Texture synthesis using 2-D noncausal autoregressive models
Rama Chellappa, R.L. Kashyap · IEEE Transactions on Acoustics Speech and Signal Processing · 1985
The purpose of this paper is to illustrate the usefulness of two dimensional noncausal autoregressive (NCAR) models for the synthesis of textures. These models characterize the gray level at a pixel as a linear combination of gray levels at nearby locations in all directions and an additive white noise variable. We first show that the class of NCAR models is capable of generating a wide variety of image patterns posessing the local replication attribute, an essential ingredient of many natural textures. It is also shown that the theoretical variograms of many NCAR models possess an oscillatory behavior, a characteristic of the variograms of many natural textures. Next, we give experimental results of synthesis of 64 × 64 textures resembling several real textures in the Brodatz album. The synthetic textures generated by 16 parameter NCAR models retain most the visual characteristics of the original textures. The variograms of the original and synthetic textures are also similar.