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Retinex Algorithm Combined with Denoising Methods

Retinex Algorithm Combined with Denoising Methods. Hae Jong, Seo Multi Dimensional Signal Processing Group University of California at Santa Cruz. Overview. Background. SSR, MSR, MSRCR. New Approaches. Retinex Algorithm by two Bilateral filters

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Retinex Algorithm Combined with Denoising Methods

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  1. Retinex Algorithm Combined with Denoising Methods Hae Jong, Seo Multi Dimensional Signal Processing Group University of California at Santa Cruz

  2. Overview Background SSR, MSR, MSRCR New Approaches Retinex Algorithm by two Bilateral filters Retinex Algorithm by two higher order Bilateral filters Experimental Results Summary

  3. Part 1 Background on Retinex Algorithm

  4. Retinex Algorithm Flow Chart Kimmel et.al “A variational Framework for Retinex”

  5. Retinex Algorithms Single Scale Retinex Reflectance Given image Gaussian function Multi Scale Retinex The weighted average version of different scale SSR Multi Scale Retinex With Color restoration Different weight factor for different color bands Retinex Image Enhancement : Daniel J. Jonson et.al

  6. Retinex Algorithm Dynamic range compression Sharpening Color constancy Daniel J. Jonson et.al Retinex Image Enhancement :

  7. Shortcoming? Amplify the noise

  8. Part 2 Retinex by Two Bilateral Filters Michael Elad “Retinex by Two Bilateral Filters”

  9. Things to consider The illumination is supposed to be piecewise smooth. Since the reflectance is passive, 0≤R≤1, we require S≤L and s≤ . Trivial solution (L=255) should be avoided - The illumination should be forced to be close to s. Michael Elad “Retinex by Two Bilateral Filters”

  10. The Overall Model - shortcoming smooth illumination envelope smooth reflectance Requires an iterative solver! Noise is magnified in dark areas. Forcing works against noise suppression. Promotes hallows on the boundaries of the illumination. Michael Elad “Retinex by Two Bilateral Filters”

  11. Bilateral Filter The bilateral filter is a weighted average smoothing, with weights inversely proportional to the radiometric distance and spatial distance between the center pixel and the neighbor[Tomasi and Manduchi, 1998] The first Jacobi iteration that minimizes the above function leads to the bilateral filter[Elad, 2002]

  12. The Formulation with Bilateral Filter Smooth illumination Smooth Reflectance With this new formulation: Non-iterative solvercan be deployed, Both the illumination and the reflectance are forced to be piece-wise smooth,thus preventing hallows, Noiseis treated appropriately. Michael Elad “Retinex by Two Bilateral Filters”

  13. Part 1: Find by assuming r=0 Part 1: Find by assuming r=0 Part 2: Given , find r by Bilateral filter on s in anenvelopemode Bilateral filter on s- in aregularmode Numerical Solution illumination Reflectance Part 2: Given , find r by Michael Elad “Retinex by Two Bilateral Filters”

  14. Part 1: Find by assuming r=0 Part 2: Given , find ri by Higher order Bilateral filter on z in anenvelopemode New Suggestion – Higher order Bilateral Higher order Bilateral filter on z- in aregularmode

  15. Returning Some Illumination Kimmel et.al “A Variational Framework for Retinex”

  16. Part 3 Experiment Results Michael Elad “Retinex by Two Bilateral Filters”

  17. Example 1 Original Result (γ=3) Envelope mode Parameter : Regular mode

  18. Example 2 Original Result (γ=3) Envelope mode Parameter : Regular mode

  19. Example 3 Original Result (γ=3) Envelope mode Parameter : Regular mode

  20. Example 4 ( Hallow Effect ) Original Result (γ=3) Envelope mode Parameter : Regular mode

  21. Bilater Filter VS Kernel Regression Original Bilateral Filtered Result Kernel Regression Filtered Result Envelope mode Parameter : Regular mode

  22. Conclusion & Future work Implemented Retinex by two bilateral filters It overcomes hallows, the need for iterations, and handles noise well. Kernel regression method can do better using higher order. Apply Iterative Steering Kernel Regression this frame work Kimmel et.al “A Variational Framework for Retinex”

  23. Conclusion & Future work Implemented Retinex by two bilateral filters It overcomes hallows, the need for iterations, and handles noise well. Kernel regression method can do better using higher order. Apply Iterative Steering Kernel Regression this frame work Kimmel et.al “A Variational Framework for Retinex”

  24. Main References [1] Elad.M, “Retinex by Two Bilateral Filters”, Scale-Space 2005, LNCS 3459, pp. 217-229, (2005). [2] Rahman.Z, Jobson.D.J, Woodell.G.A : “Retinex processing for automatic image enhancement”. Journal of Electronic imaging, January (2004) [3] Takeda.H, S.Farsiu, and P.Milanfar, “Kernel Regression for Image Processing and Reconstruction”, IEEE Trans. on Image Processing, vol. 16, no. 2, pp. 349-366, Feb. (2007) 2, 8

  25. Thanks Hae Jong, Seo Email : rokaf@soe.ucsc.edu Website : http://soe.ucsc.edu/~rokaf

  26. Back up Michael Elad “Retinex by Two Bilateral Filters”

  27. Large Small s llumination as an Upper Envelope ( ) æ ö 2 2 ò d W Ñ + a - Minimize s ç l l ø è ³ s l W smooth illumination being close to s Michael Elad “Retinex by Two Bilateral Filters”

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