An FFT will transform spatial data ( ie. a picture ) to a spatial frequency domain. This spatial frequency information sort of shows the amounts of detail in the spatial data. Say you had a 64x64 array which was a sine wave as a function of X only, the array would look like a plowed field. And say this plowed field had 5 furrows across it, when you do a FFT you will find that there are dots at F(-5,0) and F(5,0).
Inverse FFT:
Much the same as FFT only it transforms spatial frequency data to spatial data.
Uses for FFT:
Several complex operations can be done simply with FFTs.
Convolutions of two data windows can be done by taking FFTs of both windows, multiplying the magnitudes and adding the phases. Then inverse FFT the resulting magnitude and phase to yield a convolution.
Filtering of a data window can be done by taking an FFT of that window and then using one of the drawing tools remove the frequencies desired. Then perform an inverse FFT to yield a filtered data window. Removing high frequencies smooth, removing low frequencies sharpen.
Color Scanning/Automated Scan:
Color scanning/Automated Scan can help you find hidden detail in a data set.
Statistics:
Most times this will show the user what his data looks like statistically, if it does not look right chances are it isn't. Close that statistics window and do a Calc Statistics, then do Statistics again.
Histogram Equalization:
If you have a data window whose gray scale plot is being saturated by some very high values do the following to improve visibility of washed out data. Select an area of low contrast, select ReScale Window from the Graphics menu. The selection you made will be scanned for a maximum and minimum value. This max and min will be use to scale your data's gray scale representation.
