A Dynamic Programming Solution to Bounded Dejittering Problems

We propose a dynamic programming solution to image dejittering problems with bounded displacements and obtain efficient algorithms for the removal of line jitter, line pixel jitter, and pixel jitter.Comment: The final publication is available at link.springer.co

A system for reconstruction of missing data in image sequences using sampled 3D AR models and MRF motion priors

This paper presents a new technique for interpolating missing data in image sequences. A 3D autoregressive (AR) model is employed and a sampling based interpolator is developed in which reconstructed data is generated as a typical realization from the underlying AR process, rather than e.g. least squares (LS). In this way a perceptually improved result is achieved. A hierarchical gradient-based motion estimator, robust in regions of corrupted data, employing a Markov random field (MRF) motion prior is also presented for the estimation of motion before interpolation

Joint detection, interpolation, motion and parameter estimation for image sequences with missing data

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 and an adaptation of the Gibbs Sampler [1, 2] is used for optimization of the resulting non-linear objective functions. 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