DDM is based on an algorithm proposed in Croccolo et al. and Alaimo et al., which is conveniently named differential dynamic algorithm (DDA). DDA works by subtracting images acquired at different times and taking advantage that, as the delay  between two subtracted images gets large, the energy content of the difference image increases correspondingly. A two-dimensional fast Fourier transform (FFT) analysis of the difference images allows to quantify the growth of the signal contains for each wave vector  and one can calculate the Fourier power spectrum of the difference images for different delays  to obtain the so-called image structure function . Calculation shows that for both scattering- and fluorescence-based DDM
where  is the normalized intermediate scattering function that would be measured in a dynamic light scattering (DLS) experiment,  the sample scattering intensity that would be measured in a static light scattering (SLS) experiment,  a background term due to the noise along the detection chain  a transfer function that depends on the microscope details. Equation () shows that DDM can be used for DLS experiments, provided that a model for the normalized intermediate scattering function is available. For instance, in the case of Brownian motion one has  where  is the diffusion coefficient of the Brownian particles. If the transfer function  is determined by calibrating the microscope with a suitable sample, DDM can be employed also for SLS experiments. Alternative algorithms for data analysis are suggested in.
Running DDM on a series of frames smaller than the full-frame has been called multi-DDM. This is analogous to changing the scattering volume in a light scattering experiment, but is readily carried out by selecting out of the full-frame movie. The coherence lengthscale of the dynamics can be picked up from a multi-DDM analysis.