Random Phase Textures: Theory and Synthesis

• Published in: IEEE transactions on image processing Vol. 20; no. 1; pp. 257 - 267
• Main Authors: Galerne, B, Gousseau, Y, Morel, J
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Asymptotic properties; Color; Computer Science; Digital images; Engineering Sciences; Exact sciences and technology; Fourier transforms; Graphics; Humans; Image processing; Information, signal and communications theory; Mathematical analysis; Mathematical model; Mathematical models; Noise; Pattern recognition; Permission; Phase noise; Preprocessing; Random phase; shot noise; Signal and Image Processing; Signal processing; spot noise; Statistics; Stochastic processes; Surface layer; Telecommunications and information theory; Texture; texture synthesis; Phase noise; Random noise; Random phase; Artefact; Stochastic process; Algorithm; Texture; Color image; Discrimination; Mathematical analysis; texture synthesis; Shot noise; spot noise; Pattern synthesis; shot noise; Texture synthesis; random phase
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2010.2052822

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.