This work is devoted to comparative experimental analysis of different stochastic optimization algorithms for image registration in spatial domain: stochastic gradient descent, Momentum, Nesterov momentum, Adagrad, RMSprop, Adam. Correlation coefficient is considered as the objective function. Experiments are performed on synthetic data generated via wave model with different noise-to-signal ratio.
An optimization criterion is suggested for the plan of counts in a local sample used to determine the pseudogradient of the objective function of estimation quality. The use of the criterion is considered in the case when the object functions are defined as the interframe difference mean square, the covariance, and the interframe correlation coefficient. The optimization is directed at increasing the convergence rate of esti mates of the parameters of geometric interframe image deformations.
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