COMPARATIVE ANALYSIS OF STOCHASTIC OPTIMIZATION ALGORITHMS FOR IMAGE REGISTRATION
This work is devoted to comparative experimental analysis of different stochastic optimization algorithms for image registration in spatial domain: stochastic gradient descent, Momentum, Adagrad, RMSprop, Adam. Correlation coefficient is considered as the objective function. Experiments are performed on synthetic data generated via wave model with different correlation function close to the Gaussian.