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Reseach Article

Threshold based Adaptive Power-Law Applications in Image Enhancement

by T. Romen Singh, Sudipta Roy, Kh. Manglem Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 7
Year of Publication: 2012
Authors: T. Romen Singh, Sudipta Roy, Kh. Manglem Singh
10.5120/7202-9983

T. Romen Singh, Sudipta Roy, Kh. Manglem Singh . Threshold based Adaptive Power-Law Applications in Image Enhancement. International Journal of Computer Applications. 47, 7 ( June 2012), 32-40. DOI=10.5120/7202-9983

@article{ 10.5120/7202-9983,
author = { T. Romen Singh, Sudipta Roy, Kh. Manglem Singh },
title = { Threshold based Adaptive Power-Law Applications in Image Enhancement },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 7 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number7/7202-9983/ },
doi = { 10.5120/7202-9983 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:41:17.206925+05:30
%A T. Romen Singh
%A Sudipta Roy
%A Kh. Manglem Singh
%T Threshold based Adaptive Power-Law Applications in Image Enhancement
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 7
%P 32-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a spatial domain threshold based adaptive power-law applications (TAPLA) in image enhancement technique in which adaptation is carried out with local thresholds. This is an improved version of Adaptive Power-law Transformations (APLT) [14] in which adaptation is carried out with local means. The computational time of APLT is window-size dependent to find local mean while the TAPLA is independent of window-size to find local means, which are used to determine the local threshold values. Window-size independent of computational time is due to use of integral average image as prior process to find local mean. Like APLT, TAPLA can control the enhancement factors such as contrast, brightness and sharpness/smoothness with a proper choice of parameters through a single function. This method can be applied on both the grey scale and color images. In the case of color images, each channel is considered separately. TAPLA outperforms better than APLT in image quality as well as in computational time.

References
  1. R. C. Gonzales, R. E. Woods, Digital Image Processing, 2nd Edn. 2005.
  2. S. Mallat, Characterization of signals for multiscale edges, IEEE Trans. Patt. Anal. , Machine Intelligence, vol. PAMI 14, pp. 710-732, 1992.
  3. J. K. Kim, J. M. Park, K. S. Song and H. W. Park, Adaptive mammographic image enhancement using first derivative and local statistics, IEEE Trans. Medical Imaging, vol. 16, iss. 5, pp. 495-502, Oct. 1997.
  4. J. L. Starck, E. Cand es, and D. L. Donoho. The curvelet transform for image denoising. IEEE Transactions on Image Processing, 11(6):131{141, 2002.
  5. Nagesha and G. H. Kumar, A level crossing enhancement scheme for chest radiograph images, Elsevier, Computer in Biology and Medicines, vol. 37, iss. 10, pp. 1455-1460, Oct 2007.
  6. Kuroda, Algorithm and architecture for real time adaptive image enhancement, SiPS 2000, pp. 171-180, 2000.
  7. D. C. Chang and W. R. Wu, Image contrast enhancement based on a histogram transformation of local standard deviation, IEEE Trans. MI, vol. 17, no. 4, pp. 518-531, Aug. 1998.
  8. G. Boccignone and M. Ferraro, Multiscale contrast enhancement, Electron. Lett. , vol. 37, no. 12, pp. 751-752, 2001.
  9. Sarif Kumar Naik and C. A. Murthy "Hue-Preserving Color Image Enhancement Without Gamut Problem", IEEE Transactions On Image Processing, Vol. 12, No. 12, December 2003.
  10. R. N. Strickland, C. S. Kim and W. F. Mcdonnel, Digital color image enhancement based on the saturation component, Opt. Engg, vol. 26, no. 7, pp. 609-616, 1987.
  11. J. S. Lee, "Digital image enhancement and noise filtering by using local statistics," IEEE Trans. Pattern Anal. Machine Intell. , vol. PAMI-2, pp. 165–168, Feb. 1980. .
  12. T. -L. Ji, M. K. Sundareshan, and H. Roehrig, "Adaptive image contrast enhancement based on human visual properties," IEEE Trans. Med. Imag. , vol. 13, pp. 573–586, Aug. 1994.
  13. L. Lucchese, S. K. Mitra, and J. Mukherjee, "A new algorithm based on saturation and desaturation in the xy-chromaticity diagram for enhancement and re-rendition of color images," Proc. IEEE Int. Conf. on ImageProcessing, pp. 1077–1080, 2001.
  14. T. Romen Singh, O. Imocha Singh , Kh. Manglem Singh , Tejmani Sinam and Th. Rupachandra Singh "Image Enhancement by Adaptive Power-Law Transformations", Bahria University Journal of Information and Communication Technology Volume 3 Issue 1 (BUJICT 2010), ISSN 1999-4974.
  15. T. Romen Singh, Sudipta Roy , O. Imocha Singh, Tejmani Sinam, Kh. Manglem Singh "A New Local Adaptive Thresholding Technique in Binarization", IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 2, November 2011 ISSN (Online): 1694-0814.
  16. Bernsen, J. 1986, Dynamic thresholding of gray-level images. Proc. 8th Int. Conf. on Pattern Recognition, Paris, pp 1251-1255.
  17. W. Niblack, 1986 An Introduction to Image Processing,Prince Hall Englewood Cliffs, NJ.
  18. J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition 33(2), pp 255-236,2000.
  19. Jinshan Tang, A contrast based image fusion technique in the DCT domain, Digital Signal Processing 14(2004) 218-226.
  20. Iyad Jafar and Hao Ying , A New Method for Image Contrast Enhancement Based on Automatic Specification of Local Histograms, IJCSNS International Journal of Computer Science and Network Security, VOL. 7, July 2007.
  21. Bei Tang, Guillermo Sapiro ,Color Image Enhancement via Chromaticity Diffusion, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 5,May,2001.
  22. Agaian, S. S, K. Panetta A. M. Grigoryan : A new measure of image enhancement. In: IASTED Int. Conf. Signal Processing Communication, Marbella, Spain, Sep,19-22,2000.
  23. Agaian, S. S. , K. Panetta, A. Grigoryan. : Transform based image enhancement with performance measure. In: IEEE Transactions on Image Processing, vol. 10,No. 3, pp. 367-381, March,2001.
  24. Agaian, Sos S. , Blair Silver, Karen A. Panetta. : Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy. In: IEEE Transactions on Image Processing, vol. 16, No. 3,March, 2007.
  25. Silver, B. , S. S. Agaian, K. A. Panetta. : Logarithmic transform coefficient Histogram matching with spatial equalization. In: SPIE Defence and Security Symposium, Mar. 2005.
  26. Narasimhan K, Sudarshan C R and Nagarajan Raju, A Comparison of Contrast Enhancement Techniques in Poor Illuminated Gray Level and Color Images, International Journal of Computer Applications (0975 – 8887) Volume 25– No. 2, July 2011.
  27. Sos Agaian, Blair Silver?, and Karen Panetta,, Transform Coefficient Histogram Based Image Enhancement Algorithms using Contrast Entropy, TIP-01692-2005.
  28. Konstantinos G. Derpanis," Integral image-based representations", Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Computer Vision and Pattern Recognition (pp. I:511–518).
  29. Dah-Chung Chang* and Wen-Rong Wu, Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 17, NO. 4, AUGUST 1998.
  30. Sascha D. Cvetkovic*?, Johan Schirris*, Peter H. N. de With?, Locally-Adaptive Image Contrast Enhancement Without Noise And Ringing Artifacts , 1-4244-1437-7/07/$20. 00 ©2007 IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Power-law Image Enhancement Contrast Transformations Image Sharpening Artifact Integral Average Image