New PAR/NL scheme for stochastic texture interpolation

Authors:
Byung Tae Oh;C.-C. Jay Kuo
Affiliations:
Ming Hsieh Department of Electrical Engineering and Signal and Image Processing Institute, University of Southern California, Los Angeles, CA;Ming Hsieh Department of Electrical Engineering and Signal and Image Processing Institute, University of Southern California, Los Angeles, CA
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 5
Cited 1

Example-Based Super-Resolution

IEEE Computer Graphics and Applications
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
Regularity-preserving image interpolation

IEEE Transactions on Image Processing
New edge-directed interpolation

IEEE Transactions on Image Processing
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

IEEE Transactions on Image Processing

Super-resolution texture synthesis using stochastic PAR/NL model

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A texture interpolation technique based on the locally piecewise auto-regressive (PAR) model and the non-local (NL) training procedure is investigated in this work. The proposed PAR/NL scheme selects model parameters adaptively based on local image properties with an objective to improve the interpolation performance of non-adaptive models, e.g., the bicubic algorithm. To determine model parameters for stochastic texture, we use the non-local (NL) learning algorithm to update and refine these local model parameters under the assumption that the PAR model parameters are self-regular. As compared to previous interpolation algorithms, the proposed PAR/NL scheme boosts texture details, and eliminates blurring artifacts perceptually. Experimental results are given to demonstrate the performance of the proposed technique.