Near-Regular Texture Synthesis by Random Sampling and Gap Filling

Abstract

This paper addresses the synthesis of near-regular textures, i.e. textures that consist of a regular global structure plus subtle yet very characteristic stochastic irregularities. Such textures are difficult to synthesize due to the complementary characteristics of these structures. In this paper, we propose a method which we call Random Sampling and Gap Filling (RSGF) to synthesize near-regular textures. The synthesis approach is guided by a lattice of the global structure estimated from a generalized normalized autocorrelation of the sample image. This lattice constrains a random sampling process to maintain the global regular structure yet ensuring the characteristic randomness of the irregular structures. Results presented in this paper show that our method does not only produce convincing results for regular or near-regular textures but also for irregular textures.