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EDIT: To finish off this topic: I found out that the observed sinc-like pattern in the spectrum was not caused by windowing of the FFT, but rather by the pattern of the pixels that (unfortunately) still show up significantly in the tracked signal. Combined with the interpolation of the pixels, this results in a convolution with a triangular kernel (see linear interpolation and its frequency response; so sinc^2 see here)

In the end, what led me to the realization, was the fact that due to the inverse behavior in Fourier space any large patterns are produced by a small structure in real-space (see also one of my comments), as is the case for the windowing-effect of the FFT.

I marked the answer by Eric Jacobsen as correct, since he suggested, that the structure may also be caused by something very different from the windowing-effect of the FFT.

Notes 2: Follow-up on effect of Zero-padding on FFT With regards to the scaling behavior of the spectra, I performed the following tests as suggested by Eric Jacobsen. In the plots below I show the same dataset performing the FFT with varying levels of zero-padding. Color coding of plots of the FFT with different zero-paddings: red: no padding, green: +1 zero padding, blue: +4 zero padding, cyan: +6 zero padding data-length: 2048 (for each the same)

Plot of the FFT of the contour of a single movie frame

Plot of the fluctuations of the spectrum calculated as the variance of the values of each mode:

<|c|^2>_t-<|c|>_t^2

where c is the FFT of the signal.

Plots of the two components of the variance of the non-zero-padded transform:

red: <|c|^2>_t, blue: <|c|>_t^2

As can be seen, the contribution due to the window-function is present in both in slightly different intensity and thus is not cancelled out, when calculating the difference.