Abstract

This paper presents a method to simultaneously acquire an aberration-corrected, wide field-of-view fluorescence image and a high-resolution coherent bright-field image using a computational microscopy method. First, the procedure applies Fourier ptychographic microscopy (FPM) to retrieve the amplitude and phase of a sample, at a resolution that significantly exceeds the cutoff spatial frequency of the microscope objective lens. At the same time, redundancy within the set of acquired FPM bright-field images offers a means to estimate microscope aberrations. Second, the procedure acquires an aberrated fluorescence image, and computationally improves its resolution through deconvolution with the estimated aberration map. An experimental demonstration successfully improves the bright-field resolution of fixed, stained and fluorescently tagged HeLa cells by a factor of 4.9, and reduces the error caused by aberrations in a fluorescence image by up to 31%, over a field of view of 6.2 mm by 9.3 mm. For optimal deconvolution, we show the fluorescence image needs to have a signal-to-noise ratio of at least ~18.

© 2016 Optical Society of America

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References

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2015 (5)

2014 (7)

2013 (3)

2012 (1)

2011 (3)

Y. Shechtman, Y. C. Eldar, A. Szameit, and M. Segev, “Sparsity based sub-wavelength imaging with partially incoherent light via quadratic compressed sensing,” Opt. Express 19(16), 14807–14822 (2011).
[Crossref] [PubMed]

T. Schroeder, “Long-term single-cell imaging of mammalian stem cells,” Nat. Methods 8(4S), S30–S35 (2011).
[Crossref] [PubMed]

J.-H. Lee and Y.-S. Ho, “High-quality non-blind image deconvolution with adaptive regularization,” J. Vis. Commun. Image Represent. 22(7), 653–663 (2011).
[Crossref]

2009 (2)

P. Thibault, M. Dierolf, O. Bunk, A. Menzel, and F. Pfeiffer, “Probe retrieval in ptychographic coherent diffractive imaging,” Ultramicroscopy 109(4), 338–343 (2009).
[Crossref] [PubMed]

A. M. Maiden and J. M. Rodenburg, “An improved ptychographical phase retrieval algorithm for diffractive imaging,” Ultramicroscopy 109(10), 1256–1262 (2009).
[Crossref] [PubMed]

2008 (4)

L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Progressive inter-scale and intra-scale non-blind image deconvolution,” ACM Trans. Graph. 27(3), 1–10 (2008).
[Crossref]

M. Guizar-Sicairos and J. R. Fienup, “Phase retrieval with transverse translation diversity: a nonlinear optimization approach,” Opt. Express 16(10), 7264–7278 (2008).
[Crossref] [PubMed]

P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, and F. Pfeiffer, “High-Resolution Scanning X-Ray Diffraction Microscopy,” Science 321(5887), 379–382 (2008).
[Crossref] [PubMed]

A. R. Kherlopian, T. Song, Q. Duan, M. A. Neimark, M. J. Po, J. K. Gohagan, and A. F. Laine, “A review of imaging techniques for systems biology,” BMC Syst. Biol. 2(1), 74 (2008).
[Crossref] [PubMed]

2007 (2)

R. Heintzmann, “Estimating missing information by maximum likelihood deconvolution,” Micron 38(2), 136–144 (2007).
[Crossref] [PubMed]

C.-C. Liang, A. Y. Park, and J.-L. Guan, “In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro,” Nat. Protoc. 2(2), 329–333 (2007).
[Crossref] [PubMed]

2006 (2)

G. R. Brady and J. R. Fienup, “Nonlinear optimization algorithm for retrieving the full complex pupil function,” Opt. Express 14(2), 474–486 (2006).
[Crossref] [PubMed]

P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Process. Mag. 23(3), 32–45 (2006).
[Crossref]

2005 (1)

J. W. Lichtman and J.-A. Conchello, “Fluorescence microscopy,” Nat. Methods 2(12), 910–919 (2005).
[Crossref] [PubMed]

2004 (1)

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase-retrieved pupil functions in wide-field fluorescence microscopy,” J. Microsc. 216(1), 32–48 (2004).
[Crossref] [PubMed]

2003 (3)

2002 (1)

1997 (3)

1996 (2)

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Process. Mag. 13(3), 43–64 (1996).
[Crossref]

L. G. Leff and A. A. Leff, “Use of green fluorescent protein to monitor survival of genetically engineered bacteria in aquatic environments,” Appl. Environ. Microbiol. 62(9), 3486–3488 (1996).
[PubMed]

1994 (1)

G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16(3), 267–276 (1994).
[Crossref]

1993 (1)

1982 (1)

1980 (1)

K. G. Porter and Y. S. Feig, “The use of DAPI for identifying and counting aquatic microflora1,” Limnol. Oceanogr. 25(5), 943–948 (1980).
[Crossref]

1979 (1)

1976 (1)

1964 (1)

1961 (1)

R. P. Perry, “Kinetics of nucleoside incorporation into nuclear and cytoplasmic RNA,” J. Biophys. Biochem. Cytol. 11(1), 1–13 (1961).
[Crossref] [PubMed]

Agard, D. A.

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase-retrieved pupil functions in wide-field fluorescence microscopy,” J. Microsc. 216(1), 32–48 (2004).
[Crossref] [PubMed]

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase retrieval for high-numerical-aperture optical systems,” Opt. Lett. 28(10), 801–803 (2003).
[Crossref] [PubMed]

Allain, M.

Allan, V. J.

D. J. Stephens and V. J. Allan, “Light microscopy techniques for live cell imaging,” Science 300(5616), 82–86 (2003).
[Crossref] [PubMed]

Ames, B.

R. Horstmeyer, R. Y. Chen, X. Ou, B. Ames, J. A. Tropp, and C. Yang, “Solving ptychography with a convex relaxation,” New J. Phys. 17(5), 053044 (2015).
[Crossref] [PubMed]

Ao, Z.

A. Williams, J. Chung, X. Ou, G. Zheng, S. Rawal, Z. Ao, R. Datar, C. Yang, and R. Cote, “Fourier ptychographic microscopy for filtration-based circulating tumor cell enumeration and analysis,” J. Biomed. Opt. 19(6), 066007 (2014).
[Crossref] [PubMed]

Bauschke, H. H.

Brady, G. R.

Bunk, O.

P. Thibault, M. Dierolf, O. Bunk, A. Menzel, and F. Pfeiffer, “Probe retrieval in ptychographic coherent diffractive imaging,” Ultramicroscopy 109(4), 338–343 (2009).
[Crossref] [PubMed]

P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, and F. Pfeiffer, “High-Resolution Scanning X-Ray Diffraction Microscopy,” Science 321(5887), 379–382 (2008).
[Crossref] [PubMed]

Chamard, V.

Chen, M.

Chen, R. Y.

R. Horstmeyer, R. Y. Chen, X. Ou, B. Ames, J. A. Tropp, and C. Yang, “Solving ptychography with a convex relaxation,” New J. Phys. 17(5), 053044 (2015).
[Crossref] [PubMed]

Chung, J.

A. Williams, J. Chung, X. Ou, G. Zheng, S. Rawal, Z. Ao, R. Datar, C. Yang, and R. Cote, “Fourier ptychographic microscopy for filtration-based circulating tumor cell enumeration and analysis,” J. Biomed. Opt. 19(6), 066007 (2014).
[Crossref] [PubMed]

Combettes, P. L.

Conchello, J.-A.

J. W. Lichtman and J.-A. Conchello, “Fluorescence microscopy,” Nat. Methods 2(12), 910–919 (2005).
[Crossref] [PubMed]

Cote, R.

A. Williams, J. Chung, X. Ou, G. Zheng, S. Rawal, Z. Ao, R. Datar, C. Yang, and R. Cote, “Fourier ptychographic microscopy for filtration-based circulating tumor cell enumeration and analysis,” J. Biomed. Opt. 19(6), 066007 (2014).
[Crossref] [PubMed]

Datar, R.

A. Williams, J. Chung, X. Ou, G. Zheng, S. Rawal, Z. Ao, R. Datar, C. Yang, and R. Cote, “Fourier ptychographic microscopy for filtration-based circulating tumor cell enumeration and analysis,” J. Biomed. Opt. 19(6), 066007 (2014).
[Crossref] [PubMed]

David, C.

P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, and F. Pfeiffer, “High-Resolution Scanning X-Ray Diffraction Microscopy,” Science 321(5887), 379–382 (2008).
[Crossref] [PubMed]

den Dekker, A. J.

Dierolf, M.

P. Thibault, M. Dierolf, O. Bunk, A. Menzel, and F. Pfeiffer, “Probe retrieval in ptychographic coherent diffractive imaging,” Ultramicroscopy 109(4), 338–343 (2009).
[Crossref] [PubMed]

P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, and F. Pfeiffer, “High-Resolution Scanning X-Ray Diffraction Microscopy,” Science 321(5887), 379–382 (2008).
[Crossref] [PubMed]

Dong, S.

Duan, Q.

A. R. Kherlopian, T. Song, Q. Duan, M. A. Neimark, M. J. Po, J. K. Gohagan, and A. F. Laine, “A review of imaging techniques for systems biology,” BMC Syst. Biol. 2(1), 74 (2008).
[Crossref] [PubMed]

Eldar, Y. C.

Elser, V.

Feig, Y. S.

K. G. Porter and Y. S. Feig, “The use of DAPI for identifying and counting aquatic microflora1,” Limnol. Oceanogr. 25(5), 943–948 (1980).
[Crossref]

Fienup, J. R.

Godard, P.

Gohagan, J. K.

A. R. Kherlopian, T. Song, Q. Duan, M. A. Neimark, M. J. Po, J. K. Gohagan, and A. F. Laine, “A review of imaging techniques for systems biology,” BMC Syst. Biol. 2(1), 74 (2008).
[Crossref] [PubMed]

Guan, J.-L.

C.-C. Liang, A. Y. Park, and J.-L. Guan, “In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro,” Nat. Protoc. 2(2), 329–333 (2007).
[Crossref] [PubMed]

Guizar-Sicairos, M.

Guo, K.

Gustafsson, M. G.

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase-retrieved pupil functions in wide-field fluorescence microscopy,” J. Microsc. 216(1), 32–48 (2004).
[Crossref] [PubMed]

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase retrieval for high-numerical-aperture optical systems,” Opt. Lett. 28(10), 801–803 (2003).
[Crossref] [PubMed]

Hanser, B. M.

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase-retrieved pupil functions in wide-field fluorescence microscopy,” J. Microsc. 216(1), 32–48 (2004).
[Crossref] [PubMed]

B. M. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, “Phase retrieval for high-numerical-aperture optical systems,” Opt. Lett. 28(10), 801–803 (2003).
[Crossref] [PubMed]

Harris, J. L.

Hatzinakos, D.

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Process. Mag. 13(3), 43–64 (1996).
[Crossref]

Healey, G. E.

G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16(3), 267–276 (1994).
[Crossref]

Heintzmann, R.

R. Heintzmann, “Estimating missing information by maximum likelihood deconvolution,” Micron 38(2), 136–144 (2007).
[Crossref] [PubMed]

Ho, Y.-S.

J.-H. Lee and Y.-S. Ho, “High-quality non-blind image deconvolution with adaptive regularization,” J. Vis. Commun. Image Represent. 22(7), 653–663 (2011).
[Crossref]

Horstmeyer, R.

Hunt, B. R.

Kempen, G. M. P.

G. M. P. Kempen and V. L. J. Vliet, “A quantitative comparison of image restoration methods for confocal microscopy,” J. Microsc. 185(3), 354–365 (1997).
[Crossref]

Kherlopian, A. R.

A. R. Kherlopian, T. Song, Q. Duan, M. A. Neimark, M. J. Po, J. K. Gohagan, and A. F. Laine, “A review of imaging techniques for systems biology,” BMC Syst. Biol. 2(1), 74 (2008).
[Crossref] [PubMed]

Kondepudy, R.

G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16(3), 267–276 (1994).
[Crossref]

Kundur, D.

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Process. Mag. 13(3), 43–64 (1996).
[Crossref]

Laine, A. F.

A. R. Kherlopian, T. Song, Q. Duan, M. A. Neimark, M. J. Po, J. K. Gohagan, and A. F. Laine, “A review of imaging techniques for systems biology,” BMC Syst. Biol. 2(1), 74 (2008).
[Crossref] [PubMed]

Lee, J.-H.

J.-H. Lee and Y.-S. Ho, “High-quality non-blind image deconvolution with adaptive regularization,” J. Vis. Commun. Image Represent. 22(7), 653–663 (2011).
[Crossref]

Leff, A. A.

L. G. Leff and A. A. Leff, “Use of green fluorescent protein to monitor survival of genetically engineered bacteria in aquatic environments,” Appl. Environ. Microbiol. 62(9), 3486–3488 (1996).
[PubMed]

Leff, L. G.

L. G. Leff and A. A. Leff, “Use of green fluorescent protein to monitor survival of genetically engineered bacteria in aquatic environments,” Appl. Environ. Microbiol. 62(9), 3486–3488 (1996).
[PubMed]

Li, X.

Liang, C.-C.

C.-C. Liang, A. Y. Park, and J.-L. Guan, “In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro,” Nat. Protoc. 2(2), 329–333 (2007).
[Crossref] [PubMed]

Lichtman, J. W.

J. W. Lichtman and J.-A. Conchello, “Fluorescence microscopy,” Nat. Methods 2(12), 910–919 (2005).
[Crossref] [PubMed]

Liu, Z.

Luke, D. R.

Maiden, A. M.

A. M. Maiden and J. M. Rodenburg, “An improved ptychographical phase retrieval algorithm for diffractive imaging,” Ultramicroscopy 109(10), 1256–1262 (2009).
[Crossref] [PubMed]

Menzel, A.

P. Thibault, M. Dierolf, O. Bunk, A. Menzel, and F. Pfeiffer, “Probe retrieval in ptychographic coherent diffractive imaging,” Ultramicroscopy 109(4), 338–343 (2009).
[Crossref] [PubMed]

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[Crossref]

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[Crossref] [PubMed]

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Park, A. Y.

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[Crossref] [PubMed]

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Pfeiffer, F.

P. Thibault, M. Dierolf, O. Bunk, A. Menzel, and F. Pfeiffer, “Probe retrieval in ptychographic coherent diffractive imaging,” Ultramicroscopy 109(4), 338–343 (2009).
[Crossref] [PubMed]

P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, and F. Pfeiffer, “High-Resolution Scanning X-Ray Diffraction Microscopy,” Science 321(5887), 379–382 (2008).
[Crossref] [PubMed]

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Rodenburg, J.

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[Crossref] [PubMed]

Yang, C.

R. Horstmeyer, R. Y. Chen, X. Ou, B. Ames, J. A. Tropp, and C. Yang, “Solving ptychography with a convex relaxation,” New J. Phys. 17(5), 053044 (2015).
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A. Williams, J. Chung, X. Ou, G. Zheng, S. Rawal, Z. Ao, R. Datar, C. Yang, and R. Cote, “Fourier ptychographic microscopy for filtration-based circulating tumor cell enumeration and analysis,” J. Biomed. Opt. 19(6), 066007 (2014).
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Yeh, L.-H.

Yuan, L.

L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Progressive inter-scale and intra-scale non-blind image deconvolution,” ACM Trans. Graph. 27(3), 1–10 (2008).
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K. Guo, S. Dong, P. Nanda, and G. Zheng, “Optimization of sampling pattern and the design of Fourier ptychographic illuminator,” Opt. Express 23(5), 6171–6180 (2015).
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X. Ou, R. Horstmeyer, G. Zheng, and C. Yang, “High numerical aperture Fourier ptychography: principle, implementation and characterization,” Opt. Express 23(3), 3472–3491 (2015).
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G. M. P. Kempen and V. L. J. Vliet, “A quantitative comparison of image restoration methods for confocal microscopy,” J. Microsc. 185(3), 354–365 (1997).
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R. Horstmeyer, R. Y. Chen, X. Ou, B. Ames, J. A. Tropp, and C. Yang, “Solving ptychography with a convex relaxation,” New J. Phys. 17(5), 053044 (2015).
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P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, and F. Pfeiffer, “High-Resolution Scanning X-Ray Diffraction Microscopy,” Science 321(5887), 379–382 (2008).
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P. Thibault, M. Dierolf, O. Bunk, A. Menzel, and F. Pfeiffer, “Probe retrieval in ptychographic coherent diffractive imaging,” Ultramicroscopy 109(4), 338–343 (2009).
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Figures (7)

Fig. 1
Fig. 1

The experimental setup of joint FPM-fluorescence imaging. The 4f setup has a camera lens, an iris at the back focal plane, and a tube lens. The filter is included only for fluorescence imaging and FPM imaging of the same color channel. First, raw FPM images are captured using varied-angle illumination provided by the LED matrix. Then, a fluorescence image is captured with the illumination from the excitation LED.

Fig. 2
Fig. 2

Experimental reconstruction of an improved-resolution amplitude and phase image, along with an aberration-corrected fluorescence image, using FPM. (a) Series of low-resolution coherent green images captured with variable-angle illumination from an LED matrix. (b) EPRY is applied on the low-resolution images to generate a high-resolution, complex image of the sample, along with the characterization of the microscope’s pupil function. (c) The pupil function is converted into an incoherent PSF, which is the blur kernel induced to a fluorescence image by the imaging system in the same sample region. (d) The PSF is deconvolved from the fluorescence image using Eq. (6) to generate an aberration-corrected image. (e) The one-dimensional profile of two beads, in the raw and deconvolved images, shows improved contrast and position accuracy. (f) In the overlay of FPM and fluorescence images, the fluorescence signal is localized with good centroid accuracy after deconvolution.

Fig. 3
Fig. 3

Comparison between (a) a raw image of a specific sample ROI shifted 4.63 mm off the optical axis (left), its deconvolution result (right), and (b) a raw image captured with the same sample ROI centered on the optical axis, im(x,y). The PSF recovered from FPM, hm(x,y), is deconvolved from the image to remove aberrations in (a). The result shows close resemblance to the image in (b), icenter(x,y), which we assume is minimally impacted by system aberrations.

Fig. 4
Fig. 4

Demonstration of FPM and aberration-corrected fluorescence imaging across different regions of a large image FOV. (a)-(c) correspond to regions labeled in the full FOV fluorescence image in (d). With FPM, we improve bright-field image resolution (1st and 2nd column) and characterize the spatially varying pupil functions (6th column). Hot pixels and chromatic aberrations appearing in the low-resolution color images are suppressed and corrected after FPM reconstruction. We correct aberrations in the fluorescence image using each pupil function (3rd and 4th column). The phase gradient images from FPM can be combined with fluorescence data to elucidate the structures and the locations of the nuclei of HeLa cells (5th column). The cell membrane morphology elucidated by the phase gradient can differentiate between cells undergoing cytokinesis (arrow in (b)) and cells in telophase (arrow in (c)), which is otherwise difficult to do in the fluorescence images alone.

Fig. 5
Fig. 5

(a) Normalized mean square error (NMSE) of the raw, i m (u;t) , and deconvolved image, i ˜ m (u;t) , in Fig. 3(a) is plotted against the detector exposure time. Higher exposure is equivalent to higher SNR of the captured image. NMSE starts to plateau for both raw and deconvolved images after about 21 min of exposure (raw image SNR = 18.1) as indicated by the green broken line, with the deconvolved image’s NMSE = 0.0057 being 31% lower than the raw image’s NMSE = 0.0083. (b) Example images of the ROI used for this study. The reference image is generated by centering the ROI and capturing with our imaging system. We use the area inside the small yellow box to quantify the SNR. Raw images are captured with varying exposure time (45 sec and 21 min shown here) while the ROI is 4.63 mm away from the center. We use the same small area in each raw image to quantify its SNR. Deconvolved images are generated by applying Eq. (6) to the raw images. Longer exposure provides better SNR images and deconvolution results.

Fig. 6
Fig. 6

The variations in experimental setup for comparing the two different pupil function recover methods. (a) Collimated light source illuminates the sample perpendicularly, and the sample is brought to different defocus planes in the defocus diversity-based pupil recovery method. (b) LEDs of varied illumination angles act as the light source in FPM setup. EPRY algorithm jointly solves for the sample spectrum and the pupil function using sample images captured under different LED illumination angles.

Fig. 7
Fig. 7

Full FOV image of a sample of microspheres with associated pupil functions in 3 different sub-regions. The red box indicates the pupil function recovered by EPRY while the blue box stands for defocus diversity-based pupil function recovery method. The two pupil estimation methods correlate well throughout different regions of the FOV.

Equations (12)

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

h m (x,y)= | 1 [ P m ( f x , f y ) ] | 2 ,
i m (x,y)= h m (x,y) o m (x,y)+ n m (x,y).
I m ( f x , f y )= H m ( f x , f y ) O m ( f x , f y )+ N m ( f x , f y ),
O ˜ m ( f x , f y )= G m ( f x , f y ) I m ( f x , f y ),
G m ( f x , f y )= H m * ( f x , f y ) | H m ( f x , f y ) | 2 + | N m ( f x , f y ) | 2 | O m ( f x , f y ) | 2 .
o ˜ m (x,y)= 1 [ H m * ( f x , f y ) | H m ( f x , f y ) | 2 +K I m ( f x , f y ) ].
SNR= S σ s .
NMSE(t)= u | i center (u) α t i ˜ m (u;t) | 2 u | i center (u) | 2 .
α t = u i center (u) i ˜ m * (u;t) u | i ˜ m (u;t) | 2 ,
S= nmpq M = npq(1 1 NA 2 ) 2M .
SNR= S S = n pq(1 1 NA 2 ) 2M .
n= SNR 2 2M pq(1 1 NA 2 ) .

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