Notes

Slide Show

Outline

1	Image Sampling
2	Announcements Photoshop help sessions for project 1 12-1, Wednesday, Sieg 322
3	Image Scaling
4	Image sub-sampling
5	Image sub-sampling
6	Even worse for synthetic images
7	Sampling and the Nyquist rate Aliasing can arise when you sample a continuous signal or image occurs when your sampling rate is not high enough to capture the amount of detail in your image Can give you the wrong signal/image—an alias formally, the image contains structure at different scales called “frequencies” in the Fourier domain the sampling rate must be high enough to capture the highest frequency in the image To avoid aliasing: sampling rate > 2 * max frequency in the image This minimum sampling rate is called the Nyquist rate
8	2D example
9	Sampling
10	Reconstruction
11	"What happens when" What happens when the sampling rate is too low?
12	"Anti-aliasing by" Anti-aliasing by pre-filtering theoretical ideal pre-filter is a sinc function Gaussian, cubic filters work better in practice
13	Subsampling with Gaussian pre-filtering
14	Subsampling with Gaussian pre-filtering
15	Compare with...
16	Some times we want many resolutions
17	Gaussian pyramid construction
18	Image resampling So far, we considered only power-of-two subsampling What about arbitrary scale reduction? How can we increase the size of the image?
19	Image resampling So far, we considered only power-of-two subsampling What about arbitrary scale reduction? How can we increase the size of the image?
20	Image resampling So what to do if we don’t know
21	Resampling filters What does the 2D version of this hat function look like?
22	Bilinear interpolation A simple method for resampling images
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