Paper Titles
New Hybrid Blind Equalization Algorithms
p.1810
A New Image De-Noising Algorithm Based on Wavelet Transform
p.1816
Extraction of Mountain Feature Line Based on 3D Laser Scanning
p.1821
A Real-Time Sea-Sky-Line Detection Method under Complicated Sea-Sky Background
p.1826
A Image Enhancement Algorithm Combined Wavelet Transform with Image Fusion
p.1832
Color Image Encryption Based on the Multiple-Order Discrete Fractional Cosine Transform and Chaos in YCbCr Space
p.1839
Histogram Equalization Based on Rough Set
p.1844
Relaxed Parallel Scheduling Algorithms Investigation of Support Resource Reservation from Grid
p.1849
Iterated Hybrid Localization Algorithm for Random Wireless Sensor Networks Based on Centroid and DV-Hop Algorithm
p.1854

A Image Enhancement Algorithm Combined Wavelet Transform with Image Fusion

Article Preview

Abstract:

Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. The algorithm utilizes wavelet-based image fusion to accomplish the duplex enhancement task. Experiment results prove that the proposed enhancement algorithm can efficiently combine the merits of histogram equalization and sharpening, improving both the contrast and sharpness of the degraded image at the same time.

You might also be interested in these eBooks
Applied Mechanics and Mechatronics Automation View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 182-183)

Pages:

1832-1838

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.182-183.1832

Citation:

Cite this paper

Online since:

June 2012

Authors:

Sheng Min Luo

Keywords:

Image Enhancement, Image Fusion, Wavelet Transform (WT)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] Z. Zhang and R. S. Blum, A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application, Proc. IEEE, vol. 87(8), pp.1315-1326, (1999).

DOI: 10.1109/5.775414

Google Scholar

[2] V. Petovic and T. Cootes. Objectively adaptive image fusion, Information Fusion, vol. 8(2), pp.168-176, April (2007).

DOI: 10.1016/j.inffus.2005.10.002

Google Scholar

[3] G. Pajares and J. Manuel de la Cruz. A wavelet-based image fusion tutorial, Pattern Recognition, vol. 37(9), pp.1855-1872, September (2004).

DOI: 10.1016/j.patcog.2004.03.010

Google Scholar

[4] S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11(7), pp.674-693, (1989).

DOI: 10.1109/34.192463

Google Scholar

[5] S. G. Mallat, A wavelet tour of signal processing, San Diego: Academic Press, 1998, pp.302-310. X. G. Lu, Y. F. Wang, W. G. Hu and A. L. Jin, Investigation of Modification at Histogram Equalization Algorithm Based on Visual Characteristic, Control & Automation, vol. 22(5-1), pp.229-231, (2006).

Google Scholar

[6] C. C. Liu, S. B. Hu, J. H. Yang and X. Guo, A Method of Histogram Incomplete Equalization, Journal of Shandong University (Engineering Science), vol. 33(6), pp.661-664, (2003).

Google Scholar

[7] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed., Publishing House of Electronics Industry, 2002, pp.88-94.

Google Scholar

[8] G. Q. Tao, D. P. Li and G. H. Lu, Study on Image Fusion Based on Different Fusion Rules of Wavelet Transform, Infrared and Laser Engineering, vol. 32(2), pp.173-176, (2003).

Google Scholar

[9] Z. X. Qiang, J. X. Peng and H. Q. Wang, Remote Sensing Image Fusion Based on Local Deviation of Wavelet Transform, Huazhong Univ. of Sci. & Tech. (Nature Science Edition), vol. 31(6), pp, 89-91, (2003).

Google Scholar

[10] B. J. Tao, J. R. Wang and J. P. Xu, Study on image fusion based on different fusion rules of wavelet transform, Infrared Technology, vol. 28(7), pp.431-434, (2006).

Google Scholar