Adaptive Regularization of Ill-Posed Problems: Application to Non-rigid Image Registration

Details Adaptive Regularization of Ill-Posed Problems: Application to Non-rigid Image Registration Adaptive Regularization of Ill-Posed Problems: Application to Non-rigid Image Registration

2009-06-17

arxiv.org/abs/0906.3323v1

Computer Science

Computer Vision and Pattern Recognition

Scientific paper

We introduce an adaptive regularization approach. In contrast to conventional Tikhonov regularization, which specifies a fixed regularization operator, we estimate it simultaneously with parameters. From a Bayesian perspective we estimate the prior distribution on parameters assuming that it is close to some given model distribution. We constrain the prior distribution to be a Gauss-Markov random field (GMRF), which allows us to solve for the prior distribution analytically and provides a fast optimization algorithm. We apply our approach to non-rigid image registration to estimate the spatial transformation between two images. Our evaluation shows that the adaptive regularization approach significantly outperforms standard variational methods.

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