1. Image Processing Basics

2. What are images? An image is a 2-d rectilinear array of pixels

3. Pixels as samples A pixel is a sample of a continuous function

4. Images are Ubiquitous Input Optical photoreceptors Digital camera CCD array Rays in virtual camera (why?) Output TVs Computer monitors Printers

5. Properties of Images Spatial resolution Width pixels/width cm and height pixels/ height cm Intensity resolution Intensity bits/intensity range (per channel) Number of channels RGB is 3 channels, grayscale is one channel

6. Image errors Spatial aliasing Not enough spatial resolution Intensity quantization Not enough intensity resolution

7. Two issues Sampling and reconstruction Creating and displaying images while reducing spatial aliasing errors Halftoning techniques Dealing with intensity quantization

8. Sampling and reconstruction

9. Aliasing Artifacts caused by too low sampling frequency (undersampling) or improper reconstruction Undersampling rate determined by Nyquist limit (Shannon’s sampling theorem)

10. Aliasing in computer graphics In graphics, two major types Spatial aliasing Problems in individual images Temporal aliasing Problems in image sequences (motion)

11. Spatial Aliasing “Jaggies”

12. Spatial aliasing Ref: SIGGRAPH aliasing tutorial

13. Spatial aliasing Texture disintegration Ref: SIGGRAPH aliasing tutorial

14. Temporal aliasing Strobing Stagecoach wheels in movies Flickering Monitor refresh too slow Frame update rate too slow CRTs seen on other video screens

15. Antialiasing Sample at a higher rate What if the signal isn’t bandlimited? What if we can’t do this, say because the sampling device has a fixed resolution? Pre-filter to form bandlimited signal Low pass filter Trades aliasing for blurring Non-uniform sampling Not always possible, done by your visual system, suitable for ray tracing Trades aliasing for noise

16. Sampling Theory Two issues What sampling rate suffices to allow a given continuous signal to be reconstructed from a discrete sample without loss of information? What signals can be reconstructed without loss for a given sampling rate?

17. Spectral Analysis Spatial (time) domain:Frequency domain: Any (spatial, time) domain signal (function) can be written as a sum of periodic functions (Fourier)

18. Fourier Transform

19. Fourier transform: Inverse Fourier transform:

20. Sampling theorem A signal can be reconstructed from its samples if the signal contains no frequencies above ½ the sampling frequency. -Claude Shannon The minimum sampling rate for a bandlimited signal is called the Nyquist rate A signal is bandlimited if all frequencies above a given finite bound have 0 coefficients, i.e. it contains no frequencies above this bound.

21. Filtering and convolution Convolution of two functions (= filtering): Convolution theorem: Convolution in the frequency domain is the same as multiplication in the spatial (time) domain, and Convolution in the spatial (time) domain is the same as multiplication in the frequency domain

22. Filtering, sampling and image processing Many image processing operations basically involve filtering and resampling. Blurring Edge detection Scaling Rotation Warping

23. Resampling Consider reducing the image resolution:

24. Resampling The problem is to resample the image in such a way as to produce a new image, with a lower resolution, without introducing aliasing. Strategy- Low pass filter transformed image by convolution to form bandlimited signal This will blur the image, but avoid aliasing

25. Ideal low pass filter Frequency domain: Spatial (time) domain:

26. Image processing in practice Use finite, discrete filters instead of infinite continous filters Convolution is a summation of a finite number of terms rather than in integral over an infinite domain A filter can now be represented as an array of discrete terms (the kernel)

27. Discrete Convolution

28. Finite low pass filters Triangle filter

29. Finite low pass filters Gaussian filter

30. Edge Detection Convolve image with a filter that finds differences between neighboring pixels

31. Scaling Resample with a gaussian or triangle filter

32. Image processing Some other filters

33. Summary Images are discrete objects Pixels are samples Images have limited resolution Sampling and reconstruction Reduce visual artifacts caused by aliasing Filter to avoid undersampling Blurring (and noise) are preferable to aliasing