IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
Extension of phase correlation to subpixel registration
IEEE Transactions on Image Processing
Bayesian multichannel image restoration using compound Gauss-Markov random fields
IEEE Transactions on Image Processing
Parameter estimation in Bayesian high-resolution image reconstruction with multisensors
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Joint Blind Super-Resolution and Shadow Removing
IEICE - Transactions on Information and Systems
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
Fusion and inversion of SAR data to obtain a superresolution image
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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This paper presents a new method for super-resolution (SR) reconstruction of a high-resolution (HR) image from several low-resolution (LR) images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM) and a Potts Markov model (PMM) for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a randomnoise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC) Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.