Texture synthesis‐by‐analysis method based on a multiscale early‐vision model

A new texture synthesis-by-analysis method, applying a visu- ally based approach that has some important advantages over more traditional texture modeling and synthesis techniques is introduced. The basis of the method is to encode the textural information by sampling both the power spectrum and the histogram of homogeneously textured images. The spectrum is sampled in a log-polar grid using a pyramid Gabor scheme. The input image is split into a set of 16 Gabor channels (using four spatial frequency levels and four orientations), plus a low- pass residual (LPR). The energy and equivalent bandwidths of each channel, as well as the LPR power spectrum and the histogram, are measured and the latter two are compressed. The synthesis process consists of generating 16 Gabor filtered independent noise signals with spectral centers equal to those of the Gabor filters, whose energy and equivalent bandwidths are calculated to reproduce the measured values. These bandpass signals are mixed into a single image, whose LPR power spectrum and histogram are modified to match the original fea- tures. Despite the coarse sampling scheme used, very good results have been achieved with nonstructured textures as well as with some quasi- periodic textures. Besides being applicable to a wide range of textures, the method is robust (stable, fully automatic, linear, and with a fixed code length) and compact (it uses only 69 parameters). © 1996 Society of Photo- Optical Instrumentation Engineers.

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

[2]  Azriel Rosenfeld,et al.  Multiresolution image processing and analysis , 1984 .

[3]  Tor Lønnestad,et al.  An evaluation of stochastic models for analysis and synthesis of gray-scale texture , 1994, Pattern Recognit. Lett..

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

[5]  Rafael Fonolla Navarro,et al.  Gaussian wavelet transform: Two alternative fast implementations for images , 1991, Multidimens. Syst. Signal Process..

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

[7]  Songde Ma,et al.  Sequential synthesis of natural textures , 1985, Comput. Vis. Graph. Image Process..

[8]  B. MacLennan Gabor Representations of Spatiotemporal Visual Images , 1991 .

[9]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  A Gagalowicz,et al.  A Parallel Method for Natural Texture Synthesis. , 1983 .

[11]  M. Porat,et al.  Localized texture processing in vision: analysis and synthesis in the Gaborian space , 1989, IEEE Transactions on Biomedical Engineering.

[12]  Gabriel Cristóbal,et al.  Space and frequency variant image enhancement based on a Gabor representation , 1994, Pattern Recognit. Lett..

[13]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[14]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[15]  Oscar Nestares,et al.  Efficient spatial-domain implementation of a multiscale image representation based on Gabor functions , 1998, J. Electronic Imaging.