Edge-preserving MAP-estimation of motion vector fields in noisy low-dose x-ray image sequences

We describe a motion compensated temporally recursive noise reduction technique especially suited for sequences of moving X-ray images, where we focus on a robust motion estimator which is able to deal with the high noise levels in such images. These noise levels are caused by the very low X-ray dose rates used in medical real-time imaging (quantum-limited imaging). The robustness of our motion estimator is achieved by spatiotemporal regularization using a generalized Gauss-Markov random field. Unlike quadratic regularization by Gauss-Markov random fields, generalized Gauss-Markov random fields are able to account for motion edges without the need to explicitly specify an detection threshold. Instead, our model controls edges by a 'soft' parameter, which gradually allows the regularization term to behave like a median filter, which preserves edges without using detection thresholds.