Iterative maximum likelihood displacement field estimation in quantum-limited image sequences

Abstract

We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel).