Abstract
Conventional contrast enhancement methods are application-oriented and they need transformation functions and parameters which are specified manually. Furthermore, most of them do not produce satisfactory enhancement results for certain types of color images: dark, low-contrast, bright, mostly dark, high-contrast, and mostly bright. Thus, this paper proposes a decision tree-based contrast enhancement algorithm to enhance the above described color images simultaneously. This method includes three steps: first, statistical image features are extracted from the luminance distribution. Second, a decision tree-based classification is proposed to divide the input images into dark, low-contrast, bright, mostly dark, high-contrast, and mostly bright categories. Finally, these image categories are handled by piecewise linear based enhancement method. This novel enhancement method is automatic and parameter-free. Our experiments included different color and gray images. Experimental results show that the performance of the proposed enhancement method is better than other available methods in skin detection, visual perception, and image subtraction measurements.
Similar content being viewed by others
References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002). http://www.imageprocessingplace.com
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Prentice-Hall, Englewood Cliffs (2004). http://www.imageprocessingplace.com
Naik S.K., Murthy C.A.: Hue-preserving color image enhancement without gamut problem. IEEE Trans. Image Process. 12(12), 1591–1598 (2003)
Strickland R.N., Kim C.S., Mcdonnell W.F.: Digital color image enhancement based on the saturation component. Opt. Eng. 26(7), 609–616 (1987)
Thomas, B.A., Strickland, R.N., Rodriguez, J.J.: Color image enhancement using spatially adaptive saturation feedback. In: Proceedings of the International Conference on Image Processing, vol. 3, pp. 30–33 (1997)
Yang C.C., Rodriguez J.J.: Efficient luminance and saturation processing techniques for color images. J. Vis. Commun. Image Represent. 3(3), 263–277 (1997)
Yang C.C., Kwork S.H.: Efficient gamut clipping for color image processing using LHS and YIQ. Opt. Eng. 42(3), 701–711 (2003)
Pei S.C., Zeng Y.C., Chang C.H.: Virtual restoration of ancient Chinese paintings using color contrast enhancement and Lacuna texture synthesis. IEEE Trans. Image Process. 13(3), 416–429 (2004)
Tan K.K., Oakley J.P.: Physics-based approach to color image enhancement in poor visibility conditions. J. Opt. Soc. Am. A 18(10), 2460–2467 (2001)
Hanmandlu, M., Jha, D., Sharma, R.: Color image enhancement by fuzzy intensification. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 3, pp. 310–313 (2000)
Mlsna P.A., Rodriguez J.J.: A multivariate contrast enhancement technique for multispectral images. IEEE Trans. Geosci. Remote Sens. 33(1), 212–216 (1995)
Duan, J., Qiu, G.: Novel histogram processing for colour image enhancement. In: Proceedings of the Third International Conference on Image and Graphics, pp. 55–58 (2004)
Sun C.C., Ruan S.J., Shie M.C., Pai T.W.: Dynamic contrast enhancement based on histogram specification. IEEE Trans. Consumer Electron. 51(4), 1300–1305 (2005)
Chatterji, B.N., Murthy, N.R.: Adaptive contrast enhancement for color images. In: Proceedings of 1997 International Conference on Information, Communications and Signal Processing, vol. 3, pp. 1537–1541 (1997)
Meylan, L., Süsstrunk, S.: Color image enhancement using a retinex-based adaptive filter. In: Proceedings of the IS&T Second European Conference on Color in Graphics, Image, and Vision (CGIV 2004), vol. 2, pp. 359–363 (2004)
Munteanu, C., Rosa, A.: Color image enhancement using evolutionary principles and the retinex theory of color constancy. In: Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop, pp. 393–402 (2001)
Tsai C.M., Lee H.J.: Binarization of color document images via luminance and saturation color features. IEEE Trans. Image Process. 11(4), 434–451 (2002)
Tsai D.M.: A fast thresholding selection procedure for multimode and unimodel histograms. Pattern Recognit. Lett. 16, 653–666 (1995)
Asayama Y., Miyamoto S., Oi K., Ikebe Y.: Least square method for enhancement of laser radar images based on piecewise linear transformations of gray scales, Acoustics, Speech, and Signal Processing. IEEE Int. Conf. ICASSP ‘86 11, 1513–1516 (1986)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 3(6), 643–660 (2001). http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html
Wong K.W., Lam K.M., Siu W.C.: A robust scheme for live detection of human faces in color images. Signal Process. Image Commun. 18(2), 103–114 (2003)
Adobe Systems Incorporated, Adobe Photoshop CS. http://www.adobe.com/products/photoshop/main.html?c=us
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tsai, CM., Yeh, ZM. & Wang, YF. Decision tree-based contrast enhancement for various color images. Machine Vision and Applications 22, 21–37 (2011). https://doi.org/10.1007/s00138-009-0223-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-009-0223-x