Fig. 3

(a): the original graphical depiction of the Denoising Diffusion Probabilistic Model by Ho et al., representing a denoising Markov Chain with T steps – for a given noisy sample t, a neural network is trained to approximate the next denoised image t-1 through calculating a probabilistic distribution of denoised data [13]; (b): a customized illustration of the conceptual framework of UNIT-DDPM, proposed by Sasaki et al. [15]