Repetitiveness Metric of Exemplar for Texture Synthesis

Abstract

Texture synthesis has become a well-established area. However, researchers are mostly concerned with learning the algorithm of texture synthesis to achieve higher quality and better efficiency. We hereby propose a repetitiveness metric method to pick out an optimal texture exemplar which is used to synthesize texture. Different from conventional methods of texture analysis that emphasize on texture feature analysis for the target textures, our method focuses on repetitiveness metric of texture exemplar. To achieve a more efficient method, we firstly perform a Poisson disk sampling to extract unordered texture exemplars from the input image. Using normalized cross correlation (NCC) based on fast Fourier transformation (FFT) for each exemplar, we can get some matrices. Based on repetitiveness metric, we can assign each exemplar a score. Our method can satisfy visual requirement and accomplish high-quality work in a shorter time due to FFT. Compelling visual results and computational complexity analyses prove the validity of our work.