Home > CSC-OpenAccess Library > Manuscript Information
EXPLORE PUBLICATIONS BY COUNTRIES |
EUROPE | |
MIDDLE EAST | |
ASIA | |
AFRICA | |
............................. | |
United States of America | |
United Kingdom | |
Canada | |
Australia | |
Italy | |
France | |
Brazil | |
Germany | |
Malaysia | |
Turkey | |
China | |
Taiwan | |
Japan | |
Saudi Arabia | |
Jordan | |
Egypt | |
United Arab Emirates | |
India | |
Nigeria |
Image Contrast Enhancement for Brightness Preservation Based on Dynamic Stretching
Md Arifur Rahman, Shilong Liu, S. C. F. Lin, C. Y. Wong, G. Jiang, Ngaiming Kwok
Pages - 241 - 253 | Revised - 31-07-2015 | Published - 31-08-2015
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
KEYWORDS
Image Contrast Enhancement, Histogram Equalization, Brightness Preservation, Golden Section Search.
ABSTRACT
Histogram equalization is an efficient process often employed in consumer electronic systems for image contrast enhancement. In addition to an increase in contrast, it is also required to preserve the mean brightness of an image in order to convey the true scene information to the viewer. A conventional approach is to separate the image into sub-images and then process independently by histogram equalization towards a modified profile. However, due to the variations in image contents, the histogram separation threshold greatly influences the level of shift in mean brightness with respect to the uniform histogram in the equalization process. Therefore, the choice of a proper threshold, to separate the input image into sub-images, is very critical in order to preserve the mean brightness of the output image. In this research work, a dynamic range stretching approach is adopted to reduce the shift in output image mean brightness. Moreover, the computationally efficient golden section search algorithm is applied to obtain a proper separation into sub-images to preserve the mean brightness. Experiments were carried out on a large number of color images of natural scenes. Results, as compared to current available approaches, showed that the proposed method performed satisfactorily in terms of mean brightness preservation and enhancement in image contrast.
C. H. Ooi and N. Isa, “Adaptive contrast enhancement methods with brightness preserving,” Consumer Electronics, IEEE Transactions on, vol. 56, pp. 2543–2551, Nov 2010. | |
C. H. Ooi, N. Kong, and H. Ibrahim, “Bi-histogram equalization with a plateau limit for digital image enhancement,” Consumer Electronics, IEEE Transactions on, vol. 55, pp. 2072– 2080, Nov 2009. | |
F. Su, G. Fang, and N. M. Kwok, “Shadow removal using background reconstruction,” in Image and Signal Processing (CISP), 2012 5th International Congress on, pp. 154–158, Oct 2012. | |
G. Zhang, X. Jia, and N. Kwok, “Super pixel based remote sensing image classification with histogram descriptors on spectral and spatial data,” in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, pp. 4335–4338, July 2012. | |
J. R. Tang and N. A. M. Isa, “Adaptive image enhancement based on bi-histogram equalization with a clipping limit,” Computers and Electrical Engineering, vol. 40, no. 8, pp. 86 – 103, 2014. | |
K. Singh and R. Kapoor, “Image enhancement via median-mean based sub-image-clipped histogram equalization,” Optik - International Journal for Light and Electron Optics, vol. 125, no. 17, pp. 4646 – 4651, 2014. | |
R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006. | |
S.-D. Chen and A. Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement,” Consumer Electronics, IEEE Transactions on, vol. 49, pp. 1310– 1319, Nov 2003. | |
S.-W. Jung, “Image contrast enhancement using color and depth histograms,” IEEE Signal Processing Letters, no. 21, pp. 382–385, 2014. | |
T. Celik and T. Tjahjadi, “Automatic image equalization and contrast enhancement using gaussian mixture modeling,” Image Processing, IEEE Transactions on, vol. 21, no. 1, pp. 145–156, 2012. | |
T. K. Agarwal, M. Tiwari, and S. S. Lamba, “Modified histogram based contrast enhancement using homomorphic filtering for medical images,” in Advance Computing Conference (IACC), 2014 IEEE International, pp. 964–968, IEEE, 2014. | |
T. Wu, H. Wu, Y. Du, N. Kwok, and Z. Peng, “Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features,” Wear, vol. 316, no. 1-2, pp. 19 – 29, 2014. | |
Y. Wang, Q. Chen, and B. Zhang, “Image enhancement based on equal area dualistic subimage histogram equalization method,” Consumer Electronics, IEEE Transactions on, vol. 45, pp. 68–75, Feb 1999. | |
Y.-C. Chang, “N-dimension golden section search: Its variants and limitations,” in Biomedical Engineering and Informatics, 2009. BMEI’09. 2nd International Conference on, pp. 1–6, IEEE, 2009. | |
Y.-J. Lin, C.-T. Shen, C.-C. Lin, and H.-C. Yen, “A fast image fusion algorithm for image stabilization on hand-held consumer electronics,” in Consumer Electronics, 2009. ICCE ’09. Digest of Technical Papers International Conference on, pp. 1–2, Jan 2009. | |
Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” Consumer Electronics, IEEE Transactions on, vol. 43, pp. 1–8, Feb 1997. | |
Mr. Md Arifur Rahman
University of New South Wales Sydney - Australia
md.arifur.rahman056@gmail.com
Mr. Shilong Liu
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Australia - Australia
Mr. S. C. F. Lin
School of Mechanical and Manufacturing Engineering and The University of New South Wales, Australia - Australia
Mr. C. Y. Wong
School of Mechanical and Manufacturing Engineering and The University of New South Wales, Australia - Australia
Mr. G. Jiang
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Australia - Australia
Mr. Ngaiming Kwok
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Australia - Australia
|
|
|
|
View all special issues >> | |
|
|