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An Image Enhancement Technique Based on Wavelets

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Biologically Motivated Computer Vision (BMCV 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1811))

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Abstract

We propose a technique for image enhancement, especially for denoising and deblocking based on wavelets. In this proposed algorithm, a frame wavelet system designed as an optimal edge detector is used. And our theory depends on Lipschitz regularity, spatial correlation, and some important assumptions. The performance of the proposed algorithm is tested on three popular test images in image processing area. Experimental results show that the performance of the proposed algorithm is better than those of other previous denoising techniques like spatial averaging filter, Gaussian filter, median filter, Wiener filter, and some other wavelet based filters in the aspect of both PSNR and human visual system. The experimental results also show that our algorithm has approximately same capability of deblocking as those of previous developed techniques.

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References

  1. Gonzalez, R., Woods, R.: Digital Image Processing. Addison Wesley. (1993) 183–450

    Google Scholar 

  2. Weeks, A.: Fundamentals of Electronic Image Processing. SPIE/IEEE. (1996) 121–227

    Google Scholar 

  3. Donoho, D., Johnstone, I.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81 (1994) 425–455

    Article  MATH  MathSciNet  Google Scholar 

  4. Donoho, D.: De-noising by soft-thresholding. IEEE Transactions on Information Theory 41(3) (1995) 613–627

    Article  MATH  MathSciNet  Google Scholar 

  5. Burrus, C., Gopinath, R., Guo, H.: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice-Hall. (1998) 196–218

    Google Scholar 

  6. Mallat, S., Hwang, W.: Singularity detection and processing with wavelets. IEEE Transactions on Information Theory 38(2) (1992) 617–643

    Article  MathSciNet  Google Scholar 

  7. Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(7) (1992) 710–732

    Article  Google Scholar 

  8. Xu, Y., Weaver, J., Healy, D., Lu, J.: Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique. IEEE Transactions on Image Processing 3(6) (1994)

    Google Scholar 

  9. Rao, K., Hwang, J.: Techniques & Standards for Image, Video & Audio Coding. Prentice-Hall. (1996) 127–386

    Google Scholar 

  10. Vetterli, M., Kovacevic, J.: Wavelets and Subband Coding. Prentice-Hall. (1995) 1–86

    Google Scholar 

  11. Chou, J., Crouse, M., Ramchandran, K.: A simple algorithm for removing blocking artifacts in block-transform coded images. Proc. ICIP’98 1 (1998) 377–380

    Google Scholar 

  12. Yang, Y., Galatsanos, P., Katsaggelos, A.: Projection-based spatially adaptive reconstruction of block-transform compressed images. IEEE Trans. Image Processing 4 (1995) 896–908

    Article  Google Scholar 

  13. Xiong, Z., Orchard, M., Zhang, Y.: A simple deblocking algorithm for JPEG compressed images using overcomplete wavelet representations. IEEE Trans. on Circuits and Systems for Video Technology (1996)

    Google Scholar 

  14. Hsieh, J., Liao, H., Ko, M., Fan, K.: A new wavelet-based edge detector via constrained optimization. Image and Vision Computing 15(7) (1997) 511–527

    Article  Google Scholar 

  15. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. 8(6) (1986) 679–697

    Article  Google Scholar 

  16. Grossmann, A., Morlet, J.: Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. 15 (1984) 723–736

    Article  MATH  MathSciNet  Google Scholar 

  17. Mallat, S.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern Recognition and Machine Intelligence 11(7) (1989) 674–693

    Article  MATH  Google Scholar 

  18. Grossman, A.: Wavelet transform and edge detection. Stochastic Processes in Physics and Engineering (1986)

    Google Scholar 

  19. Davis, E.: Machine Vision. Academic Press. (1997) 248–249

    Google Scholar 

  20. Daubechies, I.: Ten Lectures on Wavelets. SIAM. (1992) 24–63

    Google Scholar 

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Author information

Authors and Affiliations

  1. Yonsei University, Shinchon-Dong 134, Seodaemun-Koo, Seoul, Korea

    Hae-Sung Lee

  2. Kwangwoon University, Wolke-Dong 447-1, Nowon-Koo, Seoul, Korea

    Jisang Yoo

Authors
  1. Hae-Sung Lee
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  2. Yongbum Cho
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  3. Hyeran Byun
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  4. Jisang Yoo
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Editor information

Editors and Affiliations

  1. Center for Artificial Vision Research, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Max-Planck-Institute for Biological Cybernetics, Spemannstr. 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff

  3. Department of Brain and Cognitive Sciences Artificial Intelligence Laboratory, E25-218, Massachusetts Institute of Technology, 45 Carleton Street, Cambridge, MA, 02142, USA

    Tomaso Poggio

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© 2000 Springer-Verlag Berlin Heidelberg

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Lee, HS., Cho, Y., Byun, H., Yoo, J. (2000). An Image Enhancement Technique Based on Wavelets. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_28

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  • DOI: https://doi.org/10.1007/3-540-45482-9_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67560-0

  • Online ISBN: 978-3-540-45482-3

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