A statistical framework for picture reconstruction using 2D AR models
This paper presents a framework for Filling In missing gaps in images and particularly patches with texture. The algorithm can also be used as a fallback mode in treating missing data for video sequence reconstruction. The underlying idea is to construct a parametric model of the p.d.f. of the texture to be re-synthesised and then draw samples from that p.d.f. to create the resulting reconstruction. A Bayesian approach is used to articulate 2D Autoregressive Models as generative models for texture (using the Gibbs sampler) given surrounding boundary conditions. A fast implementation is presented that iterates between pixelwise updates and blockwise parametric model estimation. The novel ideas in this paper are joint parameter estimation and fast, efficient texture reconstruction using linear models.
filling in 2d autoregressive models video reconstruction statistical interpolation texture synthesis image reconstruction image restoration gibbs sampling bayesian inference