Non-stationary approximate Bayesian super-resolution using a hierarchical prior model

  title={Non-stationary approximate Bayesian super-resolution using a hierarchical prior model},
  author={Nathan A. Woods and Nikolas P. Galatsanos},
  journal={IEEE International Conference on Image Processing 2005},
We propose a new solution to the problem of obtaining a single high-resolution image from multiple blurred, noisy, and undersampled images. Our estimator, derived using the Bayesian stochastic framework, is novel in that it employs a new hierarchical non-stationary image prior. This prior adapts the restoration of the super-resolved image to the local spatial statistics of the image. Numerical experiments demonstrate the effectiveness of the proposed approach. 

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