纹理合成的自相关性判别法及其应用

从加快纹理相似性的判别速度出发，提出了一种纹理合成的自相关性判别法.传统的纹理合成算法随着邻域和样本的增大，计算量将成倍增加，纹理合成速度减慢的劣势逐渐体现出来.因此，算法对样本纹理建立简单的自相关性距离查找表，利用L邻域内像素的自相关性距离作为像素匹配的判别依据，以查找取代传统匹配过程中的繁琐计算，极大地加快了合成速度，可实现动态的、多精度的合成效果调控，以及避免块匹配中易出现纹理接缝的问题.经验证，该算法可在纹理合成、图像修补及纹理检索中应用，并可很好地达到实时的应用要求.

Texture Synthesis with Self-Correlation Distinguishing and Its Applications

Texture synthesis from sample is a most important part of computer graphics. And the key of the texture synthesis technique from samples is local texture similarity matching. In order to improve the speed of texture comparability distinguishing, a method of texture synthesis with self-relativity distinguishing is presented in this paper. With the increase in the size of neighborhood and sample, the amount of calculation in the traditional texture synthesis approach will grow quickly and the inferior position in the synthesis speed will tack on progressively. And in the neighborhood L, the distribution of pixel is consectary at the geometry but discrete at the color space. So, this algorithm sets up a simple finding-list of self-relativity distance to the sample texture. By using the self-relativity distance of pixels in the neighborhood L as the distinguishing rule, the algorithm uses finding instead of fussy calculation in the traditional approach. It reflects the inter-character and the correlation of the texture into the distinguishing rule, quickens the speed of texture synthesis, and adjusts the multiple precision texture synthesis to avoid the problem of texture joint. After the examination, the algorithm presented has vast application in texture synthesis, image repair and texture searches, and fits for the demand of real-time in application.