A multiresolution approach for texture synthesis using the circular harmonic functions

In this paper, an unsupervised model-based texture reproduction technique is described. In accordance with the Julesz's (1962) conjecture, the statistical properties of the prototype up to the second order are copied in order to generate a synthetic texture perceptually indistinguishable from the given sample. However, this task is accomplished using a hybrid approach which operates partially in the spatial domain and partially in a multiresolution domain. The latter employed is the circular harmonic function (CHF) domain since it has been proven to be well suited for mimicking the behavior of the human visual system (HVS). This approach allows, for a wide range of textures typologies, obtaining synthetic textures that better match the prototype with respect to the ones obtained using techniques based on the Julesz's conjecture operating only in the spatial domain, and to dramatically reduce the computational complexity of similar methods operating only in the multiresolution domain.

[1]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Béla Julesz,et al.  Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.

[3]  C. A. Berenstein,et al.  Exact deconvolution for multiple convolution operators-an overview, plus performance characterizations for imaging sensors , 1990, Proc. IEEE.

[4]  Jitendra K. Tugnait,et al.  Estimation of linear parametric models of non-Gaussian discrete random fields , 1991, Electronic Imaging.

[5]  D H Hubel,et al.  Brain mechanisms of vision. , 1979, Scientific American.

[6]  Giovanni Jacovitti,et al.  Multiresolution circular harmonic decomposition , 2000, IEEE Trans. Signal Process..

[7]  Alessandro Neri,et al.  Reduced complexity modeling and reproduction of colored textures , 2000, IEEE Trans. Image Process..

[8]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[9]  R. W. Lucky,et al.  Techniques for adaptive equalization of digital communication systems , 1966 .

[10]  Stéphane Mallat,et al.  Multifrequency Channel Decompositions of Images , 1989 .

[11]  Alessandro Neri,et al.  Multiscale image features analysis with circular harmonic wavelets , 1995, Optics + Photonics.

[12]  Gaetano Scarano,et al.  A Bayesian approach to blind equalization using fractional sampling , 1999, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99.

[13]  J. Cadzow,et al.  Image texture synthesis-by-analysis using moving-average models , 1993 .

[14]  Alessandro Neri,et al.  Nonlinear prediction in the 2D Wold decomposition for texture modeling , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[15]  Georgios B. Giannakis,et al.  Bispectral analysis and model validation of texture images , 1995, IEEE Trans. Image Process..

[16]  B. V. K. Vijaya Kumar,et al.  Optimal tradeoff circular harmonic function correlation filter methods providing controlled in-plane rotation response , 2000, IEEE Trans. Image Process..

[17]  Ibrahim M. Elfadel,et al.  Gibbs Random Fields, Cooccurrences, and Texture Modeling , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Alessandro Neri,et al.  Texture synthesis-by-analysis with hard-limited Gaussian processes , 1998, IEEE Trans. Image Process..

[19]  H H Arsenault,et al.  Properties of the circular harmonic expansion for rotation-invariant pattern recognition. , 1986, Applied optics.

[20]  Rama Chellappa,et al.  Texture synthesis using 2-D noncausal autoregressive models , 1985, IEEE Trans. Acoust. Speech Signal Process..

[21]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[22]  Gaetano Scarano,et al.  Bussgang-zero crossing equalization: an integrated HOS-SOS approach , 2001, IEEE Trans. Signal Process..

[23]  Alessandro Neri,et al.  A perceptually lossless, model-based, texture compression technique , 2000, IEEE Trans. Image Process..

[24]  John Daugman,et al.  Six formal properties of two-dimensional anisotropie visual filters: Structural principles and frequency/orientation selectivity , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  Joseph M. Francos,et al.  A unified texture model based on a 2-D Wold-like decomposition , 1993, IEEE Trans. Signal Process..

[26]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Stéphane Mallat,et al.  Wavelets for a vision , 1996, Proc. IEEE.

[28]  C.-C. Jay Kuo,et al.  Texture Roughness Analysis and Synthesis via Extended Self-Similar (ESS) Model , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  J. V. Vleck,et al.  The spectrum of clipped noise , 1966 .

[30]  Eero P. Simoncelli,et al.  Texture modeling and synthesis using joint statistics of complex wavelet coefficients , 1999 .

[31]  Alessandro Neri,et al.  Complex reflectivity based non-minimum phase deconvolutionComplex reflectivity based non-minimum phase deconvolution , 1987 .

[32]  Alessandro Neri,et al.  Synthesis-by-analysis of complex textures , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[33]  Alessandro Neri,et al.  Content-based image classification with circular harmonic wavelets , 1998, Defense, Security, and Sensing.

[34]  C.-C. Jay Kuo,et al.  An improved method for 2-D self-similar image synthesis , 1996, IEEE Trans. Image Process..

[35]  B. Julesz,et al.  Human factors and behavioral science: Textons, the fundamental elements in preattentive vision and perception of textures , 1983, The Bell System Technical Journal.