Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion

Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion

Fractional Brownian motion (fBm) exhibits both randomness and strong scale-free correlations, posing a challenge for generative artificial intelligence to replicate the underlying stochastic process. In this study, we evaluate the performance of diffusion-based inpainting methods on a specific dataset of corrupted images, which represent incomplete Euclidean distance matrices (EDMs) of fBm across various memory exponents (H). Our dataset reveals…