Multiscale Exemplar Based Texture Synthesis by Locally Gaussian Models

摘要

In exemplar based texture synthesis methods one of the major difficulties is to synthesize correctly the wide diversity of texture images. So far the proposed methods tend to have satisfying results for specific texture classes and fail for others. Statistics-based algorithms present good results when synthesizing textures that have few geometric structures and are able to preserve a complex statistical model of the sample texture. On the other hand, non-parametric patch-based methods have the ability to reproduce faithfully highly structured textures but lack a mechanism to preserve its global statistics. Furthermore, they are strongly dependent on a patch size that is decided manually. In this paper we propose a multiscale approach able to combine advantages of both strategies and avoid some of their drawbacks. The texture is modeled at each scale as a spatially variable Gaussian vector in the patch space, which allows to fix a patch size fairly independent of the texture.