Technical Section: Texture synthesis based on Direction Empirical Mode Decomposition
Computers and Graphics
On texture and image interpolation using Markov models
Image Communication
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Super-resolution texture synthesis using stochastic PAR/NL model
Journal of Visual Communication and Image Representation
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Noise space decomposition method for two-dimensional sinusoidal model
Computational Statistics & Data Analysis
Fast texture synthesis with cellular neural network-based patch stitching
International Journal of Circuit Theory and Applications
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A unified texture model that is applicable to a wide variety of texture types found in natural images is presented. This model leads to the derivation of texture analysis and synthesis algorithms designed to estimate the texture parameters and to reconstruct the original texture field from these parameters. The texture field is assumed to be a realization of a regular homogeneous random field, which is characterized in general by a mixed spectral distribution. The texture field is orthogonally decomposed into a purely indeterministic component and a deterministic component. The deterministic component is further orthogonally decomposed into a harmonic component, and a generalized-evanescent component. Both analytical and experimental results show that the deterministic components should be parameterized separately from the purely indeterministic component. The model is very efficient in terms of the number of parameters required to faithfully represent textures. Reconstructed textures are practically indistinguishable from the originals