Image Magnification Using Level-Set Reconstruction

Image magnification is a common problem in imaging applications, requiring interpolation to “read between the pixels”. Although many magnification/interpolation algorithms have been proposed in the literature, all methods must suffer to some degree the effects of imperfect reconstruction—false high-frequency content introduced by the underlying original sampling. Most often, these effects manifest themselves as jagged contours in the image. This paper presents a method for constrained smoothing of such artifacts that attempts to produce smooth reconstructions of the image’s level curves while still maintaining image fidelity. This is similar to other iterative reconstruction algorithms and to Bayesian restoration techniques, but instead of assuming a smoothness prior for the underlying intensity function it assumes smoothness of the level curves. Results show that this technique can produce images whose error properties are equivalent to the initial approximation (interpolation) used while their contour smoothness is both visually and quantitatively improved.