 161–226 (1987).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. II. Iterative reconstruction with and without constraint—interactive implementation,” J. Mod. Opt. 34, 321–370 (1987).

[CrossRef]

A. E. Taylor, D. C. Lay, Introduction to Functional Analysis, 2nd ed. (Wiley, New York, 1980), p. 348.

Y. Vardi, D. Lee, “From image deblurring to optimal investments: maximum likelihood solutions for positive linear inverse problems,” J. Statist. Soc. B. 55, 569–612 (1993).

E. M. Haacke, Z. Liang, S. H. Izen, “Superresolution reconstruction through object modeling and parameter estimation,” IEEE Trans. Acoust. Speech Signal Process. 37, 592–595 (1989).

[CrossRef]

C. L. Matson, “Fourier spectrum extrapolation and enhancement using support constraints,” IEEE Trans. Acoust. Speech, Signal Process. 42, 156–163 (1994).

[CrossRef]

C. L. Matson, “Variance reduction in Fourier spectra and their corresponding images with the use of support constraints,” J. Opt. Soc. Am. A 11, 97–106 (1994).

[CrossRef]

C. L. Matson, “Error reduction in images using high-quality prior knowledge,” Opt. Eng. 33, 3233–3236 (1994).

[CrossRef]

C. L. Matson, “The role of positivity for error reduction in images,” in Image and Signal Processing for Remote Sensing, J. Desachy, ed., Proc. SPIE2315, 766–777 (1994).

[CrossRef]

T. Suhara, H. Nishihara, “Theoretical analysis of super-resolution readout of disc data by semiconfocal pickup heads,” Jpn. J. Appl. Phys. 31, 534–541 (1992).

[CrossRef]

W. K. Pratt, Digital Image Processing, 2nd ed. (Wiley, New York, 1991), pp. 247–250 and 305.

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. I. Partial deconvolution and spectral extrapolation with limited field,” J. Mod. Opt. 34, 161–226 (1987).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. II. Iterative reconstruction with and without constraint—interactive implementation,” J. Mod. Opt. 34, 321–370 (1987).

[CrossRef]

M. I. Sezan, A. M. Tekalp, “Survey of recent developments in digital image restoration,” Opt. Eng. 29, 393–404 (1990).

[CrossRef]

T. Suhara, H. Nishihara, “Theoretical analysis of super-resolution readout of disc data by semiconfocal pickup heads,” Jpn. J. Appl. Phys. 31, 534–541 (1992).

[CrossRef]

A. E. Taylor, D. C. Lay, Introduction to Functional Analysis, 2nd ed. (Wiley, New York, 1980), p. 348.

M. I. Sezan, A. M. Tekalp, “Survey of recent developments in digital image restoration,” Opt. Eng. 29, 393–404 (1990).

[CrossRef]

G. E. B. Archer, D. M. Titterington, “The iterative image space reconstruction algorithm (ISRA) as an alternative to the EM algorithm for solving positive linear inverse problems,” Stat. Sin. 5, 77–96 (1995).

Y. Vardi, D. Lee, “From image deblurring to optimal investments: maximum likelihood solutions for positive linear inverse problems,” J. Statist. Soc. B. 55, 569–612 (1993).

G. Demoment, “Image reconstruction and restoration: overview of common estimation structures and problems,” IEEE Trans. Acoust. Speech Signal Process. 37, 2024–2036 (1989).

[CrossRef]

E. M. Haacke, Z. Liang, S. H. Izen, “Superresolution reconstruction through object modeling and parameter estimation,” IEEE Trans. Acoust. Speech Signal Process. 37, 592–595 (1989).

[CrossRef]

C. L. Matson, “Fourier spectrum extrapolation and enhancement using support constraints,” IEEE Trans. Acoust. Speech, Signal Process. 42, 156–163 (1994).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. I. Partial deconvolution and spectral extrapolation with limited field,” J. Mod. Opt. 34, 161–226 (1987).

[CrossRef]

A. Lannes, S. Roques, M. J. Casanove, “Stabilized reconstruction in signal and image processing. II. Iterative reconstruction with and without constraint—interactive implementation,” J. Mod. Opt. 34, 321–370 (1987).

[CrossRef]

P. J. Sementilli, B. R. Hunt, M. S. Nadar, “Analysis of the limit to superresolution in incoherent imaging,” J. Opt. Soc. Am. A 10, 2265–2276 (1993).

[CrossRef]

C. L. Matson, “Variance reduction in Fourier spectra and their corresponding images with the use of support constraints,” J. Opt. Soc. Am. A 11, 97–106 (1994).

[CrossRef]

D. L. Fried, “Analysis of the CLEAN algorithm and implications for superresolution,” J. Opt. Soc. Am. A 12, 853–860 (1995).

[CrossRef]

Y. Vardi, D. Lee, “From image deblurring to optimal investments: maximum likelihood solutions for positive linear inverse problems,” J. Statist. Soc. B. 55, 569–612 (1993).

T. Suhara, H. Nishihara, “Theoretical analysis of super-resolution readout of disc data by semiconfocal pickup heads,” Jpn. J. Appl. Phys. 31, 534–541 (1992).

[CrossRef]

M. I. Sezan, A. M. Tekalp, “Survey of recent developments in digital image restoration,” Opt. Eng. 29, 393–404 (1990).

[CrossRef]

C. L. Matson, “Error reduction in images using high-quality prior knowledge,” Opt. Eng. 33, 3233–3236 (1994).

[CrossRef]

F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proc. IEEE 66, 51–83 (1978).

[CrossRef]

G. E. B. Archer, D. M. Titterington, “The iterative image space reconstruction algorithm (ISRA) as an alternative to the EM algorithm for solving positive linear inverse problems,” Stat. Sin. 5, 77–96 (1995).

A. E. Taylor, D. C. Lay, Introduction to Functional Analysis, 2nd ed. (Wiley, New York, 1980), p. 348.

H. Stark, ed., Image Recovery: Theory and Application (Academic, Boston, Mass., 1987).

C. L. Matson, “The role of positivity for error reduction in images,” in Image and Signal Processing for Remote Sensing, J. Desachy, ed., Proc. SPIE2315, 766–777 (1994).

[CrossRef]

J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, San Francisco, Calif., 1968).

W. K. Pratt, Digital Image Processing, 2nd ed. (Wiley, New York, 1991), pp. 247–250 and 305.