Aliasing and Antialiasing Techniques in Image Processing
Explore the concepts of aliasing and antialiasing in image processing, learn about the visual artifacts caused by aliasing, and discover techniques to reduce or eliminate these issues. Sampling theory, jagged lines, missed detail, and strobing effects are discussed to help you understand and address these challenges effectively.
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Presentation Transcript
Antialiasing CMSC 435/634
Original Scene Luminosity
Pixel Sampling Samples
Displayed Image Luminosity
Aliasing Visual artifacts Jagged lines and edges High frequencies appearing as low Small objects missed Texture distortions Strobing and popping Backward movement
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
What Will Alias? Plot based on frequency Like audio equalizer Fourier transform
How to Fix It? Filter Blur away high frequency Blur is better than aliasing
Filters 23
Analytic Area Sampling Compute area of pixel covered Box in spatial domain Nice finite kernel easy to compute sinc in freq domain Plenty of high freq Still aliases
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
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
Adaptive sampling Vary numerical integration step More samples in high contrast areas Easy with ray tracing, harder for others Possible bias
Stochastic sampling Monte-Carlo integration of filter Sample distribution Poisson disk Jittered grid Aliasing Noise
