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CS 450: Introduction to Digital Signal and Image Processing

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Title: CS 450: Introduction to Digital Signal and Image Processing


1
CS 450 Introduction to Digital Signal and Image
Processing
  • A Few More Fundamentals

2
Sampling Revisited
  • How much sampling is enough?
  • Shannon Sampling TheoremTwice the highest
    frequency in the signal (in theory)
  • Nyquist rate
  • What happens if you sample above this?
  • Avoids dangers of theoretical limits
  • Better for intermediate processing
  • What happens if you dont sample enough?
  • Aliasing (false low-frequencies appear)
  • Moiré patterns
  • Insufficient sampling during acquisition
    introduces flaws that cannot be corrected through
    later processing

3
Interpolation
  • Interpolation is the process of guessing values
    of a function between samples
  • Very well-studied area in mathematics
  • Common forms of interpolation
  • Nearest-neighbor (0th order)
  • Linear draw a line between two known values
  • Quadratic fit a quadratic function to three
    points
  • Cubic fit a cubic function to four points
  • Harmonic fit using periodic functions (useful if
    you know the data is periodic)

4
Interpolation
  • Interpolating two-dimensional data
  • Bilinear, bicubic, etc.
  • Interpolate in one dimension, then the
    other(order doesnt matter)
  • More sophisticated multidimensional techniques
    (data dependent simplices, radial basis
    functions, etc.)
  • Interpolating three-dimensional data
  • Trilinear, tricubic, etc.

5
Nearest-Neighbor Interpolation
  • Simplest to implement
  • Round off x and y values
  • Result is not continuous (blocky)

6
Bilinear Interpolation
  • Linearly interpolate vertically
  • Linearly interpolate horizontally

7
Higher-Order Interpolation
  • Higher-degree polynomials
  • e.g., bicubic (cubic interpolation vertically
    followed by cubic interpolation horizontally)
  • Sometimes other interpolating functions
  • Requires a larger neighborhood
  • e.g., bicubic requires a 4x4 neighborhood

8
Playing on the Pixel Grid
  • Many of the things well do involve using
    neighboring samples
  • Who is my neighbor?
  • Common approaches
  • 4-connected (N, S, E, W)
  • 8-connected (add NE, SE, SW, NW)
  • Distance?
  • Euclidean (as the crow flies)
  • 4-connected (city block, Manhattan)
  • 8-connected (chessboard)
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