Abstract

Digital holography is an emerging imaging technique for displaying and sensing three-dimensional objects. The perceived image quality of a hologram is frequently corrupted by speckle noise due to coherent illumination. Although several speckle noise reduction methods have been developed so far, there are scarce quality assessment studies to address their performance, and they typically focus solely on objective metrics. However, these metrics do not reflect the visual quality perceived by a human observer. In this work, the performances of four speckle reduction algorithms, namely, the nonlocal means–the Lee, the Frost, and the block-matching 3D filters, with varying parameterizations–were subjectively evaluated. The results were ranked with respect to the perceived image quality to obtain the mean opinion scores using pairwise comparison. The correlation between the subjective results and 20 different no-reference objective quality metrics was evaluated. The experiment indicates that block-matching 3D and Lee are the preferred filters, depending on hologram characteristics. The best-performing objective metrics were identified for each filter.

© 2019 Optical Society of America

Full Article  |  PDF Article
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References

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2019 (2)

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

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[Crossref]

2018 (6)

Y. Tounsi, M. Kumar, A. Nassim, and F. Mendoza-Santoyo, “Speckle noise reduction in digital speckle pattern interferometric fringes by nonlocal means and its related adaptive kernel-based methods,” Appl. Opt. 57, 7681–7690 (2018).
[Crossref]

M. V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P. T. Fiadeiro, A. M. Pinheiro, and M. Pereira, “Holographic representation: hologram plane vs. object plane,” Signal Process. Image Commun. 68, 193–206 (2018).
[Crossref]

V. Cazac, A. Meshalkin, E. Achimova, V. Abashkin, V. Katkovnik, I. Shevkunov, D. Claus, and G. Pedrini, “Surface relief and refractive index gratings patterned in chalcogenide glasses and studied by off-axis digital holography,” Appl. Opt. 57, 507–513 (2018).
[Crossref]

C. G. T. Ruiz, H. Manuel, J. Flores-Moreno, C. Frausto-Reyes, and F. M. Santoyo, “Cortical bone quality affectations and their strength impact analysis using holographic interferometry,” Biomed. Opt. Express 9, 4818–4833 (2018).
[Crossref]

M. Kumar, A. S. Birhman, S. Kannan, and C. Shakher, “Measurement of initial displacement of canine and molar in human maxilla under different canine retraction methods using digital holographic interferometry,” Opt. Eng. 57, 094106 (2018).
[Crossref]

V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, and P. Ferraro, “Strategies for reducing speckle noise in digital holography,” Light Sci. Appl. 7, 48 (2018).
[Crossref]

2016 (6)

2015 (1)

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
[Crossref]

2013 (2)

2011 (1)

N. D. Narvekar and L. J. Karam, “A no-reference image blur metric based on the cumulative probability of blur detection (CPBD),” IEEE Trans. Image Process. 20, 2678–2683 (2011).
[Crossref]

2010 (3)

T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Comparing numerical error and visual quality in reconstructions from compressed digital holograms,” Proc. SPIE 7690, 769012 (2010).
[Crossref]

E. Darakis, M. Kowiel, R. Näsänen, and T. J. Naughton, “Visually lossless compression of digital hologram sequences,” Proc. SPIE 7529, 752912 (2010).
[Crossref]

R. Srivastava, J. R. Gupta, and H. Parthasarthy, “Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images,” Opt. Lasers Eng. 48, 626–635 (2010).
[Crossref]

2009 (5)

T. M. Lehtimäki, K. Sääskilahti, and T. J. Naughton, “Visual perception of digital holograms on autostereoscopic displays,” Proc. SPIE 7329, 73290C (2009).
[Crossref]

Y. Park, W. Choi, Z. Yaqoob, R. Dasari, K. Badizadegan, and M. S. Feld, “Speckle-field digital holographic microscopy,” Opt. Express 17, 12285–12292 (2009).
[Crossref]

P. Coupe, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Trans. Image Process. 18, 2221–2229 (2009).
[Crossref]

R. Ferzli and L. J. Karam, “A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB),” IEEE Trans. Image Process. 18, 717–728 (2009).
[Crossref]

J. L. Mateo and A. Fernández-Caballero, “Finding out general tendencies in speckle noise reduction in ultrasound images,” Expert Syst. Appl. 36, 7786–7797 (2009).
[Crossref]

2008 (2)

K. M. Molony, J. Maycock, J. B. McDonald, B. M. Hennelly, and T. J. Naughton, “A comparison of wavelet analysis techniques in digital holograms,” Proc. SPIE 6994, 699412 (2008).
[Crossref]

T. Nomura, M. Okamura, E. Nitanai, and T. Numata, “Image quality improvement of digital holography by superposition of reconstructed images obtained by multiple wavelengths,” Appl. Opt. 47, D38–D43 (2008).
[Crossref]

2007 (2)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[Crossref]

R. Ferzli and L. J. Karam, “A no reference objective shaprness metric using Riemannian tensor,” Simulation 1, 1 (2007).

2006 (2)

2004 (3)

L. Ma, H. Wang, Y. Li, and H. Jin, “Numerical reconstruction of digital holograms for three-dimensional shape measurement,” J. Opt. A 6, 396–400 (2004).
[Crossref]

Q. Kemao, “Windowed fourier transform for fringe pattern analysis,” Appl. Opt. 43, 2695–2702 (2004).
[Crossref]

P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “Perceptual blur and ringing metrics: application to jpeg2000,” Signal Process.: Image Commun. 19, 163–172 (2004).
[Crossref]

2003 (1)

W. An and T. E. Carlsson, “Speckle interferometry for measurement of continuous deformations,” Opt. Lasers Eng. 40, 529–541 (2003).
[Crossref]

2002 (1)

J.-L. Starck, E. Candès, and D. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[Crossref]

2001 (1)

D. Dirksen, H. Droste, B. Kemper, H. Delere, M. Deiwick, H. Scheld, and G. Von Bally, “Lensless Fourier holography for digital holographic interferometry on biological samples,” Opt. Lasers Eng. 36, 241–249 (2001).
[Crossref]

1994 (1)

1982 (2)

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 157–166 (1982).
[Crossref]

S. Erasmus and K. Smith, “An automatic focusing and astigmatism correction system for the SEM and CTEM,” J. Microsc. 127, 185–199 (1982).
[Crossref]

1980 (1)

J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2, 165–168 (1980).
[Crossref]

1952 (1)

R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs: I. The method of paired comparisons,” Biometrika 39, 324–345 (1952).
[Crossref]

1927 (1)

L. L. Thurstone, “A law of comparative judgment,” Psychol. Rev. 34, 273–286 (1927).
[Crossref]

Abashkin, V.

Achimova, E.

Ahar, A.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
[Crossref]

A. Ahar, T. Birnbaum, C. Jäh, and P. Schelkens, “A new similarity measure for complex amplitude holographic data,” in Applications of Digital Image Processing XL (2017), p. 54.

A. Ahar, T. Birnbaum, D. Blinder, A. Symeonidou, and P. Schelkens, “Performance evaluation of sparseness significance ranking measure (SSRM) on holographic content,” in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP) (Optical Society of America, 2018), p. JTu4A.10.

An, W.

W. An and T. E. Carlsson, “Speckle interferometry for measurement of continuous deformations,” Opt. Lasers Eng. 40, 529–541 (2003).
[Crossref]

Antonini, M.

M. V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P. T. Fiadeiro, A. M. Pinheiro, and M. Pereira, “Holographic representation: hologram plane vs. object plane,” Signal Process. Image Commun. 68, 193–206 (2018).
[Crossref]

Arrifano, A.

M. V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P. T. Fiadeiro, A. M. Pinheiro, and M. Pereira, “Holographic representation: hologram plane vs. object plane,” Signal Process. Image Commun. 68, 193–206 (2018).
[Crossref]

Badizadegan, K.

Barillot, C.

P. Coupe, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Trans. Image Process. 18, 2221–2229 (2009).
[Crossref]

Bernardo, M. V.

M. V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P. T. Fiadeiro, A. M. Pinheiro, and M. Pereira, “Holographic representation: hologram plane vs. object plane,” Signal Process. Image Commun. 68, 193–206 (2018).
[Crossref]

Bettens, S.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

Bianco, V.

V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, and P. Ferraro, “Strategies for reducing speckle noise in digital holography,” Light Sci. Appl. 7, 48 (2018).
[Crossref]

Birhman, A. S.

M. Kumar, A. S. Birhman, S. Kannan, and C. Shakher, “Measurement of initial displacement of canine and molar in human maxilla under different canine retraction methods using digital holographic interferometry,” Opt. Eng. 57, 094106 (2018).
[Crossref]

Birnbaum, T.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

A. Ahar, T. Birnbaum, D. Blinder, A. Symeonidou, and P. Schelkens, “Performance evaluation of sparseness significance ranking measure (SSRM) on holographic content,” in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP) (Optical Society of America, 2018), p. JTu4A.10.

A. Ahar, T. Birnbaum, C. Jäh, and P. Schelkens, “A new similarity measure for complex amplitude holographic data,” in Applications of Digital Image Processing XL (2017), p. 54.

Blinder, D.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
[Crossref]

A. Ahar, T. Birnbaum, D. Blinder, A. Symeonidou, and P. Schelkens, “Performance evaluation of sparseness significance ranking measure (SSRM) on holographic content,” in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP) (Optical Society of America, 2018), p. JTu4A.10.

Bovik, A. C.

L. Liu, Y. Hua, Q. Zhao, H. Huang, and A. C. Bovik, “Blind image quality assessment by relative gradient statistics and adaboosting neural network,” Signal Process.: Image Commun. 40, 1–15 (2016).
[Crossref]

Bradley, R. A.

R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs: I. The method of paired comparisons,” Biometrika 39, 324–345 (1952).
[Crossref]

Bruylants, T.

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
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L. Liu, Y. Hua, Q. Zhao, H. Huang, and A. C. Bovik, “Blind image quality assessment by relative gradient statistics and adaboosting neural network,” Signal Process.: Image Commun. 40, 1–15 (2016).
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V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, and P. Ferraro, “Strategies for reducing speckle noise in digital holography,” Light Sci. Appl. 7, 48 (2018).
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S. Montresor and P. Picart, “Quantitative appraisal for noise reduction in digital holographic phase imaging,” Opt. Express 24, 14322–14343 (2016).
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S. Montresor and P. Picart, “Evaluation of de-noising algorithms for amplitude image restoration in digital holography,” in Applied Industrial Optics: Spectroscopy, Imaging and Metrology (Optical Society of America, 2018), pp. JTu4A-8.

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A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (2005), Vol. 2, pp. 60–65.

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K. M. Molony, J. Maycock, J. B. McDonald, B. M. Hennelly, and T. J. Naughton, “A comparison of wavelet analysis techniques in digital holograms,” Proc. SPIE 6994, 699412 (2008).
[Crossref]

T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Evaluation of perceived quality attributes of digital holograms viewed with a stereoscopic display,” in 9th Euro-American Workshop on Information Optics (2010), pp. 1–3.

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G. Pedrini, V. Martnez-Garca, P. Weidmann, M. Wenzelburger, A. Killinger, U. Weber, S. Schmauder, R. Gadow, and W. Osten, “Residual stress analysis of ceramic coating by laser ablation and digital holography,” Exp. Mech. 56, 683–701 (2016).
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V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, and P. Ferraro, “Strategies for reducing speckle noise in digital holography,” Light Sci. Appl. 7, 48 (2018).
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S. Montresor and P. Picart, “Quantitative appraisal for noise reduction in digital holographic phase imaging,” Opt. Express 24, 14322–14343 (2016).
[Crossref]

S. Montresor and P. Picart, “Evaluation of de-noising algorithms for amplitude image restoration in digital holography,” in Applied Industrial Optics: Spectroscopy, Imaging and Metrology (Optical Society of America, 2018), pp. JTu4A-8.

Pinheiro, A.

Pinheiro, A. M.

M. V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P. T. Fiadeiro, A. M. Pinheiro, and M. Pereira, “Holographic representation: hologram plane vs. object plane,” Signal Process. Image Commun. 68, 193–206 (2018).
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T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Comparing numerical error and visual quality in reconstructions from compressed digital holograms,” Proc. SPIE 7690, 769012 (2010).
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T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Evaluation of perceived quality attributes of digital holograms viewed with a stereoscopic display,” in 9th Euro-American Workshop on Information Optics (2010), pp. 1–3.

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N. F. Zhang, A. Vladar, M. T. Postek, and R. D. Larrabee, “A kurtosis-based statistical measure for two-dimensional processes and its applications to image sharpness,” (2003).

Praneeth, D.

N. Venkatanath, D. Praneeth, M. Chandrasekhar Bh, S. S. Channappayya, and S. S. Medasani, “Blind image quality evaluation using perception based features,” in Twenty First National Conference on Communications (NCC) (2015), pp. 1–6.

Reilly, R. G.

T. M. Lehtimäki, R. G. Reilly, and T. J. Naughton, “Towards perception-inspired numerical measures of compression error in digital holograms of natural three-dimensional scenes,” in Imaging and Applied Optics (Optical Society of America, 2018), p. JTu4A.42.

Rivenson, Y.

Ruiz, C. G. T.

Sääskilahti, K.

T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Comparing numerical error and visual quality in reconstructions from compressed digital holograms,” Proc. SPIE 7690, 769012 (2010).
[Crossref]

T. M. Lehtimäki, K. Sääskilahti, and T. J. Naughton, “Visual perception of digital holograms on autostereoscopic displays,” Proc. SPIE 7329, 73290C (2009).
[Crossref]

T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Evaluation of perceived quality attributes of digital holograms viewed with a stereoscopic display,” in 9th Euro-American Workshop on Information Optics (2010), pp. 1–3.

Santoyo, F. M.

Scheld, H.

D. Dirksen, H. Droste, B. Kemper, H. Delere, M. Deiwick, H. Scheld, and G. Von Bally, “Lensless Fourier holography for digital holographic interferometry on biological samples,” Opt. Lasers Eng. 36, 241–249 (2001).
[Crossref]

Schelkens, P.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
[Crossref]

A. Ahar, T. Birnbaum, D. Blinder, A. Symeonidou, and P. Schelkens, “Performance evaluation of sparseness significance ranking measure (SSRM) on holographic content,” in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP) (Optical Society of America, 2018), p. JTu4A.10.

A. Ahar, T. Birnbaum, C. Jäh, and P. Schelkens, “A new similarity measure for complex amplitude holographic data,” in Applications of Digital Image Processing XL (2017), p. 54.

Schmauder, S.

G. Pedrini, V. Martnez-Garca, P. Weidmann, M. Wenzelburger, A. Killinger, U. Weber, S. Schmauder, R. Gadow, and W. Osten, “Residual stress analysis of ceramic coating by laser ablation and digital holography,” Exp. Mech. 56, 683–701 (2016).
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Schnars, U.

Schretter, C.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
[Crossref]

Shaked, D.

D. Shaked and I. Tastl, “Sharpness measure: towards automatic image enhancement,” in IEEE International Conference on Image Processing (IEEE, 2005), Vol. 1, pp. 1–937.

Shakher, C.

M. Kumar, A. S. Birhman, S. Kannan, and C. Shakher, “Measurement of initial displacement of canine and molar in human maxilla under different canine retraction methods using digital holographic interferometry,” Opt. Eng. 57, 094106 (2018).
[Crossref]

M. Kumar and C. Shakher, “Experimental characterization of the hygroscopic properties of wood during convective drying using digital holographic interferometry,” Appl. Opt. 55, 960–968 (2016).
[Crossref]

Shanmugan, K. S.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 157–166 (1982).
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Singh, K.

Smith, K.

S. Erasmus and K. Smith, “An automatic focusing and astigmatism correction system for the SEM and CTEM,” J. Microsc. 127, 185–199 (1982).
[Crossref]

Srivastava, R.

R. Srivastava, J. R. Gupta, and H. Parthasarthy, “Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images,” Opt. Lasers Eng. 48, 626–635 (2010).
[Crossref]

Starck, J.-L.

J.-L. Starck, E. Candès, and D. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[Crossref]

Stern, A.

Stiles, J. A.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 157–166 (1982).
[Crossref]

Symeonidou, A.

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

A. Ahar, T. Birnbaum, D. Blinder, A. Symeonidou, and P. Schelkens, “Performance evaluation of sparseness significance ranking measure (SSRM) on holographic content,” in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP) (Optical Society of America, 2018), p. JTu4A.10.

Tastl, I.

D. Shaked and I. Tastl, “Sharpness measure: towards automatic image enhancement,” in IEEE International Conference on Image Processing (IEEE, 2005), Vol. 1, pp. 1–937.

Terry, M. E.

R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs: I. The method of paired comparisons,” Biometrika 39, 324–345 (1952).
[Crossref]

Thurstone, L. L.

L. L. Thurstone, “A law of comparative judgment,” Psychol. Rev. 34, 273–286 (1927).
[Crossref]

Tounsi, Y.

Tsukida, K.

K. Tsukida and M. R. Gupta, “How to analyze paired comparison data,” (Department of Electrical Engineering, University of Washington, 2011).

Uzan, A.

Venkatanath, N.

N. Venkatanath, D. Praneeth, M. Chandrasekhar Bh, S. S. Channappayya, and S. S. Medasani, “Blind image quality evaluation using perception based features,” in Twenty First National Conference on Communications (NCC) (2015), pp. 1–6.

Ventre, M.

Vladar, A.

N. F. Zhang, A. Vladar, M. T. Postek, and R. D. Larrabee, “A kurtosis-based statistical measure for two-dimensional processes and its applications to image sharpness,” (2003).

Von Bally, G.

D. Dirksen, H. Droste, B. Kemper, H. Delere, M. Deiwick, H. Scheld, and G. Von Bally, “Lensless Fourier holography for digital holographic interferometry on biological samples,” Opt. Lasers Eng. 36, 241–249 (2001).
[Crossref]

Wang, H.

L. Ma, H. Wang, Y. Li, and H. Jin, “Numerical reconstruction of digital holograms for three-dimensional shape measurement,” J. Opt. A 6, 396–400 (2004).
[Crossref]

Weber, U.

G. Pedrini, V. Martnez-Garca, P. Weidmann, M. Wenzelburger, A. Killinger, U. Weber, S. Schmauder, R. Gadow, and W. Osten, “Residual stress analysis of ceramic coating by laser ablation and digital holography,” Exp. Mech. 56, 683–701 (2016).
[Crossref]

Weidmann, P.

G. Pedrini, V. Martnez-Garca, P. Weidmann, M. Wenzelburger, A. Killinger, U. Weber, S. Schmauder, R. Gadow, and W. Osten, “Residual stress analysis of ceramic coating by laser ablation and digital holography,” Exp. Mech. 56, 683–701 (2016).
[Crossref]

Wenzelburger, M.

G. Pedrini, V. Martnez-Garca, P. Weidmann, M. Wenzelburger, A. Killinger, U. Weber, S. Schmauder, R. Gadow, and W. Osten, “Residual stress analysis of ceramic coating by laser ablation and digital holography,” Exp. Mech. 56, 683–701 (2016).
[Crossref]

Winkler, S.

P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “Perceptual blur and ringing metrics: application to jpeg2000,” Signal Process.: Image Commun. 19, 163–172 (2004).
[Crossref]

Yaqoob, Z.

Zhang, N. F.

N. F. Zhang, A. Vladar, M. T. Postek, and R. D. Larrabee, “A kurtosis-based statistical measure for two-dimensional processes and its applications to image sharpness,” (2003).

Zhao, Q.

L. Liu, Y. Hua, Q. Zhao, H. Huang, and A. C. Bovik, “Blind image quality assessment by relative gradient statistics and adaboosting neural network,” Signal Process.: Image Commun. 40, 1–15 (2016).
[Crossref]

Appl. Opt. (10)

Q. Kemao, “Windowed fourier transform for fringe pattern analysis,” Appl. Opt. 43, 2695–2702 (2004).
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A. Nelleri, J. Joseph, and K. Singh, “Recognition and classification of three-dimensional phase objects by digital Fresnel holography,” Appl. Opt. 45, 4046–4053 (2006).
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T. Nomura, M. Okamura, E. Nitanai, and T. Numata, “Image quality improvement of digital holography by superposition of reconstructed images obtained by multiple wavelengths,” Appl. Opt. 47, D38–D43 (2008).
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A. Uzan, Y. Rivenson, and A. Stern, “Speckle denoising in digital holography by nonlocal means filtering,” Appl. Opt. 52, A195–A200 (2013).
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P. Memmolo, M. Iannone, M. Ventre, P. Netti, A. Finizio, M. Paturzo, and P. Ferraro, “Quantitative phase maps denoising of long holographic sequences by using SPADEDH algorithm,” Appl. Opt. 52, 1453–1460 (2013).
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M. Kumar and C. Shakher, “Experimental characterization of the hygroscopic properties of wood during convective drying using digital holographic interferometry,” Appl. Opt. 55, 960–968 (2016).
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E. S. Fonseca, P. T. Fiadeiro, M. Pereira, and A. Pinheiro, “Comparative analysis of autofocus functions in digital in-line phase-shifting holography,” Appl. Opt. 55, 7663–7674 (2016).
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V. Cazac, A. Meshalkin, E. Achimova, V. Abashkin, V. Katkovnik, I. Shevkunov, D. Claus, and G. Pedrini, “Surface relief and refractive index gratings patterned in chalcogenide glasses and studied by off-axis digital holography,” Appl. Opt. 57, 507–513 (2018).
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Y. Tounsi, M. Kumar, A. Nassim, and F. Mendoza-Santoyo, “Speckle noise reduction in digital speckle pattern interferometric fringes by nonlocal means and its related adaptive kernel-based methods,” Appl. Opt. 57, 7681–7690 (2018).
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Y. Tounsi, M. Kumar, A. Nassim, F. Mendoza-Santoyo, and O. Matoba, “Speckle denoising by variant nonlocal means methods,” Appl. Opt. 58, 7110–7120 (2019).
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Biomed. Opt. Express (1)

Biometrika (1)

R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs: I. The method of paired comparisons,” Biometrika 39, 324–345 (1952).
[Crossref]

Electron. Imag. (1)

K. Dabov and A. Foi, “Image denoising with block-matching and {3D} filtering,” Electron. Imag. 6064, 1–12 (2006).
[Crossref]

Exp. Mech. (1)

G. Pedrini, V. Martnez-Garca, P. Weidmann, M. Wenzelburger, A. Killinger, U. Weber, S. Schmauder, R. Gadow, and W. Osten, “Residual stress analysis of ceramic coating by laser ablation and digital holography,” Exp. Mech. 56, 683–701 (2016).
[Crossref]

Expert Syst. Appl. (1)

J. L. Mateo and A. Fernández-Caballero, “Finding out general tendencies in speckle noise reduction in ultrasound images,” Expert Syst. Appl. 36, 7786–7797 (2009).
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IEEE Trans. Image Process. (5)

N. D. Narvekar and L. J. Karam, “A no-reference image blur metric based on the cumulative probability of blur detection (CPBD),” IEEE Trans. Image Process. 20, 2678–2683 (2011).
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R. Ferzli and L. J. Karam, “A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB),” IEEE Trans. Image Process. 18, 717–728 (2009).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[Crossref]

P. Coupe, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Trans. Image Process. 18, 2221–2229 (2009).
[Crossref]

J.-L. Starck, E. Candès, and D. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2, 165–168 (1980).
[Crossref]

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4, 157–166 (1982).
[Crossref]

J. Microsc. (1)

S. Erasmus and K. Smith, “An automatic focusing and astigmatism correction system for the SEM and CTEM,” J. Microsc. 127, 185–199 (1982).
[Crossref]

J. Opt. A (1)

L. Ma, H. Wang, Y. Li, and H. Jin, “Numerical reconstruction of digital holograms for three-dimensional shape measurement,” J. Opt. A 6, 396–400 (2004).
[Crossref]

J. Opt. Soc. Am. A (1)

Light Sci. Appl. (1)

V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, and P. Ferraro, “Strategies for reducing speckle noise in digital holography,” Light Sci. Appl. 7, 48 (2018).
[Crossref]

Opt. Eng. (1)

M. Kumar, A. S. Birhman, S. Kannan, and C. Shakher, “Measurement of initial displacement of canine and molar in human maxilla under different canine retraction methods using digital holographic interferometry,” Opt. Eng. 57, 094106 (2018).
[Crossref]

Opt. Express (3)

Opt. Lasers Eng. (3)

D. Dirksen, H. Droste, B. Kemper, H. Delere, M. Deiwick, H. Scheld, and G. Von Bally, “Lensless Fourier holography for digital holographic interferometry on biological samples,” Opt. Lasers Eng. 36, 241–249 (2001).
[Crossref]

W. An and T. E. Carlsson, “Speckle interferometry for measurement of continuous deformations,” Opt. Lasers Eng. 40, 529–541 (2003).
[Crossref]

R. Srivastava, J. R. Gupta, and H. Parthasarthy, “Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images,” Opt. Lasers Eng. 48, 626–635 (2010).
[Crossref]

Proc. SPIE (5)

T. M. Lehtimäki, K. Sääskilahti, and T. J. Naughton, “Visual perception of digital holograms on autostereoscopic displays,” Proc. SPIE 7329, 73290C (2009).
[Crossref]

T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Comparing numerical error and visual quality in reconstructions from compressed digital holograms,” Proc. SPIE 7690, 769012 (2010).
[Crossref]

E. Darakis, M. Kowiel, R. Näsänen, and T. J. Naughton, “Visually lossless compression of digital hologram sequences,” Proc. SPIE 7529, 752912 (2010).
[Crossref]

A. Ahar, D. Blinder, T. Bruylants, C. Schretter, A. Munteanu, and P. Schelkens, “Subjective quality assessment of numerically reconstructed compressed holograms,” Proc. SPIE 9599, 95990K (2015).
[Crossref]

K. M. Molony, J. Maycock, J. B. McDonald, B. M. Hennelly, and T. J. Naughton, “A comparison of wavelet analysis techniques in digital holograms,” Proc. SPIE 6994, 699412 (2008).
[Crossref]

Psychol. Rev. (1)

L. L. Thurstone, “A law of comparative judgment,” Psychol. Rev. 34, 273–286 (1927).
[Crossref]

Signal Process. Image Commun. (2)

D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, and P. Schelkens, “Signal processing challenges for digital holographic video display systems,” Signal Process. Image Commun. 70, 114–130 (2019).
[Crossref]

M. V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P. T. Fiadeiro, A. M. Pinheiro, and M. Pereira, “Holographic representation: hologram plane vs. object plane,” Signal Process. Image Commun. 68, 193–206 (2018).
[Crossref]

Signal Process.: Image Commun. (2)

L. Liu, Y. Hua, Q. Zhao, H. Huang, and A. C. Bovik, “Blind image quality assessment by relative gradient statistics and adaboosting neural network,” Signal Process.: Image Commun. 40, 1–15 (2016).
[Crossref]

P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “Perceptual blur and ringing metrics: application to jpeg2000,” Signal Process.: Image Commun. 19, 163–172 (2004).
[Crossref]

Simulation (1)

R. Ferzli and L. J. Karam, “A no reference objective shaprness metric using Riemannian tensor,” Simulation 1, 1 (2007).

Other (16)

N. Venkatanath, D. Praneeth, M. Chandrasekhar Bh, S. S. Channappayya, and S. S. Medasani, “Blind image quality evaluation using perception based features,” in Twenty First National Conference on Communications (NCC) (2015), pp. 1–6.

A. V. Murthy and L. J. Karam, “A MATLAB-based framework for image and video quality evaluation,” in Second International Workshop on Quality of Multimedia Experience (QoMEX) (IEEE, 2010), pp. 242–247.

D. Shaked and I. Tastl, “Sharpness measure: towards automatic image enhancement,” in IEEE International Conference on Image Processing (IEEE, 2005), Vol. 1, pp. 1–937.

N. F. Zhang, A. Vladar, M. T. Postek, and R. D. Larrabee, “A kurtosis-based statistical measure for two-dimensional processes and its applications to image sharpness,” (2003).

R. Ferzli, L. J. Karam, and J. Caviedes, “A robust image sharpness metric based on kurtosis measurement of wavelet coefficients,” inProceedings of International Workshop on Video Processing and Quality Metrics for Consumer Electronics (2005), Vol. 12.

International Telecommunication Union, “Methodology for the subjective assessment of the quality of television pictures,” (2012).

International Telecommunication Union, “General viewing conditions for subjective assessment of quality of SDTV and HDTV television pictures on flat panel displays,” (2012).

K. Tsukida and M. R. Gupta, “How to analyze paired comparison data,” (Department of Electrical Engineering, University of Washington, 2011).

S. Montresor and P. Picart, “Evaluation of de-noising algorithms for amplitude image restoration in digital holography,” in Applied Industrial Optics: Spectroscopy, Imaging and Metrology (Optical Society of America, 2018), pp. JTu4A-8.

J. Dainty, Laser Speckle and Related Phenomena, Topics in Applied Physics (Springer, 2013).

J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts and Company Publishers, 2007).

A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (2005), Vol. 2, pp. 60–65.

A. Ahar, T. Birnbaum, C. Jäh, and P. Schelkens, “A new similarity measure for complex amplitude holographic data,” in Applications of Digital Image Processing XL (2017), p. 54.

A. Ahar, T. Birnbaum, D. Blinder, A. Symeonidou, and P. Schelkens, “Performance evaluation of sparseness significance ranking measure (SSRM) on holographic content,” in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP) (Optical Society of America, 2018), p. JTu4A.10.

T. M. Lehtimäki, R. G. Reilly, and T. J. Naughton, “Towards perception-inspired numerical measures of compression error in digital holograms of natural three-dimensional scenes,” in Imaging and Applied Optics (Optical Society of America, 2018), p. JTu4A.42.

T. M. Lehtimäki, K. Sääskilahti, T. Pitkäaho, and T. J. Naughton, “Evaluation of perceived quality attributes of digital holograms viewed with a stereoscopic display,” in 9th Euro-American Workshop on Information Optics (2010), pp. 1–3.

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Figures (8)

Fig. 1.
Fig. 1. Numerical reconstruction of the Dice2 digital hologram before (upper image) and after (lower image) applying a speckle reduction filter on the reconstructed amplitude. The right column shows how the histogram of a selected area changes after filter processing.
Fig. 2.
Fig. 2. Experimental acquired holograms from EmergImg-HoloGrail-v2. (a) Astronaut. (b) Car. (c) Dice1. (d) Skull. (e) Dice2.
Fig. 3.
Fig. 3. Stimuli visualization sequence. Up: observation; down: participant decision.
Fig. 4.
Fig. 4. MOS results (colored bars) and corresponding 95% confidence intervals (gray bars) per filter parameter index, for the set of four experimental holograms and for the four filters. (a) BM3D. (b) NLM. (c) Lee. (d) Frost.
Fig. 5.
Fig. 5. MOS results (colored bars) and corresponding 95% confidence intervals (gray bars) per filter at the respective optimal parameter value.
Fig. 6.
Fig. 6. Absolute values of Pearson linear correlation coefficient for the studied IQMs.
Fig. 7.
Fig. 7. Absolute values of Spearman rank order correlation coefficient for the studied IQMs.
Fig. 8.
Fig. 8. Selected IQM fits corresponding to Table 3. Each plot displays the results of the four objects, namely, Astronaut (A), Car (C), Dice1 (D), and Skull (S).

Tables (3)

Tables Icon

Table 1. Filter Parameters a

Tables Icon

Table 2. Optimal Filter Parameters

Tables Icon

Table 3. Performance Measures of the Selected IQMs

Equations (14)

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

p I ( I ) = 1 2 σ 2 exp ( I 2 σ 2 ) ,
p I ( I ) = ( N I ) N 1 Γ ( N ) I N 1 exp ( N I I ) ,
d ( i , j ) = μ s ( i , j ) + σ s 2 ( i , j ) σ s 2 ( i , j ) + σ n 2 ( i , j ) [ s ( i , j ) μ s ( i , j ) ] ,
d ( i , j ) = s ( i , j ) m ( i , j ) ,
C i j = w i j + 1 ,
μ L μ R = ϕ 1 [ C L R C L R + C R L ] ,
S C = σ ( I f ) μ ( I f ) .
S S I = S C ( I f ) S C ( I n ) .
S S M P I = Q σ ( I f ) σ ( I n ) ,
L A P = i , j [ I f ( i 1 , j ) + I f ( i + 1 , j ) + I f ( i , j 1 ) + I f ( i , j + 1 ) 4 I f ( i , j ) ] 2 .
A C R ( m , n ) = i , j I f ( i , j ) × I f ( i m , j n ) ,
S D R = 10 log 10 [ i , j I n ( i , j ) 2 i , j [ I n ( i , j ) I f ( i , j ) ] 2 ] .
E P C = i , j ( Δ I n Δ I n ¯ ) ( Δ I f Δ I f ¯ ) i , j ( Δ I n Δ I n ¯ ) 2 i , j ( Δ I f Δ I f ¯ ) 2 ,
M O S p = b 1 + b 2 1 + exp [ b 3 × ( M R b 4 ) ] ,

Metrics