Parametric texture synthesis for filling holes in pictures
Anil C. Kokaram · Proceedings - International Conference on Image Processing · 2003
This paper presents a framework for "filling in" missing gaps in images and particularly patches with texture. 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 repose 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.