1. Image Acquisition

2. Electromagnetic Spectrum
The wavelength required to “see” an object must be the same size of smaller than the object

3. Image Sensors

4. Sensor Strips

5. An Example of Digital Image Acquisition Process

6. Digital Image Generation

7. Image Sampling and Quantization

8. Digital Image Representation
An image is a function defined on a 2D coordinate f(x,y).
The value of f(x,y) is the intensity.
3 such functions can be defined for a color image, each represents one color component
A digital image can be represented as a matrix.

9. Spatial and Gray Level Resolution
Spatial resolution:
# of samples per unit length or area
DPI: dots per inch specifies the size of an individual pixel
If pixel size is kept constant, the size of an image will affect spatial resolution
Gray level resolution:
Number of bits per pixel
Usually 8 bits
Color image has 3 image planes to yield 8 x 3 = 24 bits/pixel
Too few levels may cause false contour

10. Same Pixel Size, different Sizes

11. Same Size, Different Pixel Sizes

12. Varying Gray Level Resolution

13. Size, Quantization Levels and Details
Isopreference curves

14. Aliasing: Moiré Effect

15. Zooming and Interpolation

16. Sampling Theory
A band-limited continuous signal can be reconstructed perfectly from its discrete-time samples if the sampling rate is above the Nyquist rate
Frequency domain interpretation:
BL continuous time signal f
Sampled discrete time signal f
reconstructed cont. time signal f

17. Sampling Theory Derivation
xc(t): Band-limited continuous time signal
X() = 0 for |  | o, X(): Fourier spectrum
Sampled at t = nT where T: sampling period, and fs = 1/T: sampling frequency
x(n) = xc(nT) has spectrum
Note that
X() = X(T) is a periodic extension of X()
To reconstruct, convolve with inverse Fourier transform of an ideal low pass filter, leading to a sinc function in spatial domain.
Often approximate the sinc function by pulse (sample and hold circuit), followed by a low pass filter.