Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?. (19th August 2007) - Pickup Pickup, Lyndsey C. Lyndsey C.; Capel Capel, David P. David P.; Roberts Roberts, Stephen J. Stephen J.; Zisserman Zisserman, Andrew Andrew
Abstract : 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 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 of the super-resolution algorithm. We show examples on a number of real sequences including multiple stills, digital video, and DVDs of movies.