Linear approximation of mean curvature
Yuanhao Gong, Yuan Xie · 2017
Mean curvature has been shown a good regularization for many image processing tasks. Computing mean curvature, however, usually requires the image at least twice differentiable, which is an issue for discrete images, especially at edges. In this paper, we present several linear schemes to approximate the mean curvature of discrete images, based on Euler Theorem from differential geometry. We further compare these schemes with the traditional formula in terms of accuracy, computational efficiency, convexity, etc. The experiments confirm that these schemes are good approximations to the mean curvature of discrete images.