Bayesian motion estimation for temporally recursive noise reduction in X-ray fluoroscopy

This paper develops a Bayesian motion estimation algorithm for motion-compensated temporally recursive filtering of moving low-dose X-ray images (X-ray fluoroscopy). These images often exhibit a very low signal-to-noise ratio. The described motion estimation algorithm is made robust against noise by spatial and temporal regularization. A priori expectations about the spatial and temporal smoothness of the motion vector field are expressed by a generalized Gauss-Markov random field. The advantage of using a generalized Gauss-Markov random field is that, apart from smoothness, it also captures motion edges without requiring an edge detection threshold. The costs of edges are controlled by a single parameter, by means of which the influence of the regularization can be tuned from a median-filter-like behaviour to a linear-filter-like one.