International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Removing Artifact from DEFTH Histogram and Contrast Modification

Author : Miss. Kulkarni Rohini Gopalrao ¹ Prof. Gajare Yogita R ²

Date of Publication :7th July 2016

Abstract: This work is concerned with the modification of the gray level or color distribution of digital images A common drawback of classical methods is that it allows large number of artifacts or the attenuation of details and textures. In this work, we propose a generic filtering method enabling, given the original image and the radio metrically corrected one, to suppress artifacts while preserving details. The approach relies on the key observation that artifacts correspond to spatial irregularity of the so called transportation map, defined as the difference between the original and the corrected image. Then Transportation map which is the difference between original image and transformed image is calculated, then a generic filtering method also called TMR filter which draws on the nonlocal Yaroslavsky filter is used to regularize the transportation map so that artifacts are suppressed . On the basis of the histogram-modification framework, the color and depth image histograms are first partitioned into subintervals using the Gaussian mixture model. The positions partitioning the color histogram are then adjusted such that spatially neighboring pixels with the similar intensity and depth values can be grouped into the same sub-interval. By estimating the mapping curve of the contrast enhancement for each subinterval, the global image contrast can be improved without overenhancing the local image contrast.

Reference :

1. A. C. Bovik, Handbook of Image and Video Processing (Communications, Networking and Multimedia). Orlando, FL: Academic, 2005
2. R. H. Selzer, “The use of computers to improve biomedical image quality,” in Proc. AFIPS, 1968, pp. 817– 834.
3. J. Delon, “Midway image equalization,” J. Math. Imaging Vis., vol. 21, no. 2, pp. 119–134, Sep. 2004.
4. N. Papadakis, E. Provenzi, and V. Caselles, “A variational model for histogram transfer of color images,” IEEE Trans. Image Process., vol.19, no. 11, p. , Nov. 2010.
5. J. Delon, “Movie and video scale-time equalization application to flicker reduction,” IEEE Trans. Image Process., vol. 15, no. 1, pp.241–248, Jan. 2006.
6. J. Delon and A. Desolneux, “Stabilization of flicker-like effects in image sequences through local contrast correction,” SIAM J. Imaging Sci., vol. 3, no. 4, pp. 703– 704, Oct. 2010.
7. G. Haro, M. Bertalmío, and V. Caselles, “Visual acuity in day for night,” Int. J. Comput. Vis., vol. 69, no. 1, pp. 109–117, 2006.
8. M. Bertalmío, V. Caselles, E. Provenzi, and A. Rizzi, “Perceptual color correction through variational techniques,” IEEE Trans. Image Process., vol. 16, no. 4, pp. 1058–1072, Apr. 2007.
9. S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” Int. J. Comput. Vis., vol. 48, no. 3, pp. 233– 254, 2002.
10. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. T.H. Romeny, and J. B. Zimmerman, â€•Adaptive histogram equalization and its variations,â€– Comput. Vis. Graph. Image Process., vol. 39, no. 3, pp. 355–368, 1987.
11. N. Papadakis, E. Provenzi, and V. Caselles, â€•A variational model for histogram transfer of color images,â€– IEEE Trans. Image Process., vol.19, no. 11, p. , Nov. 2010.
12. Y.-W. Tai, J. Jia, and C.-K. Tang, â€•Local color transfer via probabilistic segmentation by expectationmaximization,â€– in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 747–754
13. A. Abadpour and S. Kasaei, â€•An efficient PCA-based color transfer method,â€– J. Vis. Commun. Image Represent., vol. 18, no. 1, pp. 15–34, 2007.
14. F. Alter, S. Durand, and J. Froment, â€•Adapted total variation for artifact free decompression of jpeg images,â€– J. Math. Imaging Vis., vol. 23, pp. 199–211, 2005.
15. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, Greer, B. T. H. Romeny, and J. B. Zimmerman, “Adaptive histogram equalization and its variations,” Comput. Vis. Graph. Image Process., vol. 39, no. 3, pp. 355–368, 1987.
16. N. Papadakis, E. Provenzi, and V. Caselles, “A variational model for histogram transfer of color images,” IEEE Trans. Image Process., vol. 19, no. 11, p. , Nov. 2010.
17. Y.-W. Tai, J. Jia, and C.-K. Tang, “Local color transfer via probabilistic segmentation by expectationmaximization,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 747–754
18. A. Abadpour and S. Kasaei, “An efficient PCA-based color transfer method,” J. Vis. Commun. Image Represent., vol. 18, no. 1, pp. 15–34, 2007.
19. F. Alter, S. Durand, and J. Froment, “Adapted total variation for artifact free decompression of jpeg images,” J. Math. Imaging Vis., vol. 23, pp. 199–211, 2005.
20. T. Arici, S. Dikbas, and Y. Altunbasak, “A histogram modification framework and its application for image contrast enhancement,” IEEE Trans. Image Process., vol. 18, no. 9, pp. 1921–1935, Sep. 2009.
21. T. Celik and T. Tjahjadi, “Automatic image equalization and contrast enhancement using Gaussian mixture modeling,” IEEE Trans. Image Process., vol. 21, no. 1, pp. 145–156, Jan. 2012
22. M. Abdullah-Al-Wadud, Md. H. Kabir, M. A. A. Dewan, and O. Chae, “A dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electron., vol. 53, no. 2, pp. 593–600, May 2007.
1. A. C. Bovik, Handbook of Image and Video Processing (Communications, Networking and Multimedia). Orlando, FL: Academic, 2005
2. R. H. Selzer, “The use of computers to improve biomedical image quality,” in Proc. AFIPS, 1968, pp. 817– 834
3. J. Delon, “Midway image equalization,” J. Math. Imaging Vis., vol. 21, no. 2, pp. 119–134, Sep. 2004
4. N. Papadakis, E. Provenzi, and V. Caselles, “A variational model for histogram transfer of color images,” IEEE Trans. Image Process., vol.19, no. 11, p. , Nov. 2010
5. J. Delon, “Movie and video scale-time equalization application to flicker reduction,” IEEE Trans. Image Process., vol. 15, no. 1, pp.241–248, Jan. 2006.
6. J. Delon and A. Desolneux, “Stabilization of flicker-like effects in image sequences through local contrast correction,” SIAM J. Imaging Sci., vol. 3, no. 4, pp. 703– 704, Oct. 2010.
7. G. Haro, M. Bertalmío, and V. Caselles, “Visual acuity in day for night,” Int. J. Comput. Vis., vol. 69, no. 1, pp. 109–117, 2006.
8. M. Bertalmío, V. Caselles, E. Provenzi, and A. Rizzi, “Perceptual color correction through variational techniques,” IEEE Trans. Image Process., vol. 16, no. 4, pp. 1058–1072, Apr. 2007.
9. S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” Int. J. Comput. Vis., vol. 48, no. 3, pp. 233– 254, 2002.
10. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. T.H. Romeny, and J. B. Zimmerman, â€•Adaptive histogram equalization and its variations,â€– Comput. Vis. Graph. Image Process., vol. 39, no. 3, pp. 355–368, 1987
11. N. Papadakis, E. Provenzi, and V. Caselles, â€•A variational model for histogram transfer of color images,â€– IEEE Trans. Image Process., vol.19, no. 11, p. , Nov. 2010.
12. Y.-W. Tai, J. Jia, and C.-K. Tang, â€•Local color transfer via probabilistic segmentation by expectationmaximization,â€– in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 747–754.
13. A. Abadpour and S. Kasaei, â€•An efficient PCA-based color transfer method,â€– J. Vis. Commun. Image Represent., vol. 18, no. 1, pp. 15–34, 2007.
14. F. Alter, S. Durand, and J. Froment, â€•Adapted total variation for artifact free decompression of jpeg images,â€– J. Math. Imaging Vis., vol. 23, pp. 199–211, 2005.
15. N. Papadakis, E. Provenzi, and V. Caselles, “A variational model for histogram transfer of color images,” IEEE Trans. Image Process., vol. 19, no. 11, p. , Nov. 2010.
16. Y.-W. Tai, J. Jia, and C.-K. Tang, “Local color transfer via probabilistic segmentation by expectationmaximization,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 747–754
17. A. Abadpour and S. Kasaei, “An efficient PCA-based color transfer method,” J. Vis. Commun. Image Represent., vol. 18, no. 1, pp. 15–34, 2007.
18. F. Alter, S. Durand, and J. Froment, “Adapted total variation for artifact free decompression of jpeg images,” J. Math. Imaging Vis., vol. 23, pp. 199–211, 2005.
19. T. Arici, S. Dikbas, and Y. Altunbasak, “A histogram modification framework and its application for image contrast enhancement,” IEEE Trans. Image Process., vol. 18, no. 9, pp. 1921–1935, Sep. 2009.
20. T. Celik and T. Tjahjadi, “Automatic image equalization and contrast enhancement using Gaussian mixture modeling,” IEEE Trans. Image Process., vol. 21, no. 1, pp. 145–156, Jan. 2012
21. M. Abdullah-Al-Wadud, Md. H. Kabir, M. A. A. Dewan, and O. Chae, “A dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electron., vol. 53, no. 2, pp. 593–600, May 2007.