Let's say I have a computer screen, or a TV, or a flat panel ceiling lamp, i.e. a certain luminance spread over an area.
If I wanted to determine, how homogeneous this source of light is, I'd take a picture of it and put it through a fourier transformation, e.g. a DFT.
If the panel were perfectly homogeneous, there'd only be one single peak at the zeroth frequency and nothing else. Of course, they never are, and the calculations are done in a discrete system, so it will resemble a 1/x function. Depending on the amplitudes at frequencies > 0, the panel will be homogeneous to a certain degree. High frequencies might indicate sharp edges, circular blobs in the actual image might be represented by circles in the amplitudes spectrum as well.
From such an amplitude spectrum alone, it would be very difficult to get a sharp definition for "good" or "bad".
As far as I know, the phase spectrum of an image contains most of the information for reconstructing the actual image. But looking at the phase spectrum, I see just "noise". Would it be possible to use the phase information as well?
How would you determine, from a fourier spectrum alone, if the source of light is homogeneous or not?
I have been reading about fourier transforms for the last few months, but I feel like I am still missing a huge point when it comes to connecting fourier transformations and image processing. It seems everyone is just using the amplitude information of an image while being in the fourier space, and ignoring phase.