This question is about converting from spatial to frequency domains when performing an FFT on 2D image data. I suspect my problem has a painfully obvious solution that I'm just not seeing. If I have some image data g(x,y) where I know the pixel resolution, say 256x256 pels at a resolution of 0.1 μm/pel, then I know my image is 25.6μm across in both the X and Y axes. When I take the FFT of this image and rotate quadrants to centre the low-frequency components (using, for example, MATLAB's fft() and fftshift() functions), how can I obtain the frequency associated with each pixel in the image? I'm quite confused about this. The temptation is to simply compute the radial distance of each pixel relative to the centre of the spatial image g(x,y) in units of μm, and then take the reciprocal of these values. Obviously this would give me an infinity at the origin at the centre of the spatial image before rotating quadrants to centre low-frequency components. This seems wrong, but I might be wrong about being wrong. I need to apply a low-pass filter to this image data at a specific cutoff frequency, and I'm not an optics whiz so some help would be great.