CURE: Curvature Regularization For Missing Data Recovery
This work addresses the challenge of missing data recovery in imaging and data science, offering an incremental improvement over prior regularization techniques.
The authors tackled the problem of missing data recovery by introducing a new regularization method called CURE, which combines low dimension manifold regularization with curvature regularization, and demonstrated that it significantly outperforms existing methods like LDMM and WNLL in image inpainting and semi-supervised learning tasks.
Missing data recovery is an important and yet challenging problem in imaging and data science. Successful models often adopt certain carefully chosen regularization. Recently, the low dimension manifold model (LDMM) was introduced by S.Osher et al. and shown effective in image inpainting. They observed that enforcing low dimensionality on image patch manifold serves as a good image regularizer. In this paper, we observe that having only the low dimension manifold regularization is not enough sometimes, and we need smoothness as well. For that, we introduce a new regularization by combining the low dimension manifold regularization with a higher order Curvature Regularization, and we call this new regularization CURE for short. The key step of solving CURE is to solve a biharmonic equation on a manifold. We further introduce a weighted version of CURE, called WeCURE, in a similar manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments for image inpainting and semi-supervised learning show that the proposed CURE and WeCURE significantly outperform LDMM and WNLL respectively.