Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?

Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?

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In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images.This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur) and obtaining a MAP estimate by minimizing a cost function including an appropriate prior.Two alternative approaches are examined.

First, both registrations and galaxy harmony nc the super-resolution image are found simultaneously using a joint MAP here optimization.Second, we perform Bayesian integration over the unknown image registration parameters, deriving a cost function whose only variables of interest are the pixel values of the super-resolution image.We also introduce a scheme to learn the parameters of the image prior as part of the super-resolution algorithm.

We show examples on a number of real sequences including multiple stills, digital video, and DVDs of movies.