Abstract

Camera-phone-based medical devices (CPMDs) represent a major emerging platform for point-of-care diagnostic imaging of biological tissue. In order to evaluate degradation in texture reproduction due to visible light image processing performed by CPMDs, a method ($MT{F_{DL}}$) involving the generation of a modulation transfer function ($MTF$) based on measurements of a ‘dead leaves (DL)’ target has been proposed. In this study, we have identified discrepancies in the quantification of noise based on gray patches of the DL target as compared to the textual region. To address this issue, we have proposed an approach ($MT{F_{DL - den}}$) for accurate $MT{F_{DL}}$ calculation through the use of effectively denoised DL images. Furthermore, we demonstrate that our $MT{F_{DL - den}}$ approach provides superior robustness to simulated noise. These findings will help establish effective standardized test methods for realistic benchtop assessment of CPMD image quality.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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  1. L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.
  2. J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
    [Crossref]
  3. J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
    [Crossref]
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  6. F. Cao, F. Guichard, and H. Hornung, “Dead leaves model for measuring texture quality on a digital camera,” Proc. SPIE 7537, 75370E (2010).
    [Crossref]
  7. J. McElvain, S. P. Campbell, J. Miller, and E. W. Jin, “Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems,” Proc. SPIE 7537, 75370D (2010).
    [Crossref]
  8. A. B. Lee, D. Mumford, and J. Huang, “Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model,” Int. J. Comput. Vision 41(1/2), 35–59 (2001).
    [Crossref]
  9. N. Azzabou, N. Paragios, and F. Guichard, “Uniform and textured regions separation in natural images towards MPM adaptive denoising,” in International Conference on Scale Space and Variational Methods in Computer Vision (Springer, 2007), pp.418–429.
  10. L. Kirk, P. Herzer, U. Artmann, and D. Kunz, “Description of texture loss using the dead leaves target: current issues and a new intrinsic approach,” Proc. SPIE 9023, 90230C (2014).
    [Crossref]
  11. U. Artmann, “Image quality assessment using the dead leaves target: experience with the latest approach and further investigations,” Proc. SPIE 9404, 94040J (2015).
    [Crossref]
  12. J. Nakamura, Image sensors and signal processing for digital still cameras (CRC Press, 2017).
  13. J. R. Barry, E. A. Lee, and D. G. Messerschmitt, Digital communication (Springer Science & Business Media, 2012).
  14. S. G. Chang, B. Yu, and M. Vetterli, “Wavelet thresholding for multiple noisy image copies,” IEEE Trans. Image Process. 9(9), 1631–1635 (2000).
    [Crossref]
  15. S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989).
    [Crossref]
  16. D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994).
    [Crossref]
  17. L. Birgé and P. Massart, “Gaussian model selection,” J. Eur. Math. Soc. 3(3), 203–268 (2001).
    [Crossref]
  18. D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Am. Stat. Assoc. 90(432), 1200–1224 (1995).
    [Crossref]
  19. R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
    [Crossref]
  20. G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Commun. Pure Appl. Math. 44(2), 141–183 (1991).
    [Crossref]
  21. I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41(7), 909–996 (1988).
    [Crossref]
  22. I. Daubechies, Ten lectures on wavelets (Society for Industrial and Applied Mathematics, 1992).

2015 (1)

U. Artmann, “Image quality assessment using the dead leaves target: experience with the latest approach and further investigations,” Proc. SPIE 9404, 94040J (2015).
[Crossref]

2014 (1)

L. Kirk, P. Herzer, U. Artmann, and D. Kunz, “Description of texture loss using the dead leaves target: current issues and a new intrinsic approach,” Proc. SPIE 9023, 90230C (2014).
[Crossref]

2013 (2)

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

2010 (2)

F. Cao, F. Guichard, and H. Hornung, “Dead leaves model for measuring texture quality on a digital camera,” Proc. SPIE 7537, 75370E (2010).
[Crossref]

J. McElvain, S. P. Campbell, J. Miller, and E. W. Jin, “Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems,” Proc. SPIE 7537, 75370D (2010).
[Crossref]

2007 (1)

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

2001 (2)

L. Birgé and P. Massart, “Gaussian model selection,” J. Eur. Math. Soc. 3(3), 203–268 (2001).
[Crossref]

A. B. Lee, D. Mumford, and J. Huang, “Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model,” Int. J. Comput. Vision 41(1/2), 35–59 (2001).
[Crossref]

2000 (1)

S. G. Chang, B. Yu, and M. Vetterli, “Wavelet thresholding for multiple noisy image copies,” IEEE Trans. Image Process. 9(9), 1631–1635 (2000).
[Crossref]

1995 (1)

D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Am. Stat. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

1994 (1)

D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994).
[Crossref]

1991 (1)

G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Commun. Pure Appl. Math. 44(2), 141–183 (1991).
[Crossref]

1989 (1)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989).
[Crossref]

1988 (1)

I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41(7), 909–996 (1988).
[Crossref]

Akilov, O.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Artmann, U.

U. Artmann, “Image quality assessment using the dead leaves target: experience with the latest approach and further investigations,” Proc. SPIE 9404, 94040J (2015).
[Crossref]

L. Kirk, P. Herzer, U. Artmann, and D. Kunz, “Description of texture loss using the dead leaves target: current issues and a new intrinsic approach,” Proc. SPIE 9023, 90230C (2014).
[Crossref]

Azzabou, N.

N. Azzabou, N. Paragios, and F. Guichard, “Uniform and textured regions separation in natural images towards MPM adaptive denoising,” in International Conference on Scale Space and Variational Methods in Computer Vision (Springer, 2007), pp.418–429.

Barry, J. R.

J. R. Barry, E. A. Lee, and D. G. Messerschmitt, Digital communication (Springer Science & Business Media, 2012).

Beylkin, G.

G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Commun. Pure Appl. Math. 44(2), 141–183 (1991).
[Crossref]

Binder, M.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Birgé, L.

L. Birgé and P. Massart, “Gaussian model selection,” J. Eur. Math. Soc. 3(3), 203–268 (2001).
[Crossref]

Campbell, S. P.

J. McElvain, S. P. Campbell, J. Miller, and E. W. Jin, “Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems,” Proc. SPIE 7537, 75370D (2010).
[Crossref]

Cao, F.

F. Cao, F. Guichard, and H. Hornung, “Dead leaves model for measuring texture quality on a digital camera,” Proc. SPIE 7537, 75370E (2010).
[Crossref]

Chang, S. G.

S. G. Chang, B. Yu, and M. Vetterli, “Wavelet thresholding for multiple noisy image copies,” IEEE Trans. Image Process. 9(9), 1631–1635 (2000).
[Crossref]

Coifman, R.

G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Commun. Pure Appl. Math. 44(2), 141–183 (1991).
[Crossref]

Daubechies, I.

I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41(7), 909–996 (1988).
[Crossref]

I. Daubechies, Ten lectures on wavelets (Society for Industrial and Applied Mathematics, 1992).

de Greef, L.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Donoho, D. L.

D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Am. Stat. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994).
[Crossref]

Dreiseitl, S.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

English, J. C.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Ferris, L. K.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Findlater, K.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Goel, M.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Gow, R. D.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Grant, L.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Guichard, F.

F. Cao, F. Guichard, and H. Hornung, “Dead leaves model for measuring texture quality on a digital camera,” Proc. SPIE 7537, 75370E (2010).
[Crossref]

N. Azzabou, N. Paragios, and F. Guichard, “Uniform and textured regions separation in natural images towards MPM adaptive denoising,” in International Conference on Scale Space and Variational Methods in Computer Vision (Springer, 2007), pp.418–429.

Hart, J.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Herzer, P.

L. Kirk, P. Herzer, U. Artmann, and D. Kunz, “Description of texture loss using the dead leaves target: current issues and a new intrinsic approach,” Proc. SPIE 9023, 90230C (2014).
[Crossref]

Ho, J.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Hornung, H.

F. Cao, F. Guichard, and H. Hornung, “Dead leaves model for measuring texture quality on a digital camera,” Proc. SPIE 7537, 75370E (2010).
[Crossref]

Huang, J.

A. B. Lee, D. Mumford, and J. Huang, “Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model,” Int. J. Comput. Vision 41(1/2), 35–59 (2001).
[Crossref]

Jin, E. W.

J. McElvain, S. P. Campbell, J. Miller, and E. W. Jin, “Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems,” Proc. SPIE 7537, 75370D (2010).
[Crossref]

Johnstone, I. M.

D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Am. Stat. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

Johnstone, J. M.

D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994).
[Crossref]

Kirk, L.

L. Kirk, P. Herzer, U. Artmann, and D. Kunz, “Description of texture loss using the dead leaves target: current issues and a new intrinsic approach,” Proc. SPIE 9023, 90230C (2014).
[Crossref]

Kunz, D.

L. Kirk, P. Herzer, U. Artmann, and D. Kunz, “Description of texture loss using the dead leaves target: current issues and a new intrinsic approach,” Proc. SPIE 9023, 90230C (2014).
[Crossref]

Larson, E. C.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Lee, A. B.

A. B. Lee, D. Mumford, and J. Huang, “Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model,” Int. J. Comput. Vision 41(1/2), 35–59 (2001).
[Crossref]

Lee, E. A.

J. R. Barry, E. A. Lee, and D. G. Messerschmitt, Digital communication (Springer Science & Business Media, 2012).

Mallat, S. G.

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989).
[Crossref]

Massart, P.

L. Birgé and P. Massart, “Gaussian model selection,” J. Eur. Math. Soc. 3(3), 203–268 (2001).
[Crossref]

McElvain, J.

J. McElvain, S. P. Campbell, J. Miller, and E. W. Jin, “Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems,” Proc. SPIE 7537, 75370D (2010).
[Crossref]

McLeod, S. J.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Messerschmitt, D. G.

J. R. Barry, E. A. Lee, and D. G. Messerschmitt, Digital communication (Springer Science & Business Media, 2012).

Miller, J.

J. McElvain, S. P. Campbell, J. Miller, and E. W. Jin, “Texture-based measurement of spatial frequency response using the dead leaves target: extensions, and application to real camera systems,” Proc. SPIE 7537, 75370D (2010).
[Crossref]

Moreau, J. F.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Mumford, D.

A. B. Lee, D. Mumford, and J. Huang, “Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model,” Int. J. Comput. Vision 41(1/2), 35–59 (2001).
[Crossref]

Nakamura, J.

J. Nakamura, Image sensors and signal processing for digital still cameras (CRC Press, 2017).

Nicol, R. L.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Paragios, N.

N. Azzabou, N. Paragios, and F. Guichard, “Uniform and textured regions separation in natural images towards MPM adaptive denoising,” in International Conference on Scale Space and Variational Methods in Computer Vision (Springer, 2007), pp.418–429.

Patel, S. N.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Patton, T.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Porkert, S.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Posch, C.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Ranharter, E.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Renshaw, D.

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

Rokhlin, V.

G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Commun. Pure Appl. Math. 44(2), 141–183 (1991).
[Crossref]

Scheibböck, C.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Seo, M. J.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Stout, J. W.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Taylor, J. A.

L. de Greef, M. Goel, M. J. Seo, E. C. Larson, J. W. Stout, J. A. Taylor, and S. N. Patel, “Bilicam: using mobile phones to monitor newborn jaundice,” inProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (ACM, 2014), pp. 331–342.

Vetterli, M.

S. G. Chang, B. Yu, and M. Vetterli, “Wavelet thresholding for multiple noisy image copies,” IEEE Trans. Image Process. 9(9), 1631–1635 (2000).
[Crossref]

Weingast, J.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Wolf, J. A.

J. A. Wolf, J. F. Moreau, O. Akilov, T. Patton, J. C. English, J. Ho, and L. K. Ferris, “Diagnostic inaccuracy of smartphone applications for melanoma detection,” JAMA Dermatol. 149(4), 422–426 (2013).
[Crossref]

Wurm, E. M.

J. Weingast, C. Scheibböck, E. M. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder, “A prospective study of mobile phones for dermatology in a clinical setting,” J. Telemed. Telecare 19(4), 213–218 (2013).
[Crossref]

Yu, B.

S. G. Chang, B. Yu, and M. Vetterli, “Wavelet thresholding for multiple noisy image copies,” IEEE Trans. Image Process. 9(9), 1631–1635 (2000).
[Crossref]

Biometrika (1)

D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994).
[Crossref]

Commun. Pure Appl. Math. (2)

G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Commun. Pure Appl. Math. 44(2), 141–183 (1991).
[Crossref]

I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41(7), 909–996 (1988).
[Crossref]

IEEE Trans. Electron Devices (1)

R. D. Gow, D. Renshaw, K. Findlater, L. Grant, S. J. McLeod, J. Hart, and R. L. Nicol, “A comprehensive tool for modeling CMOS image-sensor-noise performance,” IEEE Trans. Electron Devices 54(6), 1321–1329 (2007).
[Crossref]

IEEE Trans. Image Process. (1)

S. G. Chang, B. Yu, and M. Vetterli, “Wavelet thresholding for multiple noisy image copies,” IEEE Trans. Image Process. 9(9), 1631–1635 (2000).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989).
[Crossref]

Int. J. Comput. Vision (1)

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Figures (13)

Fig. 1.
Fig. 1. Similarity between (a) a DL chart and (b) a skin image with random texture.
Fig. 2.
Fig. 2. Flowchart describing the proposed MTFDL-den approach: (a) captured multiple frames, (b) one-step denoised image (${{\boldsymbol{I}}_{{\boldsymbol{avg}}}}$), (c) two-step denoised image (${{\boldsymbol{I}}_{{\boldsymbol{avg}} + {\boldsymbol{wav}}}}$), (d) calculated PSDimage-den, and (e) MTFDL-den.
Fig. 3.
Fig. 3. Experimental setup: (left) setup image, (right) schematic.
Fig. 4.
Fig. 4. Procedure for denoising performance evaluation.
Fig. 5.
Fig. 5. Parameters for wavelet thresholding: (a) B-M versus U-T methods, with ${{\boldsymbol{\sigma}}_{\boldsymbol{total}}}$=8.1; (b) denoised PSNR with different wavelet families at different wavelet decomposition levels, with ${{\boldsymbol{\sigma}}_{\boldsymbol{total}}}$=8.1; (c) denoised PSNR at different wavelet decomposition levels with ${{\boldsymbol{\sigma}}_{\boldsymbol{total}}}$ ranging from 7.7 to 20 (Coiflet-5 wavelet).
Fig. 6.
Fig. 6. Improvement in PSNR of the denoised image with image averaging (N = 10) and wavelet thresholding for different cameras, where PSNR1, PSNRavg and PSNRavg+wav are the PSNR values for I1, Iavg and Iavg+wav, respectively (Fig. 4) based on Eqs. (13), (14).
Fig. 7.
Fig. 7. Removed noise versus added noise in terms of ${\sigma _{total}}$ based on 10 noisy images: (a) Canon T3i; (b)iPhone 5S; (c) Nexus 5.
Fig. 8.
Fig. 8. ${{\boldsymbol{PSD}}_{\boldsymbol{FPN}}}$ calculated from dark frame images captured on (a) Canon T3i; (b) iPhone5S; (c) Nexus 5.
Fig. 9.
Fig. 9. PSDnoise calculated for the gray patch and the texture regions, captured (N = 10) using the a) Canon T3i (50 lx); b: Canon T3i (500 lx); c) iPhone 5S (JPEG); d) Nexus 5 (JPEG).
Fig. 10.
Fig. 10. MTF comparison for captured images with Canon T3i, iPhone 5S and Nexus 5 at illumination intensities of 50 lx and 500 lx.
Fig. 11.
Fig. 11. Intensity profiles of slanted-edge images for RAW and JPEG formats (inset): (a) 50 lx, and (b) 500 lx.
Fig. 12.
Fig. 12. Variation in PSNR of the denoised image with the number of images (N) used for averaging.
Fig. 13.
Fig. 13. Removed noise versus added noise in terms of ${\sigma _{total}}$based on 50 noisy images: (a) Canon T3i; (b) iPhone 5S; (c) Nexus 5.

Tables (1)

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Table 1. Values of ${{\sigma }_{{total}}}$ at different ${{\sigma }_{{thermal}}}$ levels

Equations (16)

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M T F D L C a o = P S D i m a g e / P S D t a r g e t
M T F D L g r a y = ( P S D i m a g e P S D n o i s e g r a y ) / P S D t a r g e t
y ( i , j ) = ( h x ) ( i , j ) + n ( i , j )
P S D i m a g e = | M T F | 2 P S D t a r g e t + P S D n o i s e
y ( i , j ) = ( h x x ) ( i , j ) + n x ( i , j )
P S D i m a g e = | M T F | 2 P S D t a r g e t + P S D n o i s e + 2 | M T F | P S D t a r g e t n o i s e
I a v g s = 1 N i = 1 N I i
I a v g w = i = 1 N α i I i , with α i = ( 1 σ i 2 ) / i = 1 N 1 σ i 2
σ ^ = M e d i a n ( | H H i | ) 0.6745
f ( t ) = i = 1 t c i 2 + 2 σ ^ 2 t ( α + log ( N p i x t ) )
η λ , s ( s i ) = { s i + λ , i f s i < λ s i λ , i f s i > λ 0 , i f | s i | λ
η λ , h ( s i ) = { s i , i f | s i | > λ 0 , i f | s i | λ
P S N R = 10 l o g 10 ( I m a x 2 M S E )
M S E = 1 a b i = 1 a j = 1 b ( I ( i , j ) I r e f ( i , j ) ) 2
M T F D L t e x = ( P S D i m a g e P S D n o i s e t e x ) / P S D t a r g e t
M T F D L d e n = P S D i m a g e d e n / P S D t a r g e t