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IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 79 - Number 9
Year of Publication: 2013
Jaya V. L
R. Gopikakumari

Jaya V L and R Gopikakumari. Article: IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements. International Journal of Computer Applications 79(9):1-9, October 2013. Full text available. BibTeX

	author = {Jaya V. L and R. Gopikakumari},
	title = {Article: IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {79},
	number = {9},
	pages = {1-9},
	month = {October},
	note = {Full text available}


Evaluation of images, after processing, is an important step for determining how well the images are being processed. Quality of image is usually assessed using image quality metrics. Unfortunately, most of the commonly used metrics cannot adequately describe the visual quality of the enhanced image. There is no universal measure, which specifies both the objective and subjective validity of the enhancement for all types of images. This paper is a study of the various quantitative metrics for enhancement against changes in contrast and sharpness of both general and medical images. A new metric is proposed that is useful for measuring the improvement in contrast as well as sharpness. It is computationally simple and can be used for all types of images.


  • Fsim matlab code [online]. available :. http://www. comp. polyu. edu. hk/ ˜cslzhang/IQA/FSIM/FSIM. htm.
  • Rfsim matlab code [online]. available:. http://www. sse. tongji. edu. cn/linzhang/IQA/RFSIM/RFSIM. htm.
  • S. S. Agaian, K. P. Lentz, and A. M. Grigoryan. A new measure of image enhancement. In IASTED 2000: Proceedings of the Int. Conf. Signal Processing and Communication, pages 19–22, 2000.
  • S. S. Agaian, K. Panetta, and A. Grigoryan. Transform based image enhancement with performance measure. IEEE Tran. Image Processing, 10:367–381, March 2001.
  • S. S. Agaian, B. Silver, and K. A. Panetta. Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process, 16:741758, Mar. 2007.
  • Ali A. Al-Zuky, Salema S. Salman, and Anwar H. Al-Saleh. Study the quality of image enhancement by using retinex technique which capture by different lighting (sun and tungsten). International Journal of Computer Applications, 73:31–38, July 2013. Published by Foundation of Computer Science, New York, USA.
  • B. Bechara, C. A. McMahan, W. S. Moore, M. Noujeim, H. Geha, and F. B. Teixeira. Contrast-to-noise ratio difference in small field of view cone beam computed tomography machines. Journal of Oral Science, 54:227–232, 2012.
  • D. M. Chandler and S. S. Hemami. Vsnr: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process, 16:2284–2298, Sep. 2007.
  • Deniz Erdogmus, Erik G Larsson, Rui Yan, Jose C Principe, and Jeffrey R Fitzsimmons. Measuring the signal-to-noise ratio in magnetic resonance imaging: a caveat. Signal processing, 84:1035–1040, 2004.
  • B. Girod. Whats wrong with mean-squared error?, chapter Digital Images and Human Vision. Cambridge, MA: MIT Press, 1993.
  • R. M Haralick. Statistical and structural approaches to texture. Proceedings of the IEEE, 67:786–804, 1979.
  • Yong Hu, Chun xia Zhao, and Hong nan Wang. Directional analysis of texture images using gray level co-occurrence matrix. In PACIIA '08: Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pages 277 – 281, 2008.
  • ITU-T Recommendation,. Methods for subjective determination of transmission quality. , 1996.
  • E. C. Larson and D. M. Chandler. Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19:1–21, March 2010.
  • M. Gaubatz. Metrix mux visual quality assessment package'[online] available :. http://www. foulard. ece. cornell. edu/gaubatz/metrix mux.
  • Ankita Pandey, Sarbjeet Singh, and Ankur Pandey. Review : On performance metrics for quantitative evaluation of contrast enhancement in mammograms. International Journal of Computer Applications, 75:40–45, August 2013. Published by Foundation of Computer Science, New York, USA.
  • K Panetta, S Agaian, Yicong Zhou, and E. J. Wharton. Parameterized logarithmic framework for image enhancement. IEEE transactions on Systems, Man, Cybernetics, 41:460–473, 2011.
  • K. A. Panetta, E. J. Wharton, and S. S. Agaian. Human visual system based image enhancement and logarithmic contrast measure. IEEE transactions on Systems, Man, Cybernetics, 38:174188, Feb. 2008.
  • K. A. Panetta, Yicong Zhou, S. S. Agaian, and Hongwei Jia. Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. on information technology in biomedicine, 15:918 – 928, Nov. 2011.
  • Planitz and A. Maeder. Medical image watermarking: A study on image degradation. In APRS '05: Proceedings of Australian Pattern Recognition Society(APRS) Workshop on Digital Image Computing, pages 8–13. U. S. A. IEEE Society, Feb. 2005.
  • Schmidt, R. L. Haskell, B. G. Eng, and K. Y. O'Riordan. An experimental time-compression system for satellite television transmission. Proceedings of the IEEE, 73:789–794, 1995.
  • S. D. Chen and A. R. Ramli. Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics, 49:1310–1319, 2003.
  • H. R. Sheikh and A. C. Bovik. Image information and visual quality. IEEE Transactions on Image Processing, 15:430– 444, Feb. 2006.
  • H. R. Sheikh, A. C. Bovik, and G. de Veciana. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. IP, 14:2117–2128, Dec. 2005.
  • J. Sijbers, P. Scheunders, N. Bonnet, D. Van Dyck, and E. Raman. Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure. Magn. Reson. Imag, 14:11571163, 1996.
  • N. Thakur and S. Devi. A new method for color image quality assessment. Internatinal Journal of Computer Applications, 15:10–17, Feb. 2011. Published by Foundation of Computer Science, New York, USA.
  • N. Damera Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik. Image quality assessment based a on degradation model. IEEE Trans. IP, 4:636–650, 2000.
  • Z. Wang and A. C. Bovik. A universal image quality index. IEEE Signal Proces. Lett, 4:81–84, Sept. 2002.
  • Z. Wang and A. C. Bovik. Modern image quality assessment, chapter Introduction, pages 1–15. Morgan and Claypool, New York, 2006.
  • Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process, 13:600–612, 2004.
  • L. Zhang, L. Zhang, and X. Mou. Rfsim: A feature based image quality assessment metric using riesz transforms. In ICIP '10: Proceedings of IEEE Internatinal conference on image processing, pages 321–324, Hong Kong, 2010.
  • L. Zhang, L. Zhang, X. Mou, and D. Zhang. Fsim: a feature similarity index for image quality assessment. IEEE Trans. Image Process, 20:2378–2386, Sep. 2011.
  • Yicong Zhou, Karen Panetta, and S. S. Agaian. Human visual system based mammogram enhancement and analysis. In IPTA, '10: 2nd International Conference on Image Processing Theory Tools and Applications, pages 229–234, 2010.