High dynamic range imaging

1. High dynamic range imaging

2. Camera pipeline 12 bits 8 bits

3. Short exposure 10-6 106 dynamic range Real world radiance 10-6 106 Picture intensity Pixel value 0 to 255

4. Long exposure 10-6 106 dynamic range Real world radiance 10-6 106 Picture intensity Pixel value 0 to 255

5. Varying shutter speeds

6. Recovering High Dynamic Range Radiance Maps from Photographs Paul E. DebevecJitendra Malik SIGGRAPH 1997

7. Recovering response curve 12 bits 8 bits

8. • 2 • 2 • 2 • 2 • 2 • 3 • 3 • 3 • 3 • 3 • 1 • 1 • 1 • 1 • 1 Recovering response curve Image series Dt =2 sec Dt =1 sec Dt =1/2 sec Dt =1/4 sec Dt =1/8 sec 255 0

9. Idea behind the math ln2

10. Idea behind the math Each line for a scene point. The offset is essentially determined by the unknown Ei

11. Idea behind the math Note that there is a shift that we can’t recover

12. Math for recovering response curve

13. Recovering response curve • The solution can be only up to a scale, add a constraint • Add a hat weighting function

14. Recovered response function

15. Constructing HDR radiance map combine pixels to reduce noise and obtain a more reliable estimation

16. Reconstructed radiance map

17. Gradient Domain High Dynamic Range Compression RaananFattalDaniLischinski Michael Werman SIGGRAPH 2002

18. log derivative attenuate integrate exp The method in 1D

19. The method in 2D • Given: a log-luminance image H(x,y) • Compute an attenuation map • Compute an attenuated gradient field G: • Problem: Gmay not be integrable!

20. Poisson equation Solution • Look for image I with gradient closest to G in the least squares sense. • I minimizes the integral:

21. Attenuation log(Luminance) gradient magnitude attenuation map

22. interpolate X = interpolate X = Multiscale gradient attenuation

23. Informal comparison Bilateral[Durand et al.] Photographic[Reinhard et al.] Gradient domain[Fattal et al.]

24. Informal comparison Bilateral[Durand et al.] Photographic[Reinhard et al.] Gradient domain[Fattal et al.]

25. Informal comparison Bilateral[Durand et al.] Photographic[Reinhard et al.] Gradient domain[Fattal et al.]

26. Local Laplacian Filters :Edge-aware Image Processingwith a Laplacian Pyramid Sylvain Paris Samuel W. Hasinoff Jan Kautz SIGGRAPH 2011

27. Background on Gaussian Pyramids • Resolution halved at each level using Gaussian kernel level 3 (residual) level 2 level 1 level 0

28. Background on Laplacian Pyramids • Difference between adjacent Gaussian levels level 3 (residual) level 2 level 1 level 0

29. Intuition for 1D Edge • Decomposition for the sake of analysis only • We do not compute it in practice = + + Input signal Discontinuity Texture Smooth

30. Intuition for 1D Edge = + + Input signal Discontinuity Texture Smooth Does notcontribute toLap. pyramid at that scale(d2/dx2=0)

31. Ideal Texture Increase Discontinuity Texture Keep unchanged Amplify

32. Our Texture Increase σ σ σ σ Input signal Local nonlinearity “Locally good”version user-defined parameter σdefines texture vs. edges

33. Our Texture Increase = + + “Locally good” Only left sideis affected Discontinuity Unaffected  Texture Left side is ok, right side is not  Smooth Does notcontribute toLap. pyramid at that scale(d2/dx2=0)

34. Negligible becausecollocated with discontinuity Negligible becauseGaussian kernel ≈ 0 Discussion = + + “Locally good” Only left sideis affected Discontinuity Unaffected  Texture Left side is ok, right side is not  Smooth Does notcontribute toLap. pyramid at that scale(d2/dx2=0) Good approximation to ideal case overall  (formal treatment in paper)

35. Texture ManipulationInput

36. Texture ManipulationDecrease

37. Texture ManipulationSmall Increase

38. Texture ManipulationLarge Increase

39. Texture ManipulationInput

40. Texture ManipulationLarge Increase