Abstract

Enhancing an image through increasing the contrast of the image is one effective way of image enhancement. To well enhance an image and suppress the produced noises in the resulting image, a multiscale top-hat selection transform-based algorithm through extracting bright and dark image regions and increasing the contrast between them is proposed. First, the multiscale top-hat selection transform is discussed and then is used to extract the bright and dark image regions of each scale. Second, the final extracted bright and dark image regions are obtained through a maximum operation on all the extracted multiscale bright and dark image regions at all scales. Finally, by using a weight strategy, the image is enhanced through increasing the contrast of the image by adding the final bright regions on and subtracting the final dark regions from the original image. The weight parameters are used to adjust the effect of image enhancement. Because the multiscale top-hat selection transform is used to effectively extract the final image regions and discriminate the possible noise regions, the image is well enhanced and some noises are suppressed. Experimental results on different types of images show that our algorithm performs well for noise-suppressed image enhancement and is useful for different applications.

© 2012 Optical Society of America

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    [CrossRef]
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2011 (2)

X. Bai, F. Zhou, and B. Xue, “Infrared image enhancement through contrast enhancement by using multi scale new top-hat transform,” Infrared Phys. Technol. 54, 61–69(2011).
[CrossRef]

J.-J. Lee, B.-G. Lee, and H. Yoo, “Image quality enhancement of computational integral imaging reconstruction for partially occluded objects using binary weighting mask on occlusion areas,” Appl. Opt. 50, 1889–1893 (2011).
[CrossRef]

2010 (4)

J. A. Ferrari and J. L. Flores, “Nondirectional edge enhancement by contrast-reverted low-pass Fourier filtering,” Appl. Opt. 49, 3291–3296 (2010).
[CrossRef]

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[CrossRef]

X. Bai and F. Zhou, “Top-hat selection transformation for infrared dim small target enhancement,” Imag. Sci. J. 58, 112–117 (2010).
[CrossRef]

R. Lai, Y. Yang, B. Wang, and H. Zhou, “A quantitative measure based infrared image enhancement algorithm using plateau histogram,” Opt. Commun. 283, 4283–4288 (2010).
[CrossRef]

2009 (2)

X. Bai, C. Sun, and F. Zhou, “Splitting touching cells based on concave points and ellipse fitting,” Pattern Recogn. 42, 2434–2446 (2009).
[CrossRef]

X. Bai, F. Zhou, Y. Xie, and T. Jin, “Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region,” Signal Process. 89, 1973–1989 (2009).
[CrossRef]

2008 (4)

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

M. A. Oliveira and N. J. Leite, “A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images,” Pattern Recogn. 41, 367–377 (2008).
[CrossRef]

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef]

2007 (2)

J. D. Mendiola-Santibanez, I. R. Terol-Villalobos, G. Herrera-Ruiz, and A. Fernandez-Bouzas, “Morphological contrast measure and contrast enhancement: one application to the segmentation of brain MRI,” Signal Process. 87, 2125–2150 (2007).
[CrossRef]

J. Angulo, “Morphological colour operators in totally ordered lattices based on distances: application to image filtering, enhancement and analysis,” Comput. Vis. Image Underst. 107, 56–73 (2007).
[CrossRef]

2006 (3)

M. Zeng, J. Li, and Z. Peng, “The design of top-hat morphological filter and application to infrared target detection,” Infrared Phys. Technol. 48, 67–76 (2006).
[CrossRef]

K.-Q. Huang, Q. Wang, and Z.-Y. Wu, “Natural color image enhancement and evaluation algorithm based on human visual system,” Comput. Vis. Image Underst. 103, 52–63 (2006).
[CrossRef]

I. De, B. Chanda, and B. Chattopadhyay, “Enhancing effective depth-of-field by image fusion using mathematical morphology,” Image Vis. Comput. 24, 1278–1287 (2006).
[CrossRef]

2005 (1)

F. Zhang, C. Li, and L. Shi, “Detecting and tracking dim moving point target in IR image sequences,” Infrared Phys. Technol. 46, 323–328 (2005).
[CrossRef]

2004 (2)

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement and denoising by complex diffusion processes,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004).
[CrossRef]

A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, “Morphological hat-transform scale spaces and their use in pattern classification,” Pattern Recogn. 37, 901–915 (2004).
[CrossRef]

2002 (2)

R. Buczynski, T. Szoplik, M. Taghizadeh, I. Veretennicoff, and H. Thienpont, “Mathematical morphology operations with a comparator array processor,” Opt. Lett. 27, 1818–1820(2002).
[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Forward-and-backward diffusion processes for adaptive image enhancement and denoising,” IEEE Trans. Image Process. 11, 689–703 (2002).
[CrossRef]

2001 (4)

S. S. Agaian, K. Panetta, and A. M. Grigoryan, “Transform-based image enhancement algorithms with performance measure,” IEEE Trans. Image Process. 10, 367–382 (2001).
[CrossRef]

B. Tang, G. Sapiro, and V. Caselles, “Color image enhancement via chromaticity diffusion,” IEEE Trans. Image Process. 10, 701–707 (2001).
[CrossRef]

B. Smolka and K. W. Wojciechowski, “Random walk approach to image enhancement,” Signal Process. 81, 465–482 (2001).
[CrossRef]

C. Kenney, Y. Deng, B. S. Manjunath, and G. Hewer, “Peer group image enhancement,” IEEE Trans. Image Process. 10, 326–334 (2001).
[CrossRef]

2000 (4)

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

P. T. Jackway, “Improved morphological top-hat,” Electron. Lett. 36, 1194–1195 (2000).
[CrossRef]

H. Greenspan, C. H. Anderson, and S. Akber, “Image enhancement by nonlinear extrapolation in frequency space,” IEEE Trans. Image Process. 9, 1035–1048 (2000).
[CrossRef]

F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Trans. Syst. Man Cybern. B 30, 110–119 (2000).
[CrossRef]

1999 (1)

B. Fischl and E. L. Schwartz, “Adaptive nonlocal filtering: a fast alternative to anisotropic diffusion for image enhancement,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 42–48 (1999).
[CrossRef]

1997 (2)

Y. Choi and R. Krishnapuram, “A robust approach to image enhancement based on fuzzy logic,” IEEE Trans. Image Process. 6, 808–825 (1997).
[CrossRef]

G.-Z. Yang and D. M. Hansell, “CT image enhancement with wavelet analysis for the detection of small airways disease,” IEEE Trans. Med. Imag. 16, 953–961 (1997).
[CrossRef]

1995 (1)

Agaian, S. S.

S. S. Agaian, K. Panetta, and A. M. Grigoryan, “Transform-based image enhancement algorithms with performance measure,” IEEE Trans. Image Process. 10, 367–382 (2001).
[CrossRef]

Akber, S.

H. Greenspan, C. H. Anderson, and S. Akber, “Image enhancement by nonlinear extrapolation in frequency space,” IEEE Trans. Image Process. 9, 1035–1048 (2000).
[CrossRef]

Anderson, C. H.

H. Greenspan, C. H. Anderson, and S. Akber, “Image enhancement by nonlinear extrapolation in frequency space,” IEEE Trans. Image Process. 9, 1035–1048 (2000).
[CrossRef]

Angulo, J.

J. Angulo, “Morphological colour operators in totally ordered lattices based on distances: application to image filtering, enhancement and analysis,” Comput. Vis. Image Underst. 107, 56–73 (2007).
[CrossRef]

Bai, X.

X. Bai, F. Zhou, and B. Xue, “Infrared image enhancement through contrast enhancement by using multi scale new top-hat transform,” Infrared Phys. Technol. 54, 61–69(2011).
[CrossRef]

X. Bai and F. Zhou, “Top-hat selection transformation for infrared dim small target enhancement,” Imag. Sci. J. 58, 112–117 (2010).
[CrossRef]

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[CrossRef]

X. Bai, C. Sun, and F. Zhou, “Splitting touching cells based on concave points and ellipse fitting,” Pattern Recogn. 42, 2434–2446 (2009).
[CrossRef]

X. Bai, F. Zhou, Y. Xie, and T. Jin, “Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region,” Signal Process. 89, 1973–1989 (2009).
[CrossRef]

Buczynski, R.

Caselles, V.

B. Tang, G. Sapiro, and V. Caselles, “Color image enhancement via chromaticity diffusion,” IEEE Trans. Image Process. 10, 701–707 (2001).
[CrossRef]

Caselli, F.

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

Chanda, B.

I. De, B. Chanda, and B. Chattopadhyay, “Enhancing effective depth-of-field by image fusion using mathematical morphology,” Image Vis. Comput. 24, 1278–1287 (2006).
[CrossRef]

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

Chattopadhyay, B.

I. De, B. Chanda, and B. Chattopadhyay, “Enhancing effective depth-of-field by image fusion using mathematical morphology,” Image Vis. Comput. 24, 1278–1287 (2006).
[CrossRef]

Choi, Y.

Y. Choi and R. Krishnapuram, “A robust approach to image enhancement based on fuzzy logic,” IEEE Trans. Image Process. 6, 808–825 (1997).
[CrossRef]

De, I.

I. De, B. Chanda, and B. Chattopadhyay, “Enhancing effective depth-of-field by image fusion using mathematical morphology,” Image Vis. Comput. 24, 1278–1287 (2006).
[CrossRef]

Deng, Y.

C. Kenney, Y. Deng, B. S. Manjunath, and G. Hewer, “Peer group image enhancement,” IEEE Trans. Image Process. 10, 326–334 (2001).
[CrossRef]

Farbiz, F.

F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Trans. Syst. Man Cybern. B 30, 110–119 (2000).
[CrossRef]

Fernandez-Bouzas, A.

J. D. Mendiola-Santibanez, I. R. Terol-Villalobos, G. Herrera-Ruiz, and A. Fernandez-Bouzas, “Morphological contrast measure and contrast enhancement: one application to the segmentation of brain MRI,” Signal Process. 87, 2125–2150 (2007).
[CrossRef]

Ferrari, J. A.

Ferreira, C.

Fierro, M.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef]

Fischl, B.

B. Fischl and E. L. Schwartz, “Adaptive nonlocal filtering: a fast alternative to anisotropic diffusion for image enhancement,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 42–48 (1999).
[CrossRef]

Flores, J. L.

Frigerio, M.

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

Garcia, J.

Gatta, C.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef]

Gilboa, G.

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement and denoising by complex diffusion processes,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004).
[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Forward-and-backward diffusion processes for adaptive image enhancement and denoising,” IEEE Trans. Image Process. 11, 689–703 (2002).
[CrossRef]

Greenspan, H.

H. Greenspan, C. H. Anderson, and S. Akber, “Image enhancement by nonlinear extrapolation in frequency space,” IEEE Trans. Image Process. 9, 1035–1048 (2000).
[CrossRef]

Grigoryan, A. M.

S. S. Agaian, K. Panetta, and A. M. Grigoryan, “Transform-based image enhancement algorithms with performance measure,” IEEE Trans. Image Process. 10, 367–382 (2001).
[CrossRef]

Guan, R.

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

Hagan, M. T.

F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Trans. Syst. Man Cybern. B 30, 110–119 (2000).
[CrossRef]

Hansell, D. M.

G.-Z. Yang and D. M. Hansell, “CT image enhancement with wavelet analysis for the detection of small airways disease,” IEEE Trans. Med. Imag. 16, 953–961 (1997).
[CrossRef]

Herrera-Ruiz, G.

J. D. Mendiola-Santibanez, I. R. Terol-Villalobos, G. Herrera-Ruiz, and A. Fernandez-Bouzas, “Morphological contrast measure and contrast enhancement: one application to the segmentation of brain MRI,” Signal Process. 87, 2125–2150 (2007).
[CrossRef]

Hewer, G.

C. Kenney, Y. Deng, B. S. Manjunath, and G. Hewer, “Peer group image enhancement,” IEEE Trans. Image Process. 10, 326–334 (2001).
[CrossRef]

Huang, K.-Q.

K.-Q. Huang, Q. Wang, and Z.-Y. Wu, “Natural color image enhancement and evaluation algorithm based on human visual system,” Comput. Vis. Image Underst. 103, 52–63 (2006).
[CrossRef]

Jackway, P. T.

P. T. Jackway, “Improved morphological top-hat,” Electron. Lett. 36, 1194–1195 (2000).
[CrossRef]

Jalba, A. C.

A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, “Morphological hat-transform scale spaces and their use in pattern classification,” Pattern Recogn. 37, 901–915 (2004).
[CrossRef]

Jin, T.

X. Bai, F. Zhou, Y. Xie, and T. Jin, “Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region,” Signal Process. 89, 1973–1989 (2009).
[CrossRef]

Kaufmann, A.

A. Kaufmann, Introduction to the Theory of Fuzzy Subsets (Academic, 1975).

Kenney, C.

C. Kenney, Y. Deng, B. S. Manjunath, and G. Hewer, “Peer group image enhancement,” IEEE Trans. Image Process. 10, 326–334 (2001).
[CrossRef]

Krishnapuram, R.

Y. Choi and R. Krishnapuram, “A robust approach to image enhancement based on fuzzy logic,” IEEE Trans. Image Process. 6, 808–825 (1997).
[CrossRef]

Lai, R.

R. Lai, Y. Yang, B. Wang, and H. Zhou, “A quantitative measure based infrared image enhancement algorithm using plateau histogram,” Opt. Commun. 283, 4283–4288 (2010).
[CrossRef]

Lee, B.-G.

Lee, J.-J.

Leite, N. J.

M. A. Oliveira and N. J. Leite, “A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images,” Pattern Recogn. 41, 367–377 (2008).
[CrossRef]

Li, C.

F. Zhang, C. Li, and L. Shi, “Detecting and tracking dim moving point target in IR image sequences,” Infrared Phys. Technol. 46, 323–328 (2005).
[CrossRef]

Li, J.

M. Zeng, J. Li, and Z. Peng, “The design of top-hat morphological filter and application to infrared target detection,” Infrared Phys. Technol. 48, 67–76 (2006).
[CrossRef]

Liang, Y.

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

Lin, H.

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

Lojacono, R.

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

Lu, L.

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

Manjunath, B. S.

C. Kenney, Y. Deng, B. S. Manjunath, and G. Hewer, “Peer group image enhancement,” IEEE Trans. Image Process. 10, 326–334 (2001).
[CrossRef]

Mencattini, A.

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

Mendiola-Santibanez, J. D.

J. D. Mendiola-Santibanez, I. R. Terol-Villalobos, G. Herrera-Ruiz, and A. Fernandez-Bouzas, “Morphological contrast measure and contrast enhancement: one application to the segmentation of brain MRI,” Signal Process. 87, 2125–2150 (2007).
[CrossRef]

Menhaj, M. B.

F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Trans. Syst. Man Cybern. B 30, 110–119 (2000).
[CrossRef]

Motamedi, S. A.

F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Trans. Syst. Man Cybern. B 30, 110–119 (2000).
[CrossRef]

Mukhopadhyay, S.

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

Oliveira, M. A.

M. A. Oliveira and N. J. Leite, “A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images,” Pattern Recogn. 41, 367–377 (2008).
[CrossRef]

Panetta, K.

S. S. Agaian, K. Panetta, and A. M. Grigoryan, “Transform-based image enhancement algorithms with performance measure,” IEEE Trans. Image Process. 10, 367–382 (2001).
[CrossRef]

Peng, Z.

M. Zeng, J. Li, and Z. Peng, “The design of top-hat morphological filter and application to infrared target detection,” Infrared Phys. Technol. 48, 67–76 (2006).
[CrossRef]

Provenzi, E.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef]

Rizzi, A.

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef]

Roerdink, J. B. T. M.

A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, “Morphological hat-transform scale spaces and their use in pattern classification,” Pattern Recogn. 37, 901–915 (2004).
[CrossRef]

Salmeri, M.

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

Sapiro, G.

B. Tang, G. Sapiro, and V. Caselles, “Color image enhancement via chromaticity diffusion,” IEEE Trans. Image Process. 10, 701–707 (2001).
[CrossRef]

Schwartz, E. L.

B. Fischl and E. L. Schwartz, “Adaptive nonlocal filtering: a fast alternative to anisotropic diffusion for image enhancement,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 42–48 (1999).
[CrossRef]

Shi, L.

F. Zhang, C. Li, and L. Shi, “Detecting and tracking dim moving point target in IR image sequences,” Infrared Phys. Technol. 46, 323–328 (2005).
[CrossRef]

Shi, X.

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

Smolka, B.

B. Smolka and K. W. Wojciechowski, “Random walk approach to image enhancement,” Signal Process. 81, 465–482 (2001).
[CrossRef]

Sochen, N.

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement and denoising by complex diffusion processes,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004).
[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Forward-and-backward diffusion processes for adaptive image enhancement and denoising,” IEEE Trans. Image Process. 11, 689–703 (2002).
[CrossRef]

Soille, P.

P. Soille, Morphological Image Analysis—Principle and Applications (Springer, 2003).

Sun, C.

X. Bai, C. Sun, and F. Zhou, “Splitting touching cells based on concave points and ellipse fitting,” Pattern Recogn. 42, 2434–2446 (2009).
[CrossRef]

Szoplik, T.

Taghizadeh, M.

Tang, B.

B. Tang, G. Sapiro, and V. Caselles, “Color image enhancement via chromaticity diffusion,” IEEE Trans. Image Process. 10, 701–707 (2001).
[CrossRef]

Terol-Villalobos, I. R.

J. D. Mendiola-Santibanez, I. R. Terol-Villalobos, G. Herrera-Ruiz, and A. Fernandez-Bouzas, “Morphological contrast measure and contrast enhancement: one application to the segmentation of brain MRI,” Signal Process. 87, 2125–2150 (2007).
[CrossRef]

Thienpont, H.

Veretennicoff, I.

Wang, B.

R. Lai, Y. Yang, B. Wang, and H. Zhou, “A quantitative measure based infrared image enhancement algorithm using plateau histogram,” Opt. Commun. 283, 4283–4288 (2010).
[CrossRef]

Wang, Q.

K.-Q. Huang, Q. Wang, and Z.-Y. Wu, “Natural color image enhancement and evaluation algorithm based on human visual system,” Comput. Vis. Image Underst. 103, 52–63 (2006).
[CrossRef]

Wilkinson, M. H. F.

A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, “Morphological hat-transform scale spaces and their use in pattern classification,” Pattern Recogn. 37, 901–915 (2004).
[CrossRef]

Wojciechowski, K. W.

B. Smolka and K. W. Wojciechowski, “Random walk approach to image enhancement,” Signal Process. 81, 465–482 (2001).
[CrossRef]

Wu, Z.-Y.

K.-Q. Huang, Q. Wang, and Z.-Y. Wu, “Natural color image enhancement and evaluation algorithm based on human visual system,” Comput. Vis. Image Underst. 103, 52–63 (2006).
[CrossRef]

Xie, Y.

X. Bai, F. Zhou, Y. Xie, and T. Jin, “Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region,” Signal Process. 89, 1973–1989 (2009).
[CrossRef]

Xue, B.

X. Bai, F. Zhou, and B. Xue, “Infrared image enhancement through contrast enhancement by using multi scale new top-hat transform,” Infrared Phys. Technol. 54, 61–69(2011).
[CrossRef]

Yang, C.

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

Yang, G.-Z.

G.-Z. Yang and D. M. Hansell, “CT image enhancement with wavelet analysis for the detection of small airways disease,” IEEE Trans. Med. Imag. 16, 953–961 (1997).
[CrossRef]

Yang, Y.

R. Lai, Y. Yang, B. Wang, and H. Zhou, “A quantitative measure based infrared image enhancement algorithm using plateau histogram,” Opt. Commun. 283, 4283–4288 (2010).
[CrossRef]

Yoo, H.

Zeevi, Y. Y.

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement and denoising by complex diffusion processes,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004).
[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Forward-and-backward diffusion processes for adaptive image enhancement and denoising,” IEEE Trans. Image Process. 11, 689–703 (2002).
[CrossRef]

Zeng, M.

M. Zeng, J. Li, and Z. Peng, “The design of top-hat morphological filter and application to infrared target detection,” Infrared Phys. Technol. 48, 67–76 (2006).
[CrossRef]

Zhang, F.

F. Zhang, C. Li, and L. Shi, “Detecting and tracking dim moving point target in IR image sequences,” Infrared Phys. Technol. 46, 323–328 (2005).
[CrossRef]

Zhou, F.

X. Bai, F. Zhou, and B. Xue, “Infrared image enhancement through contrast enhancement by using multi scale new top-hat transform,” Infrared Phys. Technol. 54, 61–69(2011).
[CrossRef]

X. Bai and F. Zhou, “Top-hat selection transformation for infrared dim small target enhancement,” Imag. Sci. J. 58, 112–117 (2010).
[CrossRef]

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[CrossRef]

X. Bai, F. Zhou, Y. Xie, and T. Jin, “Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region,” Signal Process. 89, 1973–1989 (2009).
[CrossRef]

X. Bai, C. Sun, and F. Zhou, “Splitting touching cells based on concave points and ellipse fitting,” Pattern Recogn. 42, 2434–2446 (2009).
[CrossRef]

Zhou, H.

R. Lai, Y. Yang, B. Wang, and H. Zhou, “A quantitative measure based infrared image enhancement algorithm using plateau histogram,” Opt. Commun. 283, 4283–4288 (2010).
[CrossRef]

Appl. Opt. (3)

Comput. Vis. Image Underst. (2)

K.-Q. Huang, Q. Wang, and Z.-Y. Wu, “Natural color image enhancement and evaluation algorithm based on human visual system,” Comput. Vis. Image Underst. 103, 52–63 (2006).
[CrossRef]

J. Angulo, “Morphological colour operators in totally ordered lattices based on distances: application to image filtering, enhancement and analysis,” Comput. Vis. Image Underst. 107, 56–73 (2007).
[CrossRef]

Electron. Lett. (1)

P. T. Jackway, “Improved morphological top-hat,” Electron. Lett. 36, 1194–1195 (2000).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi, and Y. Liang, “A fuzzy-statistics-based principal component analysis (FS-PCA) method for multispectral image enhancement and display,” IEEE Trans. Geosci. Remote Sens. 46, 3937–3947 (2008).
[CrossRef]

IEEE Trans. Image Process. (6)

Y. Choi and R. Krishnapuram, “A robust approach to image enhancement based on fuzzy logic,” IEEE Trans. Image Process. 6, 808–825 (1997).
[CrossRef]

C. Kenney, Y. Deng, B. S. Manjunath, and G. Hewer, “Peer group image enhancement,” IEEE Trans. Image Process. 10, 326–334 (2001).
[CrossRef]

B. Tang, G. Sapiro, and V. Caselles, “Color image enhancement via chromaticity diffusion,” IEEE Trans. Image Process. 10, 701–707 (2001).
[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Forward-and-backward diffusion processes for adaptive image enhancement and denoising,” IEEE Trans. Image Process. 11, 689–703 (2002).
[CrossRef]

H. Greenspan, C. H. Anderson, and S. Akber, “Image enhancement by nonlinear extrapolation in frequency space,” IEEE Trans. Image Process. 9, 1035–1048 (2000).
[CrossRef]

S. S. Agaian, K. Panetta, and A. M. Grigoryan, “Transform-based image enhancement algorithms with performance measure,” IEEE Trans. Image Process. 10, 367–382 (2001).
[CrossRef]

IEEE Trans. Instrum. Meas. (1)

A. Mencattini, M. Salmeri, R. Lojacono, M. Frigerio, and F. Caselli, “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing,” IEEE Trans. Instrum. Meas. 57, 1422–1430 (2008).
[CrossRef]

IEEE Trans. Med. Imag. (1)

G.-Z. Yang and D. M. Hansell, “CT image enhancement with wavelet analysis for the detection of small airways disease,” IEEE Trans. Med. Imag. 16, 953–961 (1997).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement and denoising by complex diffusion processes,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1020–1036 (2004).
[CrossRef]

B. Fischl and E. L. Schwartz, “Adaptive nonlocal filtering: a fast alternative to anisotropic diffusion for image enhancement,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 42–48 (1999).
[CrossRef]

E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1757–1770 (2008).
[CrossRef]

IEEE Trans. Syst. Man Cybern. B (1)

F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Trans. Syst. Man Cybern. B 30, 110–119 (2000).
[CrossRef]

Imag. Sci. J. (1)

X. Bai and F. Zhou, “Top-hat selection transformation for infrared dim small target enhancement,” Imag. Sci. J. 58, 112–117 (2010).
[CrossRef]

Image Vis. Comput. (1)

I. De, B. Chanda, and B. Chattopadhyay, “Enhancing effective depth-of-field by image fusion using mathematical morphology,” Image Vis. Comput. 24, 1278–1287 (2006).
[CrossRef]

Infrared Phys. Technol. (3)

X. Bai, F. Zhou, and B. Xue, “Infrared image enhancement through contrast enhancement by using multi scale new top-hat transform,” Infrared Phys. Technol. 54, 61–69(2011).
[CrossRef]

F. Zhang, C. Li, and L. Shi, “Detecting and tracking dim moving point target in IR image sequences,” Infrared Phys. Technol. 46, 323–328 (2005).
[CrossRef]

M. Zeng, J. Li, and Z. Peng, “The design of top-hat morphological filter and application to infrared target detection,” Infrared Phys. Technol. 48, 67–76 (2006).
[CrossRef]

Opt. Commun. (1)

R. Lai, Y. Yang, B. Wang, and H. Zhou, “A quantitative measure based infrared image enhancement algorithm using plateau histogram,” Opt. Commun. 283, 4283–4288 (2010).
[CrossRef]

Opt. Lett. (1)

Pattern Recogn. (4)

M. A. Oliveira and N. J. Leite, “A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images,” Pattern Recogn. 41, 367–377 (2008).
[CrossRef]

A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, “Morphological hat-transform scale spaces and their use in pattern classification,” Pattern Recogn. 37, 901–915 (2004).
[CrossRef]

X. Bai, C. Sun, and F. Zhou, “Splitting touching cells based on concave points and ellipse fitting,” Pattern Recogn. 42, 2434–2446 (2009).
[CrossRef]

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[CrossRef]

Signal Process. (4)

X. Bai, F. Zhou, Y. Xie, and T. Jin, “Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region,” Signal Process. 89, 1973–1989 (2009).
[CrossRef]

J. D. Mendiola-Santibanez, I. R. Terol-Villalobos, G. Herrera-Ruiz, and A. Fernandez-Bouzas, “Morphological contrast measure and contrast enhancement: one application to the segmentation of brain MRI,” Signal Process. 87, 2125–2150 (2007).
[CrossRef]

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

B. Smolka and K. W. Wojciechowski, “Random walk approach to image enhancement,” Signal Process. 81, 465–482 (2001).
[CrossRef]

Other (2)

P. Soille, Morphological Image Analysis—Principle and Applications (Springer, 2003).

A. Kaufmann, Introduction to the Theory of Fuzzy Subsets (Academic, 1975).

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Figures (11)

Fig. 1.
Fig. 1.

Implementation of the proposed algorithm.

Fig. 2.
Fig. 2.

Example of CT image: (a) original image; (b) enhanced image.

Fig. 3.
Fig. 3.

Example of scenery image: (a) original image; (b) enhanced image.

Fig. 4.
Fig. 4.

Example of mineral image: (a) original image; (b) enhanced image.

Fig. 5.
Fig. 5.

Example of an image of a child: (a) original image; (b) enhanced image.

Fig. 6.
Fig. 6.

Example of Lena image: (a) original image; (b) enhanced result of HE; (c) enhanced result of CLAHE; (d) enhanced result of MSM; (e) enhanced result of our algorithm.

Fig. 7.
Fig. 7.

Example of an infrared image: (a) original image; (b) enhanced result of HE; (c) enhanced result of CLAHE; (d) enhanced result of MSM; (e) enhanced result of our algorithm.

Fig. 8.
Fig. 8.

Example of an infrared image with very low contrast and dim target: (a) original image; (b) enhanced result of HE; (c) enhanced result of CLAHE; (d) enhanced result of MSM; (e) enhanced result of our algorithm.

Fig. 9.
Fig. 9.

Example of a cell image: (a) original image; (b) enhanced result of HE; (c) enhanced result of CLAHE; (d) enhanced result of MSM; (e) enhanced result of our algorithm.

Fig. 10.
Fig. 10.

Example of mineral image: (a) original image; (b) enhanced result of HE; (c) enhanced result of CLAHE; (d) enhanced result of MSM; (e) enhanced result of our algorithm.

Fig. 11.
Fig. 11.

Example of biomedical image: (a) original image; (b) enhanced result of HE; (c) enhanced result of CLAHE; (d) enhanced result of MSM; (e) enhanced result of our algorithm.

Tables (1)

Tables Icon

Table 1. Quantitative Comparison of Image Enhancement Using γ

Equations (19)

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

fB=maxu,v(f(xu,yv)+B(u,v)),fB=minu,v(f(x+u,y+v)B(u,v)).
fB=(fB)B,fB=(fB)B.
WTH(x,y)=f(x,y)fB(x,y),BTH(x,y)=fB(x,y)f(x,y).
fEn=f+WTHBTH.
WTH(x,y)={f(x,y)fB(x,y),f(x,y)fB(x,y)0s,else,
WTHS(x,y)={f(x,y)fB(x,y),t1f(x,y)fB(x,y)t2t,else,
BTHS(x,y)={fB(x,y)f(x,y),t1fB(x,y)f(x,y)t2t,else.
WTHS(x,y)={f(x,y)fB(x,y),f(x,y)fB(x,y)nL0,else,
BTHS(x,y)={fB(x,y)f(x,y),fB(x,y)f(x,y)nL0,else.
Bi=B0B0B0dilationitimes
WTHSi(x,y){f(x,y)fBi(x,y),f(x,y)fBi(x,y)nL0,else.
WTHS0(x,y)={f(x,y)fB0(x,y),f(x,y)fB0(x,y)nL0,else,WTHS1(x,y)={f(x,y)fB1(x,y),f(x,y)fB1(x,y)nL0,else,,WTHSn(x,y)={f(x,y)fBn(x,y),f(x,y)fBn(x,y)nL0,else.
BTHSi(x,y)={fBi(x,y)f(x,y),fBi(x,y)f(x,y)nL0,else.
BTHS0(x,y)={fB0(x,y)f(x,y),fB0(x,y)f(x,y)nL0,else,BTHS1(x,y)={fB1(x,y)f(x,y),fB1(x,y)f(x,y)nL0,else,,BTHSn(x,y)={fBn(x,y)f(x,y),fBn(x,y)f(x,y)nL0,else.
WTHSe=maxi(WTHSi).
BTHSe=maxi(BTHSi).
fEn=f+WTHSeBTHSe.
fEn=f+α1×WTHSeα2×BTHSe.
γ(f)=2MNx=1my=1nmin{pxy,(1pxy)},pxy=sin[π2×(1fxyfmax)].

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