High dynamic range imaging Camera pipeline 12 bits

High dynamic range imaging

Camera pipeline 12 bits 8 bits

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

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

Varying shutter speeds

Recovering High Dynamic Range Radiance Maps from Photographs Paul E. Debevec Jitendra Malik SIGGRAPH 1997

Recovering response curve 12 bits 8 bits

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

Idea behind the math ln 2

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

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

Math for recovering response curve

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

Recovered response function

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

Reconstructed radiance map

Gradient Domain High Dynamic Range Compression Raanan Fattal Dani Lischinski Michael Werman SIGGRAPH 2002

The method in 1 D log derivative attenuate exp integrate

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

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

Attenuation log(Luminance) gradient magnitude attenuation map

Multiscale gradient attenuation interpolate X =

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

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

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

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

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

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

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

Intuition for 1 D Edge = Input signal + Discontinuity + Smooth Texture Does not contribute to Lap. pyramid at that scale (d 2/dx 2=0) 30

Ideal Texture Increase Discontinuity Texture Keep unchanged Amplify 31

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

Our Texture Increase = + “Locally good” Discontinuity Only left side is affected Unaffected + Texture Smooth Left side is ok, right side is not Does not contribute to Lap. pyramid at that scale (d 2/dx 2=0) 33

Negligible because collocated with discontinuity Discussion = + “Locally good” Discontinuity Only left side is affected Negligible because Gaussian kernel ≈ 0 Unaffected + Texture Smooth Left side is ok, right side is not Does not contribute to Lap. pyramid at that scale (d 2/dx 2=0) Good approximation to ideal case overall (formal treatment in paper) 34

Texture Manipulation Input 35

Texture Manipulation Decrease 36

Texture Manipulation Small Increase 37

Texture Manipulation Large Increase 38

Texture Manipulation Input 39

Texture Manipulation Large Increase 40