Fuzzy Based Brightness Compensation for High Dynamic Range Images

Annamária R. Várkonyi-Kóczy; András Rövid; Péter Várlaki

doi:10.20965/jaciii.2006.p0549

single-jc.php

« previous

JACIII Vol.10 No.4 pp. 549-554

doi: 10.20965/jaciii.2006.p0549

(2006)

Paper:

Views over last 60 days: 353

Fuzzy Based Brightness Compensation for High Dynamic Range Images

Annamária R. Várkonyi-Kóczy^, András Rövid^,
and Péter Várlaki^**

^*Dept. of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok körútja 2. H-1117 Budapest, Hungary

^**Dept. of Mathematics, Széchenyi István University, Egyetem tér 1. 9026 Győr, Hungary

Received:

September 24, 2005

Accepted:

January 25, 2006

Published:

July 20, 2006

Keywords:

image reproduction, high dynamic range images, tone reproduction, image processing, fuzzy techniques

Abstract

High dynamic range of illumination may cause serious distortions and other problems in viewing and further processing of digital images. In this paper a new fuzzy based tone reproduction pre-processing algorithm is introduced which may help in developing hardly or nonviewable features and content of the images making easier the further processing of it.

Cite this article as:

A. Várkonyi-Kóczy, A. Rövid, and P. Várlaki, “Fuzzy Based Brightness Compensation for High Dynamic Range Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.4, pp. 549-554, 2006.

Data files:

References

[1] F. Russo, “Fuzzy Filtering of Noisy Sensor Data,” Proc. of the IEEE Instrumentation and Measurement Technology Conference, Brussels, Belgium, pp. 1281-1285, June 4-6, 1996.
[2] F. Russo, “Recent Advances in Fuzzy Techniques for Image Enhancement,” IEEE Transactions on Instrumentation and Measurement, Vol.47, No.6, pp. 1428-1434, Dec., 1998.
[3] E. H. Adelson, and A. P. Pentland, “The Perception of Shading and Reflectance,” D. Knill and W. Richards (eds.), Perception as Bayesian Inference, New York: Cambridge University Press, pp. 409-423, 1996.
[4] E. H. Adelson, “Lightness perception and lightness illusions,” The Cognitive Neurosciences, 2nd ed., Cambridge, MA: MIT Press, pp. 339-351, 2000.
[5] A. Gilchrist, C. Kossyfidis, F. Bonato, T. Agostini, J. Cataliotti, X. Li, B. Spehar, V. Annan, and E. Economou, “An anchoring theory of lightness perception,” Psychol. Rev., Vol.106, No.4, pp. 795-834, Oct., 1999.
[6] M. E. Rudd, and I. K. Zemach, “The highest luminance anchoring rule in lightness perception,” Journal of Vision, Vol.3, No.9, 56a, 2003.
[7] J. Theiler, and G. Gisler, “A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation,” Proc SPIE 3159, pp. 108-118, 1997.
[8] G. Ward, “A contrast-based scalefactor for luminance display,” Graphics Gems IV, P. Heckbert, Ed. Academic Press, Boston, pp. 415-421, 1994.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] F. Russo, “Fuzzy Filtering of Noisy Sensor Data,” Proc. of the IEEE Instrumentation and Measurement Technology Conference, Brussels, Belgium, pp. 1281-1285, June 4-6, 1996.

[2] [2] F. Russo, “Recent Advances in Fuzzy Techniques for Image Enhancement,” IEEE Transactions on Instrumentation and Measurement, Vol.47, No.6, pp. 1428-1434, Dec., 1998.

[3] [3] E. H. Adelson, and A. P. Pentland, “The Perception of Shading and Reflectance,” D. Knill and W. Richards (eds.), Perception as Bayesian Inference, New York: Cambridge University Press, pp. 409-423, 1996.

[4] [4] E. H. Adelson, “Lightness perception and lightness illusions,” The Cognitive Neurosciences, 2nd ed., Cambridge, MA: MIT Press, pp. 339-351, 2000.

[5] [5] A. Gilchrist, C. Kossyfidis, F. Bonato, T. Agostini, J. Cataliotti, X. Li, B. Spehar, V. Annan, and E. Economou, “An anchoring theory of lightness perception,” Psychol. Rev., Vol.106, No.4, pp. 795-834, Oct., 1999.

[6] [6] M. E. Rudd, and I. K. Zemach, “The highest luminance anchoring rule in lightness perception,” Journal of Vision, Vol.3, No.9, 56a, 2003.

[7] [7] J. Theiler, and G. Gisler, “A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation,” Proc SPIE 3159, pp. 108-118, 1997.

[8] [8] G. Ward, “A contrast-based scalefactor for luminance display,” Graphics Gems IV, P. Heckbert, Ed. Academic Press, Boston, pp. 415-421, 1994.

Fuzzy Based Brightness Compensation for High Dynamic Range Images

Annamária R. Várkonyi-Kóczy*, András Rövid*, and Péter Várlaki**

Annamária R. Várkonyi-Kóczy^, András Rövid^,
and Péter Várlaki^**