Example-based image super-resolution with class-specific predictors
Journal of Visual Communication and Image Representation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Variational Bayesian image super-resolution with GPU acceleration
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Accurate image registration for MAP image super-resolution
Image Communication
Hi-index | 0.01 |
In this paper, we propose a maximum a posteriori framework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low-resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework.