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Non-stationary approximate Bayesian super-resolution using a hierarchical prior model | IEEE Conference Publication | IEEE Xplore

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


Abstract:

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 t...Show More

Abstract:

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.
Date of Conference: 14-14 September 2005
Date Added to IEEE Xplore: 14 November 2005
Print ISBN:0-7803-9134-9

ISSN Information:

Conference Location: Genova, Italy

References

References is not available for this document.