CMLA, ENS Cachan, CNRS, UniverSud, F-94230 Cachan, France.
IEEE Trans Image Process. 2011 Jan;20(1):257-67. doi: 10.1109/TIP.2010.2052822. Epub 2010 Jun 14.
This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample.
随机相位噪声（RPN）和渐近离散点噪声（ADSN）。这些模型允许合成随机相位纹理。它们可以说是源自两个早期朱利叶斯纹理辨别理论的线性化版本。随后的数学分析表明，与文献中的一些说法相反，RPN 和 ADSN 是不同的随机过程。然而，大量实验也表明，这些算法从相同样本中获得的纹理在感知上是相似的。这项研究的意义在于提出了三个技术贡献，为解决 RPN 或 ADSN 用于模拟纹理的障碍提供了解决方案。首先，将 RPN 和 ADSN 算法扩展到彩色图像。其次，提出了一种预处理方法来避免由于真实世界纹理样本的非周期性而产生的伪影。最后，该方法被扩展到从给定样本中合成任意大小的纹理。
