Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

In this paper we propose a generic recursive algorithm for improving image denoising
methods. Given the initial denoised image, we suggest repeating the following “SOS” …

In this paper we introduce a new family of partial difference operators on graphs and study
equations involving these operators. This family covers local variational p-Laplacian, ∞ …

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by
the graph Laplacian or message passing, which filters the neighborhood node information …

Dictionary learning (DL) techniques aim to find sparse signal representations that capture
prominent characteristics in a given data. Such methods operate on a data matrix Y∈ RN× …

Any image can be represented as a function defined on a weighted graph, in which the
underlying structure of the image is encoded in kernel similarity and associated Laplacian …

In this work we consider a generalized bilevel optimization framework for solving inverse
problems. We introduce fractional Laplacian as a regularizer to improve the reconstruction …

The digital world has brought with it many different kinds of data of increasing size and
complexity. Indeed, modern devices allow us to easily obtain images of higher resolution, as …

This paper develops an approximation to the (effective) $ p $-resistance and applies it to
multi-class clustering. Spectral methods based on the graph Laplacian and its …

We have developed a new regularization method for the sparse representation and
denoising of seismic data. Our approach is based on two components: a sparse data …