Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Fractal image compression: theory and application
Fractal image compression: theory and application
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Super-Resolution Imaging
MRF parameter estimation by an accelerated method
Pattern Recognition Letters
Super-Resolution from Image Sequences - A Review
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
Accelerating fractal image compression by multi-dimensional nearest neighbor search
DCC '95 Proceedings of the Conference on Data Compression
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A learning-based method for image super-resolution from zoomed observations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ML parameter estimation for Markov random fields with applications to Bayesian tomography
IEEE Transactions on Image Processing
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
An edge-guided image interpolation algorithm via directional filtering and data fusion
IEEE Transactions on Image Processing
A Bayesian approach to image expansion for improved definition
IEEE Transactions on Image Processing
Example-based image super-resolution with class-specific predictors
Journal of Visual Communication and Image Representation
A New Technique for Image Magnification
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A fully automatic one-scan adaptive zooming algorithm for color images
Signal Processing
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In this paper, an effective image magnification algorithm based on an adaptive Markov random field (MRF) model with a Bayesian framework is proposed. A low-resolution (LR) image is first magnified to form a high-resolution (HR) image using a fractal-based method, namely the multiple partitioned iterated function system (MPIFS). The quality of this magnified HR image is then improved by means of a blockwise adaptive MRF model using the Bayesian 'maximum a posteriori' (MAP) approach. We propose an efficient parameter estimation method for the MRF model such that the staircase artifact will be reduced in the HR image. Experimental results show that, when compared to the conventional MRF model, which uses a fixed set of parameters for a whole image, our algorithm can provide a magnified image with the well-preserved edges and texture, and can achieve a better PSNR and visual quality.