Gradient Domain High Dynamic Range Compression

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 • 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 • 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