论文信息 - Joint interpolation, motion and parameter estimation for image sequences with missing data

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

This paper presents a new scheme for interpolation of missing data in image sequences, an important problem in many areas including archived motion picture film and digital video. A unified framework for image data modelling and motion estimation is adopted which is based on 3-dimensional autoregressive (3DAR) models with motion correction. A fully Bayesian methodology is implemented using the Gibbs Sampler, a method which allows for joint estimation with respect to all of the unknowns, including the motion field.

Anil C. Kokaram | Simon J. Godsill

[1] R. Kohn,et al. On Gibbs sampling for state space models , 1994 .

[2] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[3] W. R. Buckland,et al. Distributions in Statistics: Continuous Multivariate Distributions , 1973 .

[4] Simon J. Godsill,et al. Robust treatment of impulsive noise in speech and audio signals , 1996 .

[5] Donald Geman,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[6] G. C. Tiao,et al. Bayesian inference in statistical analysis , 1973 .

[7] Samuel Kotz,et al. Continuous univariate distributions : distributions in statistics , 1970 .

[8] Anil C. Kokaram,et al. Interpolation of missing data in image sequences , 1995, IEEE Trans. Image Process..

[9] Anil C. Kokaram,et al. A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors , 1996, ECCV.