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

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

Blog Article

In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images.This usually involves computing click here 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 the super-resolution image are found simultaneously using a joint MAP 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 opheliasmuse.com of the super-resolution algorithm.We show examples on a number of real sequences including multiple stills, digital video, and DVDs of movies.