Contrast Enhanced for Microstructure of Steel Materials and Engine Components

Article Preview

Abstract:

This paper analyses the contrast of qualitative and quantitative of piston and steel microstructure images. A simple discrimination metric (DMHE) is developed to avoid the drawbacks of conventional histogram equalization for gray scale images. The proposed technique uses both global and local information to remap the intensity levels that help to improve the image contrast. The original histogram is divided into sub-histograms with respect to the mean value. Discrimination metrics are used so that high contrast per pixel between real image and upgraded image is obtained. The simulation results show that the proposed method performed well for mechanical component material of piston and steel microstructure images. Parameters like structural similarity index and contrast per pixel are used to analyze the image quality. Keywords-Piston and Steel microstructure, Contrast Enhancement, Two level Histogram Equalization, Discrimination Metric.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 984-985)

Pages:

1375-1379

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Failure Analysis and Prevention, ASM International, Materials park, OH 44073-0002, 11 (2002).

Google Scholar

[2] Chen Hee Ooi, Nor Ashidi Mat Isa, Quardant Dynamic Histogram Equalization, IEEE Trans. Consum. Electron. 56 (2010) 2552 -2559.

DOI: 10.1109/tce.2010.5681140

Google Scholar

[3] R. C. Gonzalez, R. E. Woods, Digital Image Processing, 3rd edition, Prentice Hall, (2002).

Google Scholar

[4] M. Eramian and D. Mould, Histogram equalization using neighborhood metrics, Computer and Robot Vision, the 2nd Canadian Conference on, IEEE CNF, Proceedings, (2005) 397-404.

DOI: 10.1109/crv.2005.47

Google Scholar

[5] Dr. Muna F. Al-Samaraie, A New Enhancement Approach for Enhancing Image of Digital Cameras by Changing the Contrast, Int. J. Adv. Sci. Technol. 32 (2011) 13-22.

Google Scholar

[6] Nyamlkhagva Sengee and Heung-Kook Choi, Contrast Enhancement using Histogram Equalization with a Neighborhood Metrics, J. Korean Multimedia Soc. 11 (2008) 737-745.

Google Scholar

[7] Nyamlkhagva Sengee, Altansukh Sengee, Heung-Kook Choi, Image Contrast Enhancement using Bi-Histogram Equalization with Neighborhood Metrics, IEEE Trans. Consum. Electron. 56 (2010) 2552 -2559.

DOI: 10.1109/tce.2010.5681162

Google Scholar

[8] Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, Proc. IEEE 13 (2004) 600-612.

DOI: 10.1109/tip.2003.819861

Google Scholar

[9] N. Ostu A threshold selection method from gray-level histogram, IEEE Trans. Man and Cyber. (1979) 62-66.

Google Scholar