Resolving Aliasing Issues in Digital Imaging

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Explore the impact of aliasing on digital images, including visual artifacts like jagged lines and missed details. Learn how antialiasing techniques can help address these issues, along with insights into sampling theory and reconstruction limitations. Discover practical strategies for fixing aliasing problems in your images for enhanced visual quality.

  • Aliasing
  • Antialiasing
  • Digital Imaging
  • Sampling Theory
  • Visual Artifacts

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Presentation Transcript


  1. Antialiasing CMSC 435/634

  2. Original Scene Luminosity

  3. Pixel Sampling Samples

  4. Displayed Image Luminosity

  5. What went wrong?

  6. Aliasing Visual artifacts Jagged lines and edges High frequencies appearing as low Small objects missed Texture distortions Strobing and popping Backward movement

  7. Jagged Lines

  8. Jagged Edges

  9. Frequency Aliasing

  10. Missed Detail

  11. Missed Detail

  12. Strobing/Popping

  13. How might you fix aliasing?

  14. Sampling Theory Shannon s sampling theory (1D): A band limited signal f(t) with cut off frequency wF may be perfectly reconstructed from its samples f(nT0) if 2 /T0 >= 2wF wF == Nyquist limit Alternatively: a signal can be reconstructed exactly from samples only if the highest frequency is less than half the sampling rate

  15. Sampling a Sine Wave

  16. What Will Alias? Plot based on frequency Like audio equalizer Fourier transform

  17. What Will Alias? Sampling replicates spectrum in a grid Aliasing when they overlap

  18. How to Fix It? Filter Blur away high frequency Blur is better than aliasing

  19. Filters 24

  20. Using a Filter to Compute Pixel Color 25

  21. Analytic Area Sampling Compute area of pixel covered Box in spatial domain Nice finite kernel easy to compute sinc (sin(x)/x) in freq domain Plenty of high freq Still aliases

  22. Analytic higher order filtering Fold better filter into rasterization Can make rasterization much harder Usually just done for lines Draw with filter kernel paintbrush Only practical for finite filters

  23. Supersampling Numeric integration of filter Grid with equal weight = box filter Push up Nyquist frequency Edges: frequency, still alias Other filters: Grid with unequal weights Priority sampling

  24. Adaptive sampling Vary numerical integration step More samples in high contrast areas Easy with ray tracing, harder for others Possible bias

  25. Stochastic sampling Monte-Carlo integration of filter Sample distribution Poisson disk Jittered grid Aliasing Noise

  26. No Antialiasing 31

  27. With Antialiasing 32

  28. With Antialiasing 33

  29. With Antialiasing 34

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