1. Gradient Domain High Dynamic Range Compression
Raanan Fattal
Dani Lischinski
Michael Werman

2. The Dynamic Range Problem
What’s wrong with these images?
What would your eye see?
How could you put all this information into one image?

3. Whole Image Solutions Tone Reproduction Curves Examples
Re-mapping of luminance values
Easy to compute
Suffer from quantization
Examples
Linear scaling
Gamma correction
More sophisticated models…

4. Ward Larson Model One of the best total image methods
Based on models of display capabilities and human vision
Still suffers from loss of local contrast
Notice washed-out appearance of the outside area

5. Local Solutions Tone Reproduction Operators Older Methods
Take local context into account
Attempt to solve the local contrast problem
Older Methods
Based on estimating illuminance and reflectance for each part of the image
Suffer from artifacts, dark halos

6. Low Curvature Image Simplifier
Tumblin and Turk, 1999
Scale luminance of smoothed image
Add back details
8 parameters
Computationally intensive

7. Gradient Domain Method

8. Basic Assumptions
The eye responds more to local intensity differences than global illumination
A HDR image must have some large magnitude gradients
Fine details consist only of smaller magnitude gradients

9. Basic Method Take the log of the luminances
Calculate the gradient at each point
Scale the magnitudes of the gradients with a progressive scaling function (Large magnitudes are scaled down more than small magnitudes)
Re-integrate the gradients and invert the log to get the final image

10. 1D Example
Original Signal F(x) - Dynamic range: 2415:1

11. 1D Example
ln F(x)

12. 1D Example
F’(x)

13. 1D Example
G(x) = F’(x) after applying the attenuating function

14. 1D Example
I(x) = Integrate G(x)

15. 1D Example
eI(x) - New dynamic range: 7.5:1

16. Changes for 2D Use gradients instead of derivatives
May produce a non-integrable vector field after scaling
Transform scaled vectors into a conservative field whose gradients are closest to G(x)

17. Attenuation Map

18. Attenuation Details Images contain edges at multiple levels of detail
How do we handle this?
Compute gradients for many different resolutions of the image
The set of different resolution images composes a Gaussian pyramid

19. Creating the Final Image
How do we recombine the different resolution levels?
Start with coarsest image
Calculate scaling factors
Linearly interpolate those factors for each point in the next image, and multiply with the local scaling factor
Apply the combined factors to the highest resolution image

20. The Attenuation Function
α = average gradient magnitude for each level times 0.1
β = adjustable gain (between 0.8 and 0.9)

21. Performance On an 1800 MHz Pentium 4:
Computing a 512x384 image takes 1.1 seconds
Computing a 1024x768 image takes 4.5 seconds
LCIS takes 8.5 minutes to compute a 751x1130 image

22. Examples
Streetlight on a foggy night
Dynamic range 100,000:1

23. Examples
Stanford Memorial Church
DR 250,000:1

24. Applications Enhancing contrast for LDR images
Combining photographs of different exposure levels to enhance detail or stitch together for panoramas
Medical image enhancements

25. Panoramas

26. Medical Imaging

27. Questions / Credits Any questions?
All pictures in this presentation are from the original paper