Abstract

Existing hierarchical techniques that decompose an image into a smooth image and high frequency components based on Gaussian filter and bilateral filter suffer from halo effects, whereas techniques based on weighted least squares extract low contrast features as details. Other techniques require multiple images and are not tolerant to noise. We use a single image to enhance sharpness based on a hierarchical framework using a modified Laplacian pyramid. In order to ensure robustness, we remove noise by using an extra level in the hierarchical framework. We use an edge-preserving nonlocal means filter and modify it to remove potential halo effects and gradient reversals. However, these effects are only reduced but not removed completely after similar modifications are made to the bilateral filter. We compare our results with existing techniques and show better decomposition and enhancement. Based on validation by human observers, we introduce a new measure to quantify sharpness quality, which allows us to automatically set parameters in order to achieve preferred sharpness enhancement. This causes blurry images to be sharpened more and sufficiently sharp images not to be sharpened. Finally, we demonstrate applications in the context of robust high dynamic range tone mapping that is better than state-of-the-art approaches and enhancement of archaeological artifacts.

© 2013 Optical Society of America

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    [CrossRef]
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2011 (1)

E. S. L. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Trans. Graph. 30, 1–12 (2011).
[CrossRef]

2010 (1)

M. Kass and J. Solomon, “Smoothed local histogram filters,” ACM Trans. Graph. 29, 1–10 (2010).
[CrossRef]

2009 (1)

R. Fattal, “Edge-avoiding wavelets and their applications,” ACM Trans. Graph. 28, 1–10 (2009).
[CrossRef]

2008 (3)

B. Zhang and J. Allebach, “Adaptive bilateral filter for sharpness enhancement and noise removal,” IEEE Trans. Image Process. 17, 664–678 (2008).
[CrossRef]

Y.-L. Liu, J. Wang, X. Chen, Y.-W. Guo, and Q.-S. Peng, “A robust and fast non-local means algorithm for image denoising,” J. Comput. Sci. Tech. 23, 270–279 (2008).
[CrossRef]

A. Buades, B. Coll, and J.-M. Morel, “Nonlocal image and movie denoising,” Int. J. Comput. Vis. 76, 123–139 (2008).
[CrossRef]

2007 (1)

J. Chen, S. Paris, and F. Durand, “Real-time edge-aware image processing with the bilateral grid,” ACM Trans. Graph. 26(3), 103 (2007).
[CrossRef]

2006 (2)

S. Bae, S. Paris, and F. Durand, “Two-scale tone management for photographic look,” ACM Trans. Graph. 25, 637–645 (2006).
[CrossRef]

Z. Chen, B. Abidi, D. Page, and M. Abidi, “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement—part I: the basic method,” IEEE Trans. Image Process. 15, 2290–2302 (2006).
[CrossRef]

2005 (1)

B. Zhang, J. P. Allebach, and Z. Pizlo, “An investigation of perceived sharpness and sharpness metrics,” Proc. SPIE 5668, pp. 98–1102005).

2004 (2)

E. Peli, J. Kim, Y. Yitzhaky, R. B. Goldstein, and R. L. Woods, “Wideband enhancement of television images for people with visual impairments,” J. Opt. Soc. Am. 21, 937–950 (2004).
[CrossRef]

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

2003 (1)

P. Lin and Y.-T. Kim, “An adaptive color transient improvement algorithm,” IEEE Trans. Consum. Electron. 49, 1326–1329 (2003).
[CrossRef]

2002 (2)

E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” ACM Trans. Graph. 21, 267–276 (2002).
[CrossRef]

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24603–619 (2002).
[CrossRef]

2000 (1)

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

1997 (1)

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]

1990 (1)

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990).
[CrossRef]

1983 (1)

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

1982 (1)

J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology 143, 29–36 (1982).

1945 (1)

F. Wilcoxon, “Individual comparisons by ranking methods,” Biom. Bull. 1, 80–83 (1945).
[CrossRef]

Abidi, B.

Z. Chen, B. Abidi, D. Page, and M. Abidi, “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement—part I: the basic method,” IEEE Trans. Image Process. 15, 2290–2302 (2006).
[CrossRef]

Abidi, M.

Z. Chen, B. Abidi, D. Page, and M. Abidi, “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement—part I: the basic method,” IEEE Trans. Image Process. 15, 2290–2302 (2006).
[CrossRef]

Adelson, E. H.

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

Y. Li, L. Sharan, and E. H. Adelson, “Compressing and companding high dynamic range images with subband architectures,” in ACM SIGGRAPH (ACM, 2005), pp. 836–844.

Agrawala, M.

R. Fattal, M. Agrawala, and S. Rusinkiewicz, “Multi scale shape and detail enhancement from multi-light image collections,” in ACM SIGGRAPH (ACM, 2007).

Akber, S.

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

Allebach, J.

B. Zhang and J. Allebach, “Adaptive bilateral filter for sharpness enhancement and noise removal,” IEEE Trans. Image Process. 17, 664–678 (2008).
[CrossRef]

Allebach, J. P.

B. Zhang, J. P. Allebach, and Z. Pizlo, “An investigation of perceived sharpness and sharpness metrics,” Proc. SPIE 5668, pp. 98–1102005).

Anderson, C.

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

Bae, S.

S. Bae, S. Paris, and F. Durand, “Two-scale tone management for photographic look,” ACM Trans. Graph. 25, 637–645 (2006).
[CrossRef]

Bilcu, R. C.

R. C. Bilcu and M. Vehvilainen, “Constrained unsharp masking for image enhancement,” in International Conference on Image and Signal Processing (Springer-Verlag, 2008), pp. 10–19.

Bovik, A.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

Buades, A.

A. Buades, B. Coll, and J.-M. Morel, “Nonlocal image and movie denoising,” Int. J. Comput. Vis. 76, 123–139 (2008).
[CrossRef]

Burt, P. J.

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

Caviedes, J.

J. Caviedes and S. Gurbuz, “No-reference sharpness metric based on local edge kurtosis” in ICIP--International Conference on Image Processing (IEEE, 2002).

Chen, J.

J. Chen, S. Paris, and F. Durand, “Real-time edge-aware image processing with the bilateral grid,” ACM Trans. Graph. 26(3), 103 (2007).
[CrossRef]

Chen, X.

Y.-L. Liu, J. Wang, X. Chen, Y.-W. Guo, and Q.-S. Peng, “A robust and fast non-local means algorithm for image denoising,” J. Comput. Sci. Tech. 23, 270–279 (2008).
[CrossRef]

Chen, Z.

Z. Chen, B. Abidi, D. Page, and M. Abidi, “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement—part I: the basic method,” IEEE Trans. Image Process. 15, 2290–2302 (2006).
[CrossRef]

Choudhury, A.

A. Choudhury and G. Medioni, “Perceptually motivated automatic sharpness enhancement using hierarchy of non-local means,” in ICCV 2011—Proceedings of Color and Photometry in Computer Vision Workshop (IEEE Computer Society, 2011), pp. 730–737.

Choudhury, P.

P. Choudhury and J. Tumblin, “Trilateral filter for high contrast images and meshes,” in ACM SIGGRAPH (ACM, 2005).

Coll, B.

A. Buades, B. Coll, and J.-M. Morel, “Nonlocal image and movie denoising,” Int. J. Comput. Vis. 76, 123–139 (2008).
[CrossRef]

Comaniciu, D.

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24603–619 (2002).
[CrossRef]

Dorsey, J.

F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” in ACM SIGGRAPH (ACM, 2002), pp. 257–266.

Durand, F.

J. Chen, S. Paris, and F. Durand, “Real-time edge-aware image processing with the bilateral grid,” ACM Trans. Graph. 26(3), 103 (2007).
[CrossRef]

S. Bae, S. Paris, and F. Durand, “Two-scale tone management for photographic look,” ACM Trans. Graph. 25, 637–645 (2006).
[CrossRef]

F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” in ACM SIGGRAPH (ACM, 2002), pp. 257–266.

K. Subr, C. Soler, and F. Durand, “Edge-preserving multiscale image decomposition based on local extrema,” in ACM SIGGRAPH Asia (ACM, 2009).

Fairchild, M. D.

S. N. Pattanaik, J. A. Ferwerda, M. D. Fairchild, and D. P. Greenberg, “A multiscale model of adaptation and spatial vision for realistic image display,” in ACM SIGGRAPH (ACM, 1998), pp. 287–298.

Farbman, Z.

Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” in ACM SIGGRAPH (ACM, 2008).

Fattal, R.

R. Fattal, “Edge-avoiding wavelets and their applications,” ACM Trans. Graph. 28, 1–10 (2009).
[CrossRef]

R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” in ACM SIGGRAPH (ACM, 2002), pp. 249–256.

Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” in ACM SIGGRAPH (ACM, 2008).

R. Fattal, M. Agrawala, and S. Rusinkiewicz, “Multi scale shape and detail enhancement from multi-light image collections,” in ACM SIGGRAPH (ACM, 2007).

Ferwerda, J.

E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” ACM Trans. Graph. 21, 267–276 (2002).
[CrossRef]

Ferwerda, J. A.

S. N. Pattanaik, J. A. Ferwerda, M. D. Fairchild, and D. P. Greenberg, “A multiscale model of adaptation and spatial vision for realistic image display,” in ACM SIGGRAPH (ACM, 1998), pp. 287–298.

Gastal, E. S. L.

E. S. L. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Trans. Graph. 30, 1–12 (2011).
[CrossRef]

Gelb, D.

T. Malzbender, D. Gelb, and H. Wolters, “Polynomial texture maps,” in ACM SIGGRAPH (ACM, 2001).

Goldstein, R. B.

E. Peli, J. Kim, Y. Yitzhaky, R. B. Goldstein, and R. L. Woods, “Wideband enhancement of television images for people with visual impairments,” J. Opt. Soc. Am. 21, 937–950 (2004).
[CrossRef]

Greenberg, D. P.

S. N. Pattanaik, J. A. Ferwerda, M. D. Fairchild, and D. P. Greenberg, “A multiscale model of adaptation and spatial vision for realistic image display,” in ACM SIGGRAPH (ACM, 1998), pp. 287–298.

Greenspan, H.

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

Guo, Y.-W.

Y.-L. Liu, J. Wang, X. Chen, Y.-W. Guo, and Q.-S. Peng, “A robust and fast non-local means algorithm for image denoising,” J. Comput. Sci. Tech. 23, 270–279 (2008).
[CrossRef]

Gurbuz, S.

J. Caviedes and S. Gurbuz, “No-reference sharpness metric based on local edge kurtosis” in ICIP--International Conference on Image Processing (IEEE, 2002).

Hanley, J. A.

J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology 143, 29–36 (1982).

Hasinoff, S. W.

S. Paris, S. W. Hasinoff, and J. Kautz, “Local Laplacian filters: edge-aware image processing with a Laplacian pyramid,” in ACM SIGGRAPH (ACM, 2011).

Hentschel, C.

C. Hentschel, “Video moire cancellation filter for high-resolution crts,” in ICCE (IEEE Computer Society, 1999), pp. 200–201.

C. Hentschel and D. La Hei, “Effective peaking filter and its implementation on a programmable architecture,” in ICCE (IEEE Computer Society, 1999), pp. 330–331.

Jia, J.

L. Xu, C. Lu, Y. Xu, and J. Jia, “Image smoothing via L0 gradient minimization,” in SIGGRAPH Asia (ACM, 2011), pp. 1–12.

Jobson, D. J.

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]

Kass, M.

M. Kass and J. Solomon, “Smoothed local histogram filters,” ACM Trans. Graph. 29, 1–10 (2010).
[CrossRef]

Kautz, J.

S. Paris, S. W. Hasinoff, and J. Kautz, “Local Laplacian filters: edge-aware image processing with a Laplacian pyramid,” in ACM SIGGRAPH (ACM, 2011).

Kharlamov, A.

A. Kharlamov and V. Podlozhnyuk, “Image denoising,” NVIDIA Technical Report (NVIDIA, 2007).

Kim, J.

E. Peli, J. Kim, Y. Yitzhaky, R. B. Goldstein, and R. L. Woods, “Wideband enhancement of television images for people with visual impairments,” J. Opt. Soc. Am. 21, 937–950 (2004).
[CrossRef]

Kim, Y.-T.

P. Lin and Y.-T. Kim, “An adaptive color transient improvement algorithm,” IEEE Trans. Consum. Electron. 49, 1326–1329 (2003).
[CrossRef]

Kolomenkin, M.

M. Kolomenkin, I. Shimshoni, and A. Tal, “Prominent field for shape analysis of archaeological artifacts,” in CCV 2009—IEEE 12th International Conference on Computer Vision: Workshops on eHeritage & Digital Art Preservation (IEEE, 2009), pp. 915–923.

Krotkov, E. P.

E. P. Krotkov, Active Computer Vision by Cooperative Focus and Stereo (Springer-Verlag, 1989).

La Hei, D.

C. Hentschel and D. La Hei, “Effective peaking filter and its implementation on a programmable architecture,” in ICCE (IEEE Computer Society, 1999), pp. 330–331.

Li, Y.

Y. Li, L. Sharan, and E. H. Adelson, “Compressing and companding high dynamic range images with subband architectures,” in ACM SIGGRAPH (ACM, 2005), pp. 836–844.

Lin, P.

P. Lin and Y.-T. Kim, “An adaptive color transient improvement algorithm,” IEEE Trans. Consum. Electron. 49, 1326–1329 (2003).
[CrossRef]

Lischinski, D.

Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” in ACM SIGGRAPH (ACM, 2008).

R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” in ACM SIGGRAPH (ACM, 2002), pp. 249–256.

Liu, Y.-L.

Y.-L. Liu, J. Wang, X. Chen, Y.-W. Guo, and Q.-S. Peng, “A robust and fast non-local means algorithm for image denoising,” J. Comput. Sci. Tech. 23, 270–279 (2008).
[CrossRef]

Lu, C.

L. Xu, C. Lu, Y. Xu, and J. Jia, “Image smoothing via L0 gradient minimization,” in SIGGRAPH Asia (ACM, 2011), pp. 1–12.

Malik, J.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990).
[CrossRef]

Malzbender, T.

T. Malzbender, D. Gelb, and H. Wolters, “Polynomial texture maps,” in ACM SIGGRAPH (ACM, 2001).

Manduchi, R.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in ICCV-1998, Sixth International Conference on Computer Vision (IEEE Computer Society, 1998), pp. 839–846.

McNeil, B. J.

J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology 143, 29–36 (1982).

Medioni, G.

A. Choudhury and G. Medioni, “Perceptually motivated automatic sharpness enhancement using hierarchy of non-local means,” in ICCV 2011—Proceedings of Color and Photometry in Computer Vision Workshop (IEEE Computer Society, 2011), pp. 730–737.

Meer, P.

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24603–619 (2002).
[CrossRef]

Morel, J.-M.

A. Buades, B. Coll, and J.-M. Morel, “Nonlocal image and movie denoising,” Int. J. Comput. Vis. 76, 123–139 (2008).
[CrossRef]

Oliveira, M. M.

E. S. L. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Trans. Graph. 30, 1–12 (2011).
[CrossRef]

Page, D.

Z. Chen, B. Abidi, D. Page, and M. Abidi, “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement—part I: the basic method,” IEEE Trans. Image Process. 15, 2290–2302 (2006).
[CrossRef]

Paris, S.

J. Chen, S. Paris, and F. Durand, “Real-time edge-aware image processing with the bilateral grid,” ACM Trans. Graph. 26(3), 103 (2007).
[CrossRef]

S. Bae, S. Paris, and F. Durand, “Two-scale tone management for photographic look,” ACM Trans. Graph. 25, 637–645 (2006).
[CrossRef]

S. Paris, S. W. Hasinoff, and J. Kautz, “Local Laplacian filters: edge-aware image processing with a Laplacian pyramid,” in ACM SIGGRAPH (ACM, 2011).

Pattanaik, S. N.

S. N. Pattanaik, J. A. Ferwerda, M. D. Fairchild, and D. P. Greenberg, “A multiscale model of adaptation and spatial vision for realistic image display,” in ACM SIGGRAPH (ACM, 1998), pp. 287–298.

Peli, E.

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M. Kolomenkin, I. Shimshoni, and A. Tal, “Prominent field for shape analysis of archaeological artifacts,” in CCV 2009—IEEE 12th International Conference on Computer Vision: Workshops on eHeritage & Digital Art Preservation (IEEE, 2009), pp. 915–923.

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C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in ICCV-1998, Sixth International Conference on Computer Vision (IEEE Computer Society, 1998), pp. 839–846.

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J. Tumblin and G. Turk, “LCIS: a boundary hierarchy for detail-preserving contrast reduction,” in ACM SIGGRAPH (ACM, 1999), pp. 83–90.

P. Choudhury and J. Tumblin, “Trilateral filter for high contrast images and meshes,” in ACM SIGGRAPH (ACM, 2005).

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J. Tumblin and G. Turk, “LCIS: a boundary hierarchy for detail-preserving contrast reduction,” in ACM SIGGRAPH (ACM, 1999), pp. 83–90.

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R. C. Bilcu and M. Vehvilainen, “Constrained unsharp masking for image enhancement,” in International Conference on Image and Signal Processing (Springer-Verlag, 2008), pp. 10–19.

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E. Peli, J. Kim, Y. Yitzhaky, R. B. Goldstein, and R. L. Woods, “Wideband enhancement of television images for people with visual impairments,” J. Opt. Soc. Am. 21, 937–950 (2004).
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Other (23)

T. Malzbender, D. Gelb, and H. Wolters, “Polynomial texture maps,” in ACM SIGGRAPH (ACM, 2001).

M. Kolomenkin, I. Shimshoni, and A. Tal, “Prominent field for shape analysis of archaeological artifacts,” in CCV 2009—IEEE 12th International Conference on Computer Vision: Workshops on eHeritage & Digital Art Preservation (IEEE, 2009), pp. 915–923.

A. Kharlamov and V. Podlozhnyuk, “Image denoising,” NVIDIA Technical Report (NVIDIA, 2007).

D. Shaked and I. Tastl, “Sharpness measure: towards automatic image enhancement,” in ICIP--International Conference on Image Processing (IEEE, 2005).

J. Caviedes and S. Gurbuz, “No-reference sharpness metric based on local edge kurtosis” in ICIP--International Conference on Image Processing (IEEE, 2002).

P. Choudhury and J. Tumblin, “Trilateral filter for high contrast images and meshes,” in ACM SIGGRAPH (ACM, 2005).

E. P. Krotkov, Active Computer Vision by Cooperative Focus and Stereo (Springer-Verlag, 1989).

J. M. Tenenbaum, “Accommodation in computer vision,” Ph.D. thesis (Stanford, 1971).

R. C. Bilcu and M. Vehvilainen, “Constrained unsharp masking for image enhancement,” in International Conference on Image and Signal Processing (Springer-Verlag, 2008), pp. 10–19.

S. N. Pattanaik, J. A. Ferwerda, M. D. Fairchild, and D. P. Greenberg, “A multiscale model of adaptation and spatial vision for realistic image display,” in ACM SIGGRAPH (ACM, 1998), pp. 287–298.

Y. Li, L. Sharan, and E. H. Adelson, “Compressing and companding high dynamic range images with subband architectures,” in ACM SIGGRAPH (ACM, 2005), pp. 836–844.

R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” in ACM SIGGRAPH (ACM, 2002), pp. 249–256.

Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” in ACM SIGGRAPH (ACM, 2008).

C. Hentschel and D. La Hei, “Effective peaking filter and its implementation on a programmable architecture,” in ICCE (IEEE Computer Society, 1999), pp. 330–331.

J. Tumblin and G. Turk, “LCIS: a boundary hierarchy for detail-preserving contrast reduction,” in ACM SIGGRAPH (ACM, 1999), pp. 83–90.

R. Fattal, M. Agrawala, and S. Rusinkiewicz, “Multi scale shape and detail enhancement from multi-light image collections,” in ACM SIGGRAPH (ACM, 2007).

F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” in ACM SIGGRAPH (ACM, 2002), pp. 257–266.

C. Hentschel, “Video moire cancellation filter for high-resolution crts,” in ICCE (IEEE Computer Society, 1999), pp. 200–201.

A. Choudhury and G. Medioni, “Perceptually motivated automatic sharpness enhancement using hierarchy of non-local means,” in ICCV 2011—Proceedings of Color and Photometry in Computer Vision Workshop (IEEE Computer Society, 2011), pp. 730–737.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in ICCV-1998, Sixth International Conference on Computer Vision (IEEE Computer Society, 1998), pp. 839–846.

K. Subr, C. Soler, and F. Durand, “Edge-preserving multiscale image decomposition based on local extrema,” in ACM SIGGRAPH Asia (ACM, 2009).

S. Paris, S. W. Hasinoff, and J. Kautz, “Local Laplacian filters: edge-aware image processing with a Laplacian pyramid,” in ACM SIGGRAPH (ACM, 2011).

L. Xu, C. Lu, Y. Xu, and J. Jia, “Image smoothing via L0 gradient minimization,” in SIGGRAPH Asia (ACM, 2011), pp. 1–12.

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Figures (24)

Fig. 1.
Fig. 1.

Sharpness enhancement. Image (a) is from Farbman et al. [1], © ACM. The details increase from left to right with our method.

Fig. 2.
Fig. 2.

Effects of smoothing on noisy images. Column (a) is the noisy image. For columns (b)–(f), the top row is the smooth image whereas the bottom row is the method noise. Column (b) uses Gaussian filter with σc=5. Column (c) uses a bilateral filter with σc=5 and σs=0.05. Column (d) uses a bilateral filter with σc=5 and σs=0.5. Column (e) uses a WLS filter with α=0.25 and λ=1.2. Column (f) uses NL-means filter with h=0.03. The method noise is normalized for better visibility.

Fig. 3.
Fig. 3.

Flowchart of our method.

Fig. 4.
Fig. 4.

Filtering using edge information. The top row is the smooth image and the bottom row is the method noise. The red rectangle is zoomed for clarity. (a) uses NL-means filter with high value of h=0.5 and no edge information. (b) uses edge and varying h. (c) uses bilateral filter along with edge and varying σ. Though most regions are smooth and there are no edges in the method noise due to low σ, noise can be seen along the edges of smooth image.

Fig. 5.
Fig. 5.

Sharpness enhancement. Note the increase in sharpness from left to right.

Fig. 6.
Fig. 6.

Abstractions at different spatial scales by changing the filtering parameter h. (a) is original image. (b) h=0.01 at level 1 and (c) h=0.1 at level 1. l1=5 for both enhancements.

Fig. 7.
Fig. 7.

Comparison of enhancements. Details in the bottom image are more clearly visible than in the top image. Top image is from Fatal et al. [9], © ACM.

Fig. 8.
Fig. 8.

Gradient reversal. A red arrow shows the gradient reversals along the cloud boundaries in the top image (from Fattal et al. [9], © ACM]) (zoomed in the inset) and the lack of it in the bottom image.

Fig. 9.
Fig. 9.

Enhancement with Photoshop’s unsharp mask. Note the halo effect along the boundary of the flower. (Original image from Farbman et al. [1], © ACM.)

Fig. 10.
Fig. 10.

Enhanced flower from Subr et al. [14], © ACM. Note subtle halo effects (white color) along the boundary of flower and leaves and the lack of it using our method [Fig. 1(c)].

Fig. 11.
Fig. 11.

Decomposition using different approaches. The left half of the image is the smooth image and the right half is the corresponding fine level. For every subfigure, the top image is the finest level and the bottom image is the smoothest level. (a) Bilateral filter Chen et al. [11], © ACM. (b) LCIS Tumblin and Turk [8], © ACM. (c) Trilateral filter Choudhury and Tumblin [32], © ACM. (d) Fattal et al. [9], © ACM. (e) WLS, Farbman et al. [1], © ACM. (f) Iterative WLS, Farbman et al. [1], © ACM. (g) Bilateral (with segmentation). (h) NL-means (no segmentation). (i) Our technique. Images (a)–(f) are from [1], © ACM. Zoom-in for better visualization.

Fig. 12.
Fig. 12.

Correlation between the ranks assigned using Tenengrad criterion and the ranks assigned by human observers for (left) image with the best average ρ (ρ=0.9714) and (right) image with the worst average ρ (ρ=0.9524) across all observers. The identity line in black, y=x, depicts perfect agreement between the different ranks.

Fig. 13.
Fig. 13.

Tenengrad values on nine images. (Top) Shows responses marked by circles (preferred images) and squares (transition to “too-detailed” images). The size of the circles and squares correspond to the number of responses. (Bottom) Shows convergence of Tenengrad criterion. All 28 images are not included for clarity.

Fig. 14.
Fig. 14.

SPI values on our dataset. The “too much detail” region extends till SPI=1. The blue horizontal line shows mean SPI for the train preferred images. The red horizontal line shows mean SPI for train images with too much detail.

Fig. 15.
Fig. 15.

(Left) Preference for our selection. (Right) Histogram of mean preference ratings.

Fig. 16.
Fig. 16.

Az’s for all observers.

Fig. 17.
Fig. 17.

Automatic sharpness enhancement of images. Left are the original images and right are the enhanced images. The box represents the preferred images according to our metric. For the first image, the enhanced image (l1=3) is preferred, whereas for the second image the original image (l1=1) is preferred. The enhancements are better visible at the original high resolution.

Fig. 18.
Fig. 18.

Comparison of enhancements using SPI. The “optimal” region is from SPI[0.23,0.36].

Fig. 19.
Fig. 19.

(Left) Tone-mapped results and (right) close-up. Both methods use the same tone mapping algorithm. Note the lack of halos around picture frames and light fixture and better color balance in the close up of the bottom image.

Fig. 20.
Fig. 20.

(Top row) Exaggerated tone-mapped results using different techniques of an HDR image. (Bottom row) Close-up of a part of the image. Halos are shown in the close-ups of the images. Note the presence of halo effects in (a) and (c) and the lack of them in (b) and (d). Images (a)-(c) are from Farbman et al. [1], Paris et al. [19], and Li et al. [25], respectively, all © ACM.

Fig. 21.
Fig. 21.

Close-up of tone-mapped image by (left) Fattal [22], © ACM and (right) our method. A red arrow shows the irregular edge generated in the left image due to aliasing and the lack of it in the right image.

Fig. 22.
Fig. 22.

Tone-mapping of noisy image. Note the relatively lower amplification of noise using our method. Top image is from Farbman et al. [1], © ACM.

Fig. 23.
Fig. 23.

Enhancement of eroded artifacts. (a) is the original image from Kolomenkin et al. [43]. Used with permission, © IEEE. (b) is the enhanced image from Kolomenkin et al. [43]. Used with permission, © IEEE. (c) is enhanced by our method. The structure beneath some weathered regions are clearly visible in (c) as compared to (b).

Fig. 24.
Fig. 24.

Power spectrum characteristics. The left image is the power spectrum of Fig. 1(a) and the right image is that of Fig. 1(b). Note the magnification of high frequencies in the right image.

Equations (8)

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

NLu(x)=1N(x)e(Gρ*|u(x+.)u(y+.)|2)(0)h2u(y)dy,
J=I+R,
I=s+i=1kfi,
fi=ni1NLhi(n0),
E=l0.s+i=1kli.fi,
T=x,y(gradx2+grady2)n,
ρ=16i=1ndi2n(n21),
SPI=Image Tenengrad CriterionConvergent Tenengrad Criterion,

Metrics