Abstract

Contrast enhancement is an important task in image processing that is commonly used as a preprocessing step to improve the images for other tasks such as segmentation. However, some methods for contrast improvement that work well in low-contrast regions affect good contrast regions as well. This occurs due to the fact that some elements may vanish. A method focused on images with different luminance conditions is introduced in the present work. The proposed method is based on morphological transformations by reconstruction and rational operations, which, altogether, allow a more accurate contrast enhancement resulting in regions that are in harmony with their environment. Furthermore, due to the properties of these morphological transformations, the creation of new elements on the image is avoided. The processing is carried out on luminance values in the uvY color space, which avoids the creation of new colors. As a result of the previous considerations, the proposed method keeps the natural color appearance of the image.

© 2011 Optical Society of America

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    [CrossRef]
  2. J. Serra, “Toggle mappings,” Tech. Rep. N-18/88/MM (Centre de Morphologie Matematique, ENSMP, 1988).
  3. F. Meyer and J. Serra, “Activity mappings,” Signal Process. 16, 303–317 (1989).
    [CrossRef]
  4. A. Toet, “Multi-scale contrast enhancement with applications to image fusion,” Opt. Eng. 31, 1026–1031 (1992).
    [CrossRef]
  5. I. R. Terol-Villalobos, “Morphological slope filters,” Proc. SPIE 2588, 712–722 (1995).
  6. I. R. Terol-Villalobos, “Non-increasing filters using morphological gradient criteria,” Opt. Eng. 35, 3172–3182 (1996).
    [CrossRef]
  7. S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast enhancement,” Signal Process. 80, 685–696 (2000).
    [CrossRef]
  8. J. D. Mendiola-Santibañez and I. R. Terol-Vilallobos, “Mapeos de contraste morfológicos sobre particiones basados en la noción de zona plana,” Comput. Sist. 6, 25–37 (2002).
  9. J. D. Mendiola-Santibañez and I. R. Terol-Vilallobos, “Quantifying contrast methods through morphological gradient,” Comput. Sist. 8, 317–333 (2005).
  10. M. Espino-Gudiño, I. Santillãn, and I. R. Terol-Villalobos, “Morphological multiscale contrast approach for gray and color images consistent with visual perception,” Opt. Eng. 46, 067003 (2007).
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  13. P. Soille, E. J. Breen, and R. Jones, “Recursive implementation of erosions and dilations along discrete lines at arbitrary angles,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 562–567 (1996).
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  14. E. R. Urbach and M. H. F. Wilkinson, “Efficient 2-D grayscale morphological transformations with arbitrary flat structuring elements,” IEEE Trans. Image Process. 17, 1–8 (2008).
    [CrossRef]
  15. L. Vincent, “Current topics in applied morphological image analysis” in Current Trends in Stochastics Geometry and its Applications, W. S. Kendall, O. E. Barndorff-Nielsen, and M. C. van Lieshout, eds. (Chapman & Hall, 1997).
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    [CrossRef]
  17. E. Land and J. J. McCann, “Lightness and Retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971).
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  18. E. Land, “Recent advances in Retinex theory,” Vis. Res. 26, 7–21 (1986).
    [CrossRef]
  19. A. Rizzi, C. Gatta, and D. Marini, “From Retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization,” Electron. Imag. 13, 75–84(2004).
    [CrossRef]
  20. Z. Rahman, D. J. Jobson, and G. A. Woodell, “Retinex processing for automatic image enhancement,” Electron. Imag. 13, 100–110 (2004).
    [CrossRef]
  21. D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale Retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process. 6, 965–976 (1997).
    [CrossRef]
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  24. S. Süsstrunk, J. Holm, and G. D. Finlayson, “Chromatic adaptation performance of different RGB sensors,” Proc. SPIE 4300, 172–183 (2001).
  25. A. R. Smith, “Color gamut transform pairs,” ACM SIGGRAPH Comput. Graph. 12 (3), 12–19 (1978).
  26. R. C. González and R. E. Woods, Digital Image Processing(Prentice-Hall, 1992).
  27. H. Levkowitz and G. T. Herman, “GLHS: a generalized lightness, hue and saturation color models,” CVGIP Graph. Models Image Process. 55, 271–285 (1993).
    [CrossRef]
  28. A. Hanbury and J. Serra, “Mathematical morphology in CIELAB space,” Image Anal. Stereol. 21, 201–206 (2002).
  29. S. C. Pei, Y. C. Zeng, and C. H. Chang, “Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis,” IEEE Trans. Image Process. 13, 416–429 (2004).
    [CrossRef]
  30. L. Lucchese and S. K. Mitra, “A new class of chromatic filters for color image processing. Theory and applications,” IEEE Trans. Image Process. 13, 534–548 (2004).
    [CrossRef]
  31. D. Ruderman, T. W. Croning, and C. C. Chiao, “Statistics of cone responses to natural images: implications for visual coding,” J. Opt. Soc. Am. A 15, 2036–2045 (1998).
    [CrossRef]
  32. E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl. 21, 34–41 (2001).
    [CrossRef]
  33. R. G. Kogan, S. Agaian, and K. P. Lentz, “Visualization using rational morphology and magnitude reduction,” Proc. SPIE 3387, 301–312 (1998).
  34. E. H. Weber, De Pulsu, Resorptione, Audita et Tactu. Annotationes Anatomicae et Physiologicae. Koehler, Leipzig, translated by H. E. Ross (Academic, 1978).
  35. G. Matheron, Eléments pour une Théorie des Milieux Poreux (Masson, 1967).
  36. P. Maragos and R. D. Ziff, “Threshold superposition in morphological image analysis systems,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 498–504 (1990).
    [CrossRef]
  37. G. Matheron, Random Sets and Integral Geometry (Wiley, 1975).
  38. F. Dornaika and H. Zhang, “Granulometry using mathematical morphology and motion,” in Proceedings of IAPR Workshop on Machine Vision Applications (Springer-Verlag, 2000), pp. 51–54.
  39. L. Vincent, “Granulometries and opening trees,” Fundam. Informatic. 41, 57–90 (2000).
  40. E. R. Urbach, J. B. T. M. Roerdink, and M. H. F. Wilkinson, “Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 272–285 (2007).
    [CrossRef]
  41. P. Maragos, “Pattern spectrum and multiscale shape representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 701–716(1989).
    [CrossRef]
  42. E. J. Breen and R. Jones, “Attribute openings, thinnings and granulometries,” Comput. Vis. Image Underst. 64, 377–389 (1996).
    [CrossRef]
  43. G. K. Ouzounis, “Generalized connected morphological operators for robust shape extraction,” Ph. D. thesis (University of Groningen, 2009).
  44. G. K. Ouzounis and M. H. F. Wilkinson, “Mask-based second generation connectivity and attribute filters,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 990–1004 (2007).
    [CrossRef]
  45. M. H. F. Wilkinson, “Attribute-space connectivity and connected filters,” Image Vis. Comput. 25, 426–435 (2007).
    [CrossRef]
  46. G. K. Ouzounis, and M. H. F. Wilkinson, “Hyperconnected attribute filters based on k-flat zones,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 224–239 (2011).
    [CrossRef]
  47. P. Salembier, A. Oliveras, and L. Garrido, “Anti-extensive connected operators for image and sequence processing,” IEEE Trans. Image Process. 7, 555–570 (1998).
    [CrossRef]

2011

G. K. Ouzounis, and M. H. F. Wilkinson, “Hyperconnected attribute filters based on k-flat zones,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 224–239 (2011).
[CrossRef]

2008

E. R. Urbach and M. H. F. Wilkinson, “Efficient 2-D grayscale morphological transformations with arbitrary flat structuring elements,” IEEE Trans. Image Process. 17, 1–8 (2008).
[CrossRef]

2007

M. Espino-Gudiño, I. Santillãn, and I. R. Terol-Villalobos, “Morphological multiscale contrast approach for gray and color images consistent with visual perception,” Opt. Eng. 46, 067003 (2007).
[CrossRef]

E. R. Urbach, J. B. T. M. Roerdink, and M. H. F. Wilkinson, “Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 272–285 (2007).
[CrossRef]

G. K. Ouzounis and M. H. F. Wilkinson, “Mask-based second generation connectivity and attribute filters,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 990–1004 (2007).
[CrossRef]

M. H. F. Wilkinson, “Attribute-space connectivity and connected filters,” Image Vis. Comput. 25, 426–435 (2007).
[CrossRef]

2005

J. D. Mendiola-Santibañez and I. R. Terol-Vilallobos, “Quantifying contrast methods through morphological gradient,” Comput. Sist. 8, 317–333 (2005).

2004

A. Rizzi, C. Gatta, and D. Marini, “From Retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization,” Electron. Imag. 13, 75–84(2004).
[CrossRef]

Z. Rahman, D. J. Jobson, and G. A. Woodell, “Retinex processing for automatic image enhancement,” Electron. Imag. 13, 100–110 (2004).
[CrossRef]

S. C. Pei, Y. C. Zeng, and C. H. Chang, “Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis,” IEEE Trans. Image Process. 13, 416–429 (2004).
[CrossRef]

L. Lucchese and S. K. Mitra, “A new class of chromatic filters for color image processing. Theory and applications,” IEEE Trans. Image Process. 13, 534–548 (2004).
[CrossRef]

2002

A. Hanbury and J. Serra, “Mathematical morphology in CIELAB space,” Image Anal. Stereol. 21, 201–206 (2002).

J. D. Mendiola-Santibañez and I. R. Terol-Vilallobos, “Mapeos de contraste morfológicos sobre particiones basados en la noción de zona plana,” Comput. Sist. 6, 25–37 (2002).

2001

S. Süsstrunk, J. Holm, and G. D. Finlayson, “Chromatic adaptation performance of different RGB sensors,” Proc. SPIE 4300, 172–183 (2001).

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl. 21, 34–41 (2001).
[CrossRef]

2000

L. Vincent, “Granulometries and opening trees,” Fundam. Informatic. 41, 57–90 (2000).

S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast enhancement,” Signal Process. 80, 685–696 (2000).
[CrossRef]

1998

R. G. Kogan, S. Agaian, and K. P. Lentz, “Visualization using rational morphology and magnitude reduction,” Proc. SPIE 3387, 301–312 (1998).

D. Ruderman, T. W. Croning, and C. C. Chiao, “Statistics of cone responses to natural images: implications for visual coding,” J. Opt. Soc. Am. A 15, 2036–2045 (1998).
[CrossRef]

P. Salembier, A. Oliveras, and L. Garrido, “Anti-extensive connected operators for image and sequence processing,” IEEE Trans. Image Process. 7, 555–570 (1998).
[CrossRef]

1997

D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale Retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process. 6, 965–976 (1997).
[CrossRef]

1996

P. Soille, E. J. Breen, and R. Jones, “Recursive implementation of erosions and dilations along discrete lines at arbitrary angles,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 562–567 (1996).
[CrossRef]

I. R. Terol-Villalobos, “Non-increasing filters using morphological gradient criteria,” Opt. Eng. 35, 3172–3182 (1996).
[CrossRef]

E. J. Breen and R. Jones, “Attribute openings, thinnings and granulometries,” Comput. Vis. Image Underst. 64, 377–389 (1996).
[CrossRef]

1995

I. R. Terol-Villalobos, “Morphological slope filters,” Proc. SPIE 2588, 712–722 (1995).

1993

H. Levkowitz and G. T. Herman, “GLHS: a generalized lightness, hue and saturation color models,” CVGIP Graph. Models Image Process. 55, 271–285 (1993).
[CrossRef]

1992

A. Toet, “Multi-scale contrast enhancement with applications to image fusion,” Opt. Eng. 31, 1026–1031 (1992).
[CrossRef]

1990

P. Maragos and R. D. Ziff, “Threshold superposition in morphological image analysis systems,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 498–504 (1990).
[CrossRef]

1989

P. Maragos, “Pattern spectrum and multiscale shape representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 701–716(1989).
[CrossRef]

F. Meyer and J. Serra, “Activity mappings,” Signal Process. 16, 303–317 (1989).
[CrossRef]

1986

E. Land, “Recent advances in Retinex theory,” Vis. Res. 26, 7–21 (1986).
[CrossRef]

1984

C. Lantuéjoul and F. Maisonneuve, “Geodesic methods in quantitative image analysis,” Pattern Recogn. 17, 177–187 (1984).
[CrossRef]

1978

A. R. Smith, “Color gamut transform pairs,” ACM SIGGRAPH Comput. Graph. 12 (3), 12–19 (1978).

1975

H. P. Kramer and J. B. Bruckner, “Iteration of non-linear transformation for enhancement of digital image,” Pattern Recogn. 7, 53–58 (1975).
[CrossRef]

1971

Agaian, S.

R. G. Kogan, S. Agaian, and K. P. Lentz, “Visualization using rational morphology and magnitude reduction,” Proc. SPIE 3387, 301–312 (1998).

Ashikhmin, M.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl. 21, 34–41 (2001).
[CrossRef]

Barnard, K.

K. Barnard and B. Funt, “Investigations into multi-scale retinex (MSR),” in Colour Imaging: Vision and Technology, L.W.Macdonald and M.R.Luo, eds. (Wiley, 1999), pp. 17–36.

Barndorff-Nielsen, O. E.

L. Vincent, “Current topics in applied morphological image analysis” in Current Trends in Stochastics Geometry and its Applications, W. S. Kendall, O. E. Barndorff-Nielsen, and M. C. van Lieshout, eds. (Chapman & Hall, 1997).

Breen, E. J.

P. Soille, E. J. Breen, and R. Jones, “Recursive implementation of erosions and dilations along discrete lines at arbitrary angles,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 562–567 (1996).
[CrossRef]

E. J. Breen and R. Jones, “Attribute openings, thinnings and granulometries,” Comput. Vis. Image Underst. 64, 377–389 (1996).
[CrossRef]

Bruckner, J. B.

H. P. Kramer and J. B. Bruckner, “Iteration of non-linear transformation for enhancement of digital image,” Pattern Recogn. 7, 53–58 (1975).
[CrossRef]

Chanda, B.

S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast enhancement,” Signal Process. 80, 685–696 (2000).
[CrossRef]

Chang, C. H.

S. C. Pei, Y. C. Zeng, and C. H. Chang, “Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis,” IEEE Trans. Image Process. 13, 416–429 (2004).
[CrossRef]

Chiao, C. C.

Croning, T. W.

Dornaika, F.

F. Dornaika and H. Zhang, “Granulometry using mathematical morphology and motion,” in Proceedings of IAPR Workshop on Machine Vision Applications (Springer-Verlag, 2000), pp. 51–54.

Espino-Gudiño, M.

M. Espino-Gudiño, I. Santillãn, and I. R. Terol-Villalobos, “Morphological multiscale contrast approach for gray and color images consistent with visual perception,” Opt. Eng. 46, 067003 (2007).
[CrossRef]

Fairchild, M. D.

M. D. Fairchild, Color Appearance Models (Wiley, 2005).

Finlayson, G. D.

S. Süsstrunk, J. Holm, and G. D. Finlayson, “Chromatic adaptation performance of different RGB sensors,” Proc. SPIE 4300, 172–183 (2001).

Funt, B.

K. Barnard and B. Funt, “Investigations into multi-scale retinex (MSR),” in Colour Imaging: Vision and Technology, L.W.Macdonald and M.R.Luo, eds. (Wiley, 1999), pp. 17–36.

Garrido, L.

P. Salembier, A. Oliveras, and L. Garrido, “Anti-extensive connected operators for image and sequence processing,” IEEE Trans. Image Process. 7, 555–570 (1998).
[CrossRef]

Gatta, C.

A. Rizzi, C. Gatta, and D. Marini, “From Retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization,” Electron. Imag. 13, 75–84(2004).
[CrossRef]

González, R. C.

R. C. González and R. E. Woods, Digital Image Processing(Prentice-Hall, 1992).

Gooch, B.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl. 21, 34–41 (2001).
[CrossRef]

Hanbury, A.

A. Hanbury and J. Serra, “Mathematical morphology in CIELAB space,” Image Anal. Stereol. 21, 201–206 (2002).

Herman, G. T.

H. Levkowitz and G. T. Herman, “GLHS: a generalized lightness, hue and saturation color models,” CVGIP Graph. Models Image Process. 55, 271–285 (1993).
[CrossRef]

Holm, J.

S. Süsstrunk, J. Holm, and G. D. Finlayson, “Chromatic adaptation performance of different RGB sensors,” Proc. SPIE 4300, 172–183 (2001).

Jobson, D. J.

Z. Rahman, D. J. Jobson, and G. A. Woodell, “Retinex processing for automatic image enhancement,” Electron. Imag. 13, 100–110 (2004).
[CrossRef]

D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale Retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process. 6, 965–976 (1997).
[CrossRef]

Jones, R.

P. Soille, E. J. Breen, and R. Jones, “Recursive implementation of erosions and dilations along discrete lines at arbitrary angles,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 562–567 (1996).
[CrossRef]

E. J. Breen and R. Jones, “Attribute openings, thinnings and granulometries,” Comput. Vis. Image Underst. 64, 377–389 (1996).
[CrossRef]

Kendall, W. S.

L. Vincent, “Current topics in applied morphological image analysis” in Current Trends in Stochastics Geometry and its Applications, W. S. Kendall, O. E. Barndorff-Nielsen, and M. C. van Lieshout, eds. (Chapman & Hall, 1997).

Kogan, R. G.

R. G. Kogan, S. Agaian, and K. P. Lentz, “Visualization using rational morphology and magnitude reduction,” Proc. SPIE 3387, 301–312 (1998).

Kramer, H. P.

H. P. Kramer and J. B. Bruckner, “Iteration of non-linear transformation for enhancement of digital image,” Pattern Recogn. 7, 53–58 (1975).
[CrossRef]

Land, E.

E. Land, “Recent advances in Retinex theory,” Vis. Res. 26, 7–21 (1986).
[CrossRef]

E. Land and J. J. McCann, “Lightness and Retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971).
[CrossRef]

Lantuéjoul, C.

C. Lantuéjoul and F. Maisonneuve, “Geodesic methods in quantitative image analysis,” Pattern Recogn. 17, 177–187 (1984).
[CrossRef]

Lentz, K. P.

R. G. Kogan, S. Agaian, and K. P. Lentz, “Visualization using rational morphology and magnitude reduction,” Proc. SPIE 3387, 301–312 (1998).

Levkowitz, H.

H. Levkowitz and G. T. Herman, “GLHS: a generalized lightness, hue and saturation color models,” CVGIP Graph. Models Image Process. 55, 271–285 (1993).
[CrossRef]

Lucchese, L.

L. Lucchese and S. K. Mitra, “A new class of chromatic filters for color image processing. Theory and applications,” IEEE Trans. Image Process. 13, 534–548 (2004).
[CrossRef]

Maisonneuve, F.

C. Lantuéjoul and F. Maisonneuve, “Geodesic methods in quantitative image analysis,” Pattern Recogn. 17, 177–187 (1984).
[CrossRef]

Maragos, P.

P. Maragos and R. D. Ziff, “Threshold superposition in morphological image analysis systems,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 498–504 (1990).
[CrossRef]

P. Maragos, “Pattern spectrum and multiscale shape representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 701–716(1989).
[CrossRef]

Marini, D.

A. Rizzi, C. Gatta, and D. Marini, “From Retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization,” Electron. Imag. 13, 75–84(2004).
[CrossRef]

Matheron, G.

G. Matheron, Eléments pour une Théorie des Milieux Poreux (Masson, 1967).

G. Matheron, Random Sets and Integral Geometry (Wiley, 1975).

McCann, J. J.

Mendiola-Santibañez, J. D.

J. D. Mendiola-Santibañez and I. R. Terol-Vilallobos, “Quantifying contrast methods through morphological gradient,” Comput. Sist. 8, 317–333 (2005).

J. D. Mendiola-Santibañez and I. R. Terol-Vilallobos, “Mapeos de contraste morfológicos sobre particiones basados en la noción de zona plana,” Comput. Sist. 6, 25–37 (2002).

Meyer, F.

F. Meyer and J. Serra, “Activity mappings,” Signal Process. 16, 303–317 (1989).
[CrossRef]

Mitra, S. K.

L. Lucchese and S. K. Mitra, “A new class of chromatic filters for color image processing. Theory and applications,” IEEE Trans. Image Process. 13, 534–548 (2004).
[CrossRef]

Mukhopadhyay, S.

S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast enhancement,” Signal Process. 80, 685–696 (2000).
[CrossRef]

Oliveras, A.

P. Salembier, A. Oliveras, and L. Garrido, “Anti-extensive connected operators for image and sequence processing,” IEEE Trans. Image Process. 7, 555–570 (1998).
[CrossRef]

Ouzounis, G. K.

G. K. Ouzounis, and M. H. F. Wilkinson, “Hyperconnected attribute filters based on k-flat zones,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 224–239 (2011).
[CrossRef]

G. K. Ouzounis and M. H. F. Wilkinson, “Mask-based second generation connectivity and attribute filters,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 990–1004 (2007).
[CrossRef]

G. K. Ouzounis, “Generalized connected morphological operators for robust shape extraction,” Ph. D. thesis (University of Groningen, 2009).

Pei, S. C.

S. C. Pei, Y. C. Zeng, and C. H. Chang, “Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis,” IEEE Trans. Image Process. 13, 416–429 (2004).
[CrossRef]

Rahman, Z.

Z. Rahman, D. J. Jobson, and G. A. Woodell, “Retinex processing for automatic image enhancement,” Electron. Imag. 13, 100–110 (2004).
[CrossRef]

D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale Retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process. 6, 965–976 (1997).
[CrossRef]

Reinhard, E.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl. 21, 34–41 (2001).
[CrossRef]

Rizzi, A.

A. Rizzi, C. Gatta, and D. Marini, “From Retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization,” Electron. Imag. 13, 75–84(2004).
[CrossRef]

Roerdink, J. B. T. M.

E. R. Urbach, J. B. T. M. Roerdink, and M. H. F. Wilkinson, “Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 272–285 (2007).
[CrossRef]

Ross, H. E.

E. H. Weber, De Pulsu, Resorptione, Audita et Tactu. Annotationes Anatomicae et Physiologicae. Koehler, Leipzig, translated by H. E. Ross (Academic, 1978).

Ruderman, D.

Salembier, P.

P. Salembier, A. Oliveras, and L. Garrido, “Anti-extensive connected operators for image and sequence processing,” IEEE Trans. Image Process. 7, 555–570 (1998).
[CrossRef]

Santillãn, I.

M. Espino-Gudiño, I. Santillãn, and I. R. Terol-Villalobos, “Morphological multiscale contrast approach for gray and color images consistent with visual perception,” Opt. Eng. 46, 067003 (2007).
[CrossRef]

Serra, J.

A. Hanbury and J. Serra, “Mathematical morphology in CIELAB space,” Image Anal. Stereol. 21, 201–206 (2002).

F. Meyer and J. Serra, “Activity mappings,” Signal Process. 16, 303–317 (1989).
[CrossRef]

J. Serra, “Toggle mappings,” Tech. Rep. N-18/88/MM (Centre de Morphologie Matematique, ENSMP, 1988).

J. Serra, Image Analysis and Mathematical Morphology(Academic, 1982).

Shirley, P.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl. 21, 34–41 (2001).
[CrossRef]

Smith, A. R.

A. R. Smith, “Color gamut transform pairs,” ACM SIGGRAPH Comput. Graph. 12 (3), 12–19 (1978).

Soille, P.

P. Soille, E. J. Breen, and R. Jones, “Recursive implementation of erosions and dilations along discrete lines at arbitrary angles,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 562–567 (1996).
[CrossRef]

P. Soille, Morphological Image Analysis: Principles and Applications (Springer-Verlag, 2003).

Süsstrunk, S.

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Figures (8)

Fig. 1
Fig. 1

(a) Original image and its opening by reconstruction using (b)  λ = 4 , (c)  λ = 20 , and (d)  λ = 36 according to the results of its granulometric analysis using (e)  Δ = 1 and (f)  Δ = 8 .

Fig. 2
Fig. 2

(a) Image with two luminance conditions and (b) its luminance distribution.

Fig. 3
Fig. 3

Pattern spectrum mappings of Fig. 2a using (a) granulometry and (c) antigranulometry, and the luminance images as a result of applying (b)  R M γ ˜ with λ 1 = 296 and (d)  R M φ ˜ with λ 1 = 144 and λ 2 = 224 .

Fig. 4
Fig. 4

(a) Resulting image of the MRCO process by using a = 0.6 % of Fig. 3a and b = 0.4 % of Fig. 3d, and (b) its luminance distribution.

Fig. 5
Fig. 5

Color images of the improvement of Fig. 2a by (a) MSR, (c)  R M γ and (e) equalization with (b), (d), (f) their respective luminance distributions.

Fig. 6
Fig. 6

(a) Image with different luminance conditions and its improvement by MRCO in the color spaces (c)  u v Y , (e)  L * a * b * , and (g)  l α β with (b), (d), (f), (h) their respective color histograms.

Fig. 7
Fig. 7

(a) Uniformly dark image and its improvement by (c)  R M φ using λ = 256 and (e) MSR with (b), (d), (f) their respective luminance distributions.

Fig. 8
Fig. 8

(a) Image with good luminance condition and its process by (c) MSR and (e) MRCO with λ 1 = 100 for R M γ , λ 1 = 60 for R M φ , a = 0.5 , and b = 0.5 and (b), (d), (f) their respective luminance distributions.

Tables (3)

Tables Icon

Table 1 Algorithm 1. Rational Operation (I, oper, u)

Tables Icon

Table 2 Algorithm 2. Morphological Rational Operator for Contrast Enhancement

Tables Icon

Table 1 Evaluation Times for MRCO

Equations (16)

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

γ λ B = δ λ B [ ε λ B ( f ) ] , φ λ B ( f ) = ε λ B [ δ λ B ( f ) ] .
φ ˜ λ B ( f ) = lim n ε f n [ δ λ B ( f ) ] = ε f 1 ε f 1 ε f 1 until stability [ δ λ B ( f ) ] , γ ˜ λ B ( f ) = lim n δ f n [ ε λ B ( f ) ] = δ f 1 δ f 1 δ f 1 until stability [ ε λ B ( f ) ] .
SSR ( x , y , c ) = log f ( x , y ) F ( x , y , c ) * f ( x , y ) .
MSR ( x , y , w , c ) = n = 1 N w n SSR ( x , y , c n ) .
[ X Y Z ] = [ 0.49000 0.31000 0.20000 0.17697 0.81240 0.01063 0.00000 0.01000 0.99000 ] [ R G B ] ,
u = 4 X / X + 15 Y + 3 Z , v = 9 Y / X + 15 Y + 3 Z .
X n = 95.02 , Y n = 100 , Z n = 108.82 , L * = 116 f ( Y / Y n ) 16 , a * = 500 [ f ( X / X n ) f ( Y / Y n ) ] , b * = 200 [ f ( Y / Y n ) f ( Z / Z n ) ] , where     f ( ω ) = { ω 1 / 3 , ω > 0.008856 7.787 ω + 16 / 116 , otherwise
[ L M S ] = [ 0.3811 0.5783 0.0402 0.1967 0.7244 0.0782 0.0241 0.1288 0.8444 ] [ R G B ] ,
[ l α β ] = [ 0.5774 0.5774 0.5774 0.4082 0.4082 0.8165 0.7071 0.7071 0 ] log [ L M S ] .
R M γ ˜ ( x , λ ) = n = 1 N γ ˜ λ n 1 ( f ) ( x ) γ ˜ λ n ( f ) ( x ) , with γ ˜ λ 0 ( f ) = f .
R M φ ˜ ( x , λ ) = n = 1 N φ ˜ λ n ( f ) ( x ) φ ˜ λ n 1 ( f ) ( x ) , with     φ ˜ λ 0 ( f ) = f .
MRCO ( x ) = R M γ ˜ ( x , λ ) · a + R M φ ˜ ( x , λ ) · b .
γ λ ( f ) f ,
f g γ λ ( f ) γ λ ( g ) ,
γ λ ( γ μ ( f ) ) = γ max ( λ , μ ) ( f ) ,
PS ( f ) = mes ( γ λ ( f ) ) mes ( γ λ + Δ ( f ) ) mes ( f ) .

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