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 |