Reconstructing missing regions in colour images using multichannel median models

This paper presents a method for reconstructing missing regions in colour images. A multichannel median model is proposed as the underlying image model and a statistical framework is employed to generate sampled realisations of the missing data. The nature of the model leads to a posterior expression for the missing data that does not involve an easy to manipulate multivariate probability distribution. Therefore, the problem of sampling is solved using a numerical approach. Results are included which show that this approach leads to excellent reconstructions.

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

[2]  M. M. Fahmy,et al.  On the multivariate distributions of order statistics of discrete-state processes , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[3]  David Suter,et al.  Restoration of historic film for digital compression: a case study , 1995, Proceedings., International Conference on Image Processing.

[4]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  J. Besag On the Statistical Analysis of Dirty Pictures , 1986 .