Abstract
PDE-based approaches are used to obtain superresolution of images. Using level set method for area constraint geometric evolution laws allows to remove pixelation in images without removing small-scale features. We compare mean curvature flow, surface diffusion, and Willmore flow for this purpose.
Highlights
The goal of superresolution is to construct out of a given low-resolution image an image with higher resolution
Therefor, we use the logo of our university and apply area preserving Willmore flow, mean curvature flow, and surface diffusion to smooth the jagged parts of the text given at a low resolution
We consider PDE bases approaches to reconstruct geometric properties of images by evolving their level curves. Applying these level set reconstructions to obtain superresolution images had already been shown to lead to improved results, as jagged parts of the image can be smoothed
Summary
The goal of superresolution is to construct out of a given low-resolution image an image with higher resolution. For interpolation methods based on PDEs, which are mostly adapted from inpainting problems, we refer to [2,3,4,5,6] These approaches try to reconstruct the geometric properties of the images by evolving their level curves. In [4], the problem is attacked from a different point of view, by constraining the mean curvature flow problem in order to preserve the area enclosed by each level curve. We will follow this approach and extend it to other geometric evolution problems of higher order.
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