SoftCuts Super Resolution
Supported in part by NSF and NEC
Effective image prior is necessary for image super resolution, due to its severely under-determined nature. Although the edge smoothness prior can be effective, it is generally difficult to have analytical forms to evaluate the edge smoothness, especially for soft edges that exhibit gradual intensity transitions.
This paper finds the connection between the soft edge smoothness and a soft cut metric on an image grid by generalizing the Geocuts method, and proves that the soft edge smoothness measure approximates the average length of all level lines in an intensity image. This new finding not only leads to an analytical characterization of the soft edge smoothness prior, but also gives an intuitive geometric explanation. Regularizing the super resolution problem by this new form of prior can simultaneously minimize the length of all level lines, and thus resulting in visually appealing results. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image super resolution, by normalizing edge segments with their alpha channel description, to achieve a unified treatment of edges with different contrast and scale.
Low resolution inputs vs. Our results with Softcuts
Results on tiny images
Level lines using different order of neighborhood
(larger neighborhood brings smoother results, please check paper for details)
n=2 n=4 n =12
Visualization of the iteration process
Updated 9/2008. Copyright ©
2007-2008 Ying Wu