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 | |||
| said another way: ≥ two samples per cycle | |||
| This minimum sampling rate is called the Nyquist rate | |||
Subsampling with Gaussian pre-filtering
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 |