IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital processing of random signals: theory and methods
Digital processing of random signals: theory and methods
Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Digital Picture Processing
Skewed α-stable distributions for modelling textures
Pattern Recognition Letters
Generating Sub-Resolution Detail in Images and Volumes Using Constrained Texture Synthesis
VIS '04 Proceedings of the conference on Visualization '04
Wavelet-based image interpolation using multilayer perceptrons
Neural Computing and Applications
Texture design using a simplicial complex of morphable textures
ACM SIGGRAPH 2005 Papers
Non-stationary signal processing using time-frequency filter banks with applications
Signal Processing - Fractional calculus applications in signals and systems
Correlation-based approach to color image compression
Image Communication
A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified texture model based on a 2-D Wold-like decomposition
IEEE Transactions on Signal Processing
On the Approximation of Inner Products From Sampled Data
IEEE Transactions on Signal Processing
Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise
IEEE Transactions on Information Theory
Classification of binary random patterns
IEEE Transactions on Information Theory
The effects of a visual fidelity criterion of the encoding of images
IEEE Transactions on Information Theory
MOMS: maximal-order interpolation of minimal support
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
A vector-quantization approach to coding systems
ACM Communications in Computer Algebra
Super-resolution texture synthesis using stochastic PAR/NL model
Journal of Visual Communication and Image Representation
Model-based adaptive resolution upconversion of degraded images
Journal of Visual Communication and Image Representation
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Markov-type models characterize the correlation among neighboring pixels in an image in many image processing applications. Specifically, a wide-sense Markov model, which is defined in terms of minimum linear mean-square error estimates, is applicable to image restoration, image compression, and texture classification and segmentation. In this work, we address first-order (auto-regressive) wide-sense Markov images with a separable autocorrelation function. We explore the effect of sampling in such images on their statistical features, such as histogram and the autocorrelation function. We show that the first-order wide-sense Markov property is preserved, and use this result to prove that, under mild conditions, the histogram of images that obey this model is invariant under sampling. Furthermore, we develop relations between the statistics of the image and its sampled version, in terms of moments and generating model noise characteristics. Motivated by these results, we propose a new method for texture interpolation, based on an orthogonal decomposition model for textures. In addition, we develop a novel fidelity criterion for texture reconstruction, which is based on the decomposition of an image texture into its deterministic and stochastic components. Experiments with natural texture images, as well as a subjective forced-choice test, demonstrate the advantages of the proposed interpolation method over presently available interpolation methods, both in terms of visual appearance and in terms of our novel fidelity criterion.