Joint Detection, Interpolation, Motion and Parameter estimation for Image Sequences with Missing Data

Abstract

This paper presents methods for detection and reconstruction of `missing' data in image sequences which can be modelled using 3-dimensional autoregressive (3D-AR) models. The interpolation of missing data is important in many areas of image processing, including the restoration of degraded motion pictures, reconstruction of drop-outs in digital video and automatic `re-touching' of old photographs. Here a probabilistic Bayesian framework is adopted. The method assumes no prior knowledge of the motion field or 3D-AR model parameters as these are estimated jointly with the missing image pixels. Incorporating a degradation model into the framework allows detection to proceed jointly with interpolation