Structural Analysis of Natural Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generating Sub-Resolution Detail in Images and Volumes Using Constrained Texture Synthesis
VIS '04 Proceedings of the conference on Visualization '04
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image resolution enhancement via data-driven parametric models in the wavelet space
Journal on Image and Video Processing
Single-frame image super-resolution through contourlet learning
EURASIP Journal on Applied Signal Processing
ACM SIGGRAPH 2008 papers
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
On texture and image interpolation using Markov models
Image Communication
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
New PAR/NL scheme for stochastic texture interpolation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
A unified texture model based on a 2-D Wold-like decomposition
IEEE Transactions on Signal Processing
Techniques for flexible image/video resolution conversion with heterogeneous terminals
IEEE Communications Magazine
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
Texture decomposition by harmonics extraction from higher order statistics
IEEE Transactions on Image Processing
Adaptively quadratic (AQua) image interpolation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image coding based on a fractal theory of iterated contractive image transformations
IEEE Transactions on Image Processing
Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation
IEEE Transactions on Image Processing
Markov Random Field Model-Based Edge-Directed Image Interpolation
IEEE Transactions on Image Processing
Multiresolution Bilateral Filtering for Image Denoising
IEEE Transactions on Image Processing
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Super-resolution texture synthesis using a locally-adaptive stochastic signal model is investigated in this work. The 2D random texture is modeled by a piecewise auto-regressive (PAR) process whose parameters are determined by a non-local (NL) training procedure and, consequently, it is called the PAR/NL model. Unlike previous work that applies the NL scheme to image pixels directly, the proposed PAR/NL scheme applies the NL scheme to PAR model parameters by assuming that these parameters are self-similar. Furthermore, we describe a probabilistic method for PAR/NL model computation using the maximum a posteriori (MAP) and the expectation-maximization (EM) principles. Experimental results are given to demonstrate the synthesis performance of the proposed PAR/NL technique, which can boost texture detail and eliminate the blurring artifact perceptually.