Image sequence filtering in quantum-limited noise with applications to low-dose fluoroscopy
Clinical angiography requires hundreds of X-ray images, putting the patients and particularly the medical staff at risk. Dosage reduction involves an inevitable sacrifice in image quality. In this work, the latter problem is addressed by first modeling the signal-dependent, Poisson-distributed noise that arises as a result of this dosage reduction. The commonly utilized noise model for single images is shown to be obtainable from the new model. Stochastic temporal filtering techniques are proposed to enhance clinical fluoroscopy sequences corrupted by quantum mottle. The temporal versions of these filters as developed here are more suitable for filtering image sequences, as correlations along the time axis can be utilized. For these dynamic sequences, the problem of displacement field estimation is treated in conjunction with the filtering stage to ensure that the temporal correlations are taken along the direction of motion to prevent object blur.