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Color Photo Denoising Via Hue, Saturation and Intensity Diffusion

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Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

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Abstract

This paper presents a new image denoising model for color photo noise removal. Unlike previous approaches, our algorithm is based on separating a color photo into hue, saturation, and intensity (HSI) components, and then processing each component with partial differential equations (PDE)-based diffusion flows. The hue and saturation denoising are implemented by a new weighted orientation diffusion and a modified curvature flow respectively. The intensity denoising is implemented with a PDE that is a combination of a gradient vector flow (GVF)-based filter and a fourth-order PDE filter. This combined technique provides a robust and accurate denoising process, i.e., it preserves edges well and at the same time overcomes the staircase effect in smooth regions. Experiments on color photos demonstrate the improved performance of the proposed model when compared with other recognized approaches and commercial software.

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References

  1. Perona, P., Malik, J.: Scale-space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. PAMI 12, 629–639 (1990)

    Google Scholar 

  2. Gilboa, G., Sochen, N., Zeevi, Y.: Image Enhancement and Denoising by Complex Diffusion Processes. IEEE Trans. PAMI 26, 1020–1036 (2004)

    Google Scholar 

  3. Tschumperle, D., Deriche, R.: Vector-valued Image Regularization with PDEs: a Common Framework for Different Applications. IEEE Trans. PAMI 27, 506–517 (2005)

    Google Scholar 

  4. Malladi, R., Sethian, J.A.: Image Processing: Flows Under Min/Max Curvature and Mean Curvature. Graphical Models and Image Processing 58, 127–141 (1996)

    Article  Google Scholar 

  5. Alvarez, L., Mazorra, L.: Signal and Image Restoration Using Shock Filters and Anisotropic Diffusion. SIAM J. of Numerical Anal. 31, 590–605 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  6. Carmona, R., Zhong, S.: Adaptive Smoothing Respecting Feature Directions. IEEE Trans. IP 7, 353–358 (1998)

    Google Scholar 

  7. Weickert, J.: Coherence-enhancing Diffusion Filtering. IJCV 31, 111–127 (1999)

    Article  Google Scholar 

  8. Perona, P.: Orientation Diffusions. IEEE Trans. IP 7, 457–467 (1998)

    Google Scholar 

  9. Tang, B., Sapiro, G., Caselles, V.: Color Image Enhancement via Chromaticity Diffusion. IEEE Trans. IP 10, 701–707 (2001)

    MATH  Google Scholar 

  10. Rudin, L., Osher, S., Fatemi, E.: Nonlinear Total Variation Based Noise Removal Algorithms. Physica D 60, 259–268 (1992)

    Article  MATH  Google Scholar 

  11. Blomgren, P., Chan, T.F.: Color TV: Total Variation Methods for Restoration of Vector-Valued Images. IEEE Trans. IP 7, 304–309 (1998)

    Google Scholar 

  12. Lysaker, M., Tai, X.: Iterative Image Restoration Combining Total Variation Minimization and a Second-order Functional. IJCV 66, 5–18 (2006)

    Article  Google Scholar 

  13. Chan, T., Kang, S.H., Shen, J.: Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models. J. of Visual Communication and Image Representation 12, 422–435 (2001)

    Article  Google Scholar 

  14. Sochen, N., Kimmel, R., Malladi, R.: A General Framework for Low Level Vision. IEEE Trans. IP 7, 310–318 (1998)

    MATH  MathSciNet  Google Scholar 

  15. Yezzi, A.: Modified Curvature Motion for Image Smoothing and Enhancement. IEEE Trans. IP 7, 345–352 (1998)

    Google Scholar 

  16. Kimmel, R., Sochen, N.: Orientation Diffusion or How to Comb a Porcupine. J. of Visual Communication and Image Representation 13, 238–248 (2002)

    Article  Google Scholar 

  17. Shah, J.: A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion. In: Proc. IEEE CVPR, pp. 136–142 (1996)

    Google Scholar 

  18. Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: Image Denoising Using Scale Mixtures of Gaussian in the Wavelet Domain. IEEE Trans. IP 12, 1338–1351 (2003)

    MathSciNet  Google Scholar 

  19. Roth, S., Black, M.J.: Fields of Experts: a Framework for Learning Image Priors. In: Proc. IEEE CVPR, pp. 860–867 (2005)

    Google Scholar 

  20. Black, M., Sapiro, G., Marimont, D., Heeger, D.: Robust Anisotropic Diffusion. IEEE Trans. IP 7, 421–432 (1998)

    Google Scholar 

  21. Buades, A., Coll, B., Morel, J.M.: A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling and Simulation 4, 490–530 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  22. Starck, J.L., Candès, E.J., Donoho, D.L.: The Curvelet Transform for Image Denoising. IEEE Trans. IP 11, 670–684 (2002)

    Google Scholar 

  23. Weickert, J.: Anisotropic Diffusion in Image Processing. Teubner-Verlag (1998)

    Google Scholar 

  24. Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Trans. IP 7, 359–369 (1998)

    MATH  MathSciNet  Google Scholar 

  25. Yu, H., Chua, C.: GVF-based Anisotropic Diffusion Models. IEEE Trans. IP 15, 1517–1524 (2006)

    Google Scholar 

  26. Professional Photographer Magazine, http://www.ppmag.com/bonus/2006/08/review-imagenomic-noiseware.html

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Author information

Authors and Affiliations

  1. Information Technology Department, Armstrong Atlantics State University, 11935 Abercorn Street, Savannah, Georgia, USA, 31419

    Lei He

  2. Imaging and Visualization Department, Siemens Corporate Research, 755 College Road East, Princeton, New Jersey, USA, 08540

    Chenyang Xu

Authors
  1. Lei He
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  2. Chenyang Xu
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Editor information

Aurélio Campilho Mohamed Kamel

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© 2008 Springer-Verlag Berlin Heidelberg

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He, L., Xu, C. (2008). Color Photo Denoising Via Hue, Saturation and Intensity Diffusion. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_16

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  • DOI: https://doi.org/10.1007/978-3-540-69812-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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