| Photoshop help sessions for project 1 | ||
| 12-1, Wednesday, Sieg 322 | ||
Even worse for synthetic images
| 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 | |||
| What happens when | |
| the sampling rate | |
| is too low? |
| Anti-aliasing by | ||
| pre-filtering | ||
| theoretical ideal pre-filter is a sinc function |
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| Gaussian, cubic filters work better in practice |
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Subsampling with Gaussian pre-filtering
Subsampling with Gaussian pre-filtering
Some times we want many resolutions
| So far, we considered only power-of-two subsampling | ||
| What about arbitrary scale reduction? | ||
| How can we increase the size of the image? | ||
| So far, we considered only power-of-two subsampling | ||
| What about arbitrary scale reduction? | ||
| How can we increase the size of the image? | ||
| So what to do if we don’t know |
| What does the 2D version of this hat function look like? |
| A simple method for resampling images |