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A. I. P

A. I. P. Term Project. 9512514 郭瓊蓮 922014 柯瑋明 922508 吳榮軒. Subject Review. Implement this paper : “Two-scale Tone Management for photographic Look,” Bae, Paris, and Durand. Apply the method to different kind of pictures. Add HDR technique. Algorithm Review. base.

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A. I. P

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  1. A. I. P Term Project 9512514 郭瓊蓮 922014 柯瑋明 922508 吳榮軒

  2. Subject Review • Implement this paper : “Two-scale Tone Management for photographic Look,” Bae, Paris, and Durand. • Apply the method to different kind of pictures. • Add HDR technique.

  3. Algorithm Review base large-scale transfer model textureness transfer bilateral filter input detail high pass and local averaging textureness

  4. Algorithm Review modified base black-and-white output constrained combination postprocess final output modified detail

  5. Our works Our input Our model

  6. Our works Our detail Our base

  7. Our works With edge preserving Without edgepreserving

  8. Our works Our result Author’s result

  9. HDR

  10. HDR

  11. Problems • Uncertainty. • Poisson equation. • Histogram matching. • Textureness. • Color channel.

  12. Uncertainty • An old problem while using fast bilateral filter.

  13. Uncertainty

  14. Poisson • Cost most time in our pipeline. • Use Discrete Sine Transform to reduce time complexity. • Easy to implement.

  15. Poisson • General Poisson Equation: • Ixx + Iyy = f • For discrete version, we can rewrite the equation to matrix form: • TI + IT = F ,where T is a N*N triagonal matrix of {1,-2,1}.

  16. Poisson • We define

  17. Poisson

  18. Poisson • DX+XD=B is easy to solve • Then we use I=SXS to get final answer.

  19. Poisson • In fact, SXS performs 2-D DST on X • Implementation steps: • Perform 2-D DST on F • Divide the sum of the corresponding eigenvalue and a constant. • Perform 2-D DST again

  20. Hist-matching • The gray-value in log domain are always negative or zero. • The range could be even wider if HDR added. • The function implemented by MATLAB can only handle the interval from 0 to 1……

  21. Hist-matching distribution Input histogram

  22. Hist-matching distribution Mask histogram

  23. Hist-matching distribution Output histogram

  24. Hist-matching Mask Input Output

  25. Textureness H is the high-pass version of the image. T( I )p = 1/k * ∑ gσs( |p – q| ) gσr( |Ip - Iq| )|H|q q∈|H| k = ∑ gσs( |p – q| ) gσr( |Ip - Iq| ) q∈I ρp = max( 0, ( T’p – T(B’)p ) / T(D)p ) O = B’ + ρD

  26. Textureness Input

  27. Textureness High frequency of H

  28. Textureness Absolute value of H

  29. Textureness T

  30. Textureness 0 +

  31. Textureness

  32. Color Channel • Which color channel could work best? • RGB channel. • Process separately. • Process intensity only and then interpolate the three channel. • YUV channel.

  33. Color Channel

  34. Color Channel

  35. Color Channel

  36. Color Channel

  37. More Images Input Model

  38. More Images Input Output

  39. More Images Model Input

  40. More Images Input Output

  41. More Images Model Input

  42. More Images Output Input

  43. More Images Model Input

  44. More Images Output Input

  45. Questions Thanks for your attention.

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