Image enhancement based on the shearlet transform

ICIC Express Letters

Volumn 3, Issue 3, 2009, Pages 621-626

  Pan, Jingchang ^a,b   Zhang, Caiming ^a   Guo, Qiang ^c  

^a SHANDONG UNIVERSITY   (China)

^b SHANDONG UNIVERSITY   (China)

^c SHANGHAI UNIVERSITY   (China)

Author keywords

Image enhancement; Mapping function; Shearlet transform; Thresholdingfunction; Wavelet transform

Indexed keywords

EXISTING METHOD; IMAGE DECOMPOSITION; MAPPING FUNCTION; MAPPING FUNCTIONS; MULTISCALES; NOISY IMAGE; SHEARLET TRANSFORM; SPARSE REPRESENTATION; TWO STEP METHOD;

IMAGE ENHANCEMENT; MAPPING;

WAVELET TRANSFORMS;

EID: 77953876556     PISSN: 1881803X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. K. A. Panetta E. J. Wharton and S. S. Agaian, Human visual system-basedimage enhancement and logarithmic contrast measure, IEEE Trans. Sys., Man,Cybern. B, Cybern., Vol. 38, No. 1, pp. 174-188, 2008.
2. J. A. Stark, Actaptive image contrast enhancement using generalization ofhistogram equalization, IEEE Trans. Signal Process., Vol. 9, No. 5, pp.889-896, 2000.
3. D. Coltuc, P. Bolon and J. M. Chassery, Exact histogram specification,IEEE Trans. Image Process., Vol. 15, No. 5, pp. 1143-1152, 2006.
4. J. Mukherjee, and S. K. Mitra Enhancement of color images by scaling theDCT coefficients, IEEE Trans. Image Process., Vol. 17, No. 10, pp. 1783-1794,2008.
5. T. Tateyama Z. Nakao, X. Hao and Y. W. Chen, Contrast enhancement of MRbrain images by canonical correlations based kernel independent componentanalysis, Int. Journal of Innovation Computing, Information and Control, Vol. 5,No. 7, pp. 1857-1866, 2009.
6. S. S. Agaian, B. Silver and K. A. Panetta Transform coefficienthistogram- based image enhancement algorithms using contrast entropy, IEEE Trans.Image Process., Vol. 16, No. 3, pp. 741-758, 2007.
7. K. V. Velde, Multi-scale color image enhancement, Proc. of the IEEE Int.Conf. on Image Processing, Vol. 3, pp. 584-587, 1999.
8. Y. Wan, and D. Shi, Joint exact histogram specification and imageenhancement through the wavelet transform, IEEE Trans. Image Process., Vol. 16,No. 9, pp. 2245-2250, 2007.
9. 2 singularities, Commun.Pure Appl. Math., Vol. 57, No. 2, pp. 219-266, 2004.
10. M. N. Do and M. Vetterli, The contourlet transform: an efficientdirectional multiresolution image representation, IEEE Trans. Image Process.,Vol. 14, No. 12, pp. 2091-2106, 2005.
11. A. L. Cunha J. Zhou and M. N. Do, The nonsubsampled contourlet transform:theory, design, and applications, IEEE Trans. Image Process., Vol. 15, No. 10,pp. 3089-3101, 2006.
12. G. R. Easley and F. Colonna Generalized discrete random transform andapplication to image processing, in Advances in Imaging and Electron Physics,Vol. 151, pp. 169-239, Academic Press, New York, 2008.
13. K. Guo, and D. Labate, Optimally sparse multidimensional representationusing shearlets, SIAM J. Math. Anal., Vol. 39, no. 1, pp. 298-318, 2007.
14. L. Sendur, and I. W. Selesnick, Bivariate shrinkage functions forwavelet-based denoising exploiting interscale dependency, IEEE Trans. SignalProcess., Vol. 50, No. 11, pp. 2744-2756, 2002.
15. Q. Guo, S. Yu, X. Chen, C. Liu and W. Wei, Shearlet-based image denoisingusing bivariate shrinkage with intra-band and opposite orientationdependencies, Proc. of the 2nd Int. Joint Conf. on Computational Sciences andOptimization, Vol. 1, pp. 863-866, 2009.
16. D. L. Donoho and I. M. Johnstone, Actapting to unknown smoothness viawavelet shrinkage, Journal of the American Statistical Assoc., Vol. 90, No. 432,pp. 1200-1224, 1995.