On space-time super resolution

obtaining high-resolution images and videos from multiple low resolution
inputs. The increased resolution can be in spatial or temporal
dimensions, or even in both. We will present a unified framework which
uses a generative model of the imaging process and can address spatial
super-resolution, space-time super-resolution, image deconvolution,
single image expansion, removal of noise and image restoration. We model
a high resolution image or video as a Markov random field and use
maximum a posteriori estimate as the final solution using graph-cut
optimization technique. We will derive insights in to what
super-resolution magnification factors are possible and the conditions
necessary for super-resolution. We will demonstrate spatial
super-resolution reconstruction results with magnifications higher than
predicted limits of magnification. We will also formulate a scheme for
selective super-resolution reconstruction of videos to obtain
simultaneous increase of resolutions in both spatial and temporal
directions. We will show that it is possible to achieve space-time
magnification factors beyond what has been suggested in the literature
by selectively applying super-resolution constraints.

Time permitting, we will also discuss some issues in the generation of
super resolved novel views of a 3D scene from a set of images.