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Tone mapping

Tone mapping. Digital Visual Effects, Spring 2007 Yung-Yu Chuang 2007/3/13. with slides by Fredo Durand, and Alexei Efros. Tone mapping. How can we display it? Linear scaling?, thresholding?. 10 -6. 10 6. dynamic range. Real world radiance. 10 -6. 10 6. Display intensity.

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Tone mapping

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  1. Tone mapping Digital Visual Effects, Spring 2007 Yung-Yu Chuang 2007/3/13 with slides by Fredo Durand, and Alexei Efros

  2. Tone mapping • How can we display it? Linear scaling?, thresholding? 10-6 106 dynamic range Real world radiance 10-6 106 Display intensity Pixel value 0 to 255 CRT has 300:1 dynamic range

  3. Preliminaries • For color images • Log domain is usually preferred. • Gaussian filter. Sampling issues. Efficiency issues.

  4. Eye is not a photometer! • "Every light is a shade, compared to the higher lights, till you come to the sun; and every shade is a light, compared to the deeper shades, till you come to the night." — John Ruskin, 1879

  5. We are more sensitive to contrast • Weber’s law Just-noticeable Difference (JND) background intensity flash

  6. Global operator (Reinhart et al) Approximation of scene’s key (how light or dark it is). Map to 18% of display range for average-key scene User-specified; high key or low key

  7. low key (0.18) high key (0.5)

  8. Frequency domain • First proposed by Oppenheim in 1968! • Under simplified assumptions, image = illuminance * reflectance low-frequency attenuate more high-frequency attenuate less

  9. Oppenheim • Taking the logarithm to form density image • Perform FFT on the density image • Apply frequency-dependent attenuation filter • Perform inverse FFT • Take exponential to form the final image

  10. Fast Bilateral Filteringfor the Display ofHigh-Dynamic-Range Images Frédo Durand & Julie Dorsey Laboratory for Computer Science Massachusetts Institute of Technology

  11. A typical photo • Sun is overexposed • Foreground is underexposed

  12. Gamma compression • X -> Xg • Colors are washed-out Input Gamma

  13. Gamma compression on intensity • Colors are OK, but details (intensity high-frequency) are blurred Intensity Gamma on intensity Color

  14. Chiu et al. 1993 • Reduce contrast of low-frequencies • Keep high frequencies Low-freq. Reduce low frequency High-freq. Color

  15. The halo nightmare • For strong edges • Because they contain high frequency Low-freq. Reduce low frequency High-freq. Color

  16. Durand and Dorsey • Do not blur across edges • Non-linear filtering Large-scale Output Detail Color

  17. Edge-preserving filtering • Blur, but not across edges • Anisotropic diffusion [Perona & Malik 90] • Blurring as heat flow • LCIS [Tumblin & Turk] • Bilateral filtering [Tomasi & Manduci, 98] Input Gaussian blur Edge-preserving

  18. Start with Gaussian filtering • Here, input is a step function + noise output input

  19. Start with Gaussian filtering • Spatial Gaussian f output input

  20. Start with Gaussian filtering • Output is blurred output input

  21. Gaussian filter as weighted average • Weight of x depends on distance to x output input

  22. The problem of edges • Here, “pollutes” our estimate J(x) • It is too different output input

  23. Principle of Bilateral filtering • [Tomasi and Manduchi 1998] • Penalty g on the intensity difference output input

  24. Bilateral filtering • [Tomasi and Manduchi 1998] • Spatial Gaussian f output input

  25. Bilateral filtering • [Tomasi and Manduchi 1998] • Spatial Gaussian f • Gaussian g on the intensity difference output input

  26. Normalization factor • [Tomasi and Manduchi 1998] • k(x)= output input

  27. Bilateral filtering is non-linear • [Tomasi and Manduchi 1998] • The weights are different for each output pixel output input

  28. Contrast reduction Input HDR image Contrast too high!

  29. Contrast reduction Input HDR image Intensity Color

  30. Contrast reduction Input HDR image Large scale Intensity FastBilateral Filter Color

  31. Contrastreduction Input HDR image Large scale Intensity FastBilateral Filter Detail Color

  32. Contrast reduction Input HDR image Scale in log domain Large scale Large scale Intensity Reducecontrast FastBilateral Filter Detail Color

  33. Contrast reduction Input HDR image Large scale Largescale Intensity Reducecontrast FastBilateral Filter Detail Detail Preserve! Color

  34. Contrast reduction Input HDR image Output Large scale Largescale Intensity Reducecontrast FastBilateral Filter Detail Detail Preserve! Color Color

  35. Oppenheim bilateral

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