Synthesis of exemplar textures by self-similarity matching

Abstract

Texture synthesis is proposed to construct a large digital image from a small sample by taking advantage of its structural content. However, many approaches in texture synthesis are based on the presence of broken features at the overlap of adjacent patches. Due to inaccurate similarity measures, several optimization schemes for patch mergence may fail if these schemes cannot find satisfactory candidates from input samples. Self-similarity of candidate patches was proposed, and an algorithm was developed to perform distance error matching and alignment via the sum of self-similarity. First, when the matching window moves in the texture samples along the scan line, the sum of self-similarity replaces the sum-of-squared difference to decrease broken features or texels of outputs. Next, the synthesis speed was accelerated by eliminating redundancy calculation during matching window sliding. Finally, the proposed method enlarges the search range of the suture from one patch to all patches in the horizontal direction, and the broken features of overlaps are eliminated in the outputs. To demonstrate the performance of the proposed method, the obtained synthesis results were compared with other conventional synthesis methods. In all cases, experimental results demonstrate that the self-similarity matching-based method can decrease the number of feature discontinuities and accelerate synthesis time.