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Gradient Domain High Dynamic Range Compression

Gradient Domain High Dynamic Range Compression. Raanan Fattal Dani Lischinski Michael Werman. 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?. Whole Image Solutions. Tone Reproduction Curves

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Gradient Domain High Dynamic Range Compression

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  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

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