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naïve compositing

This paper presents a robust and efficient method for compositing with visual masking to hide residuals in textured areas, aiming to achieve better gradient fields at boundaries. By utilizing weighted least-squares optimization and generating a low-curl boundary, our approach reduces bleeding, enhances output quality, and ensures time-performance efficiency. The strategy involves concealing bleeding in textured regions and leveraging perceptual effects for seamless compositing. Results and comparisons with related works like Poisson Compositing and L1 Norm Show positive outcomes.

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naïve compositing

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  1. naïve compositing

  2. Poisson compositing

  3. our result

  4. Objectives Seamless compositing • Robust to inaccurate selection • Output Quality • - Limit color bleeding • Time-Performance • - Efficient method

  5. Related Work: Poisson Compositing Pasting gradients instead of pixels Pérez 03, Georgiev06, … • Pros: • Blends seamlessly • Linear computation • Cons: • Bleeding visible • Foreground to background • bleeding Poisson Compositing Result

  6. Related Work: L1 Norm Introduced in shape from shading Reddy 09 • Pros: • Reduced bleeding • Cons: • Nonlinear • - Computationally intensive L1 NormResult

  7. Related Work: Moving Boundaries Move the boundaries to avoid bleeding Jia 06 • Pros: • Avoids bleeding • Cons: • We don’t want boundaries • to change • Changed composition Changing boundaries

  8. Contributions • Conceal bleeding in textured areas • Better gradient field at boundary • Efficient linear scheme

  9. Overview • Problem Description • Hiding Residuals with Visual Masking • Generating a low-curl boundary • Results and Comparisons

  10. Algorithm Overview Foreground Selection Background

  11. Algorithm Overview Foreground Selection grad Background grad

  12. Algorithm Overview Foreground Targetgradient field Selection grad paste Background grad

  13. Algorithm Overview Foreground Targetgradient field Selection grad paste Background integrate grad Result

  14. Standard Approach: Poisson compositing We seek image I with gradients close to target v. target gradient field =

  15. Standard Approach: Poisson compositing We seek image I with gradients close to target v. target gradient field = Least-squares: output gradient field

  16. Our Approach We seek image I with gradients close to target v. target gradient field = value that minimizes curl Weighted least-square: weight output gradient field

  17. Overview • Problem Statement • Hiding Residuals with Visual Masking • Generating a low-curl boundary • Results and Comparisons

  18. Our Strategy • Use the weights to locate integration residuals where they are less visible • Exploit perceptual effect: visual masking • human perception affected by texture.

  19. Visual Masking Demo Can you spot all the dots?

  20. Visual Masking Demo Texture hides low-frequency content

  21. Visual Masking Demo Can you spot all the dots?

  22. Design of the Weights smooth region: needs high weightto prevent bleeding textured regions: low weight is okbecause bleedingless visible weights (white is high)

  23. Estimating the Amount of Texture RGB gradient field

  24. Estimating the Amount of Texture Small Gaussian convolution Noise controlling function RGB gradient field Large Gaussian convolution

  25. Estimating the Amount of Texture small Gaussian large Gaussian gradient field g

  26. Estimating the Amount of Texture Texture Map Output (white indicates more texture)

  27. Weight Formula = 1 – • v depends only on foreground and background • does not depend on the unknown I • weights are constant in the optimization • our energy is a classical least-squares optimization Weight output

  28. Hiding Residuals with Visual Masking Bleeding only in textured areas With texture weights Poisson compositing

  29. Hiding Residuals with Visual Masking Residuals With texture weights Poisson compositing

  30. Hiding Residuals with Visual Masking Reduced bleeding but not fully With texture weights Poisson compositing

  31. Overview • Problem Statement • Hiding Residuals with Visual Masking • Generating a low-curl boundary • Results and Comparisons

  32. Boundary Problems • Target field vintegrableiffcurl(v)=0 • Only boundary has non-zero curl • Consequence oftarget field construction(see paper) • Our strategy: reducing the curl curl usingstandard strategy

  33. Naïve Approach • Minimize • Unfortunately, the result is not seamless: output of naïve approach close-up

  34. Our Approach: Least Squares Trade-off • Unknowns: target field v along boundary • Weights depend on texture (detail in paper) zero curl weight seamless compositing

  35. Overview • Problem Statement • Hiding Residuals with Visual Masking • Generating a low-curl boundary • Results and Comparisons

  36. Results and Comparisons Copy-and-paste

  37. Results and Comparisons Poisson Reconstruction : Pérez 03, Georgiev 06, …

  38. Results and Comparisons Maximum Gradient : Pérez 03

  39. Results and Comparisons Diffusion : Agrawal 06

  40. Results and Comparisons Lalonde 07

  41. Results and Comparisons L1 Norm : Reddy 09

  42. Results and Comparisons Our Result

  43. Results and Comparisons

  44. Results and Comparisons Poisson

  45. Results and Comparisons Our Result

  46. Results and Comparisons Poisson

  47. Results and Comparisons Our Result

  48. Discussion • Several parameters • - all examples use the same parameters • Discoloration may happen • - happens in all gradient based operators • Our model of visual masking is simple • - good for performance • - more complex model could be used

  49. Conclusion • Robust compositing using visual masking • Better gradient field at boundary • Efficient linear scheme Poisson Reconstruction Our Result

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