Dynamics of the maximum marginal likelihood hyperparameter estimation in image restoration: gradient descent versus expectation and maximization algorithm.

Dynamical properties of image restoration and hyperparameter estimation are investigated by means of statistical mechanics. We introduce an exactly solvable model for image restoration and derive differential equations with respect to macroscopic quantities. From these equations, we evaluate relaxation processes of the system to the equilibrium state. Our statistical mechanical approach also enables us to investigate the hyperparameter estimation by means of maximization of the marginal likelihood by using gradient descent and the expectation and maximization algorithm from the dynamical point of view.