Bayesian Image Super-Resolution, Continued
Lyndsey Pickup, David Capel, Stephen Roberts and Andrew Zisserman
In: NIPS 2006, 4-7 Dec 2006, Vancouver, Canada.
This paper develops a multi-frame image super-resolution approach from a Bayesian view-point by marginalizing over the unknown registration parameters relating the set of input low-resolution views. In Tipping and Bishop's Bayesian image super-resolution approach [Tipping & Bishop 2003], the marginalization was over the super-resolution image, necessitating the use of an unfavorable image prior. By integrating over the registration parameters rather than the high-resolution image, our method allows for more realistic prior distributions, and also reduces the dimension of the integral considerably, removing the main computational bottleneck of the other algorithm. In addition to the motion model used by Tipping and Bishop, illumination components are introduced into the generative model, allowing us to handle changes in lighting as well as motion. We show results on real and synthetic datasets to illustrate the efficacy of this approach.