The little engine that could: Regularization by denoising (RED)
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 …
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …
Boosting of image denoising algorithms
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” …
methods. Given the initial denoised image, we suggest repeating the following “SOS” …
On the -Laplacian and -Laplacian on Graphs with Applications in Image and Data Processing
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, ∞ …
equations involving these operators. This family covers local variational p-Laplacian, ∞ …
Bilevel optimization, deep learning and fractional Laplacian regularization with applications in tomography
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 …
problems. We introduce fractional Laplacian as a regularizer to improve the reconstruction …
A general framework for regularized, similarity-based image restoration
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 …
underlying structure of the image is encoded in kernel similarity and associated Laplacian …
Dual graph regularized dictionary learning
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× …
prominent characteristics in a given data. Such methods operate on a data matrix Y∈ RN× …
Complete the missing half: Augmenting aggregation filtering with diversification for graph convolutional networks
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 …
the graph Laplacian or message passing, which filters the neighborhood node information …
A connected network-regularized logistic regression model for feature selection
Feature selection on a network structure can not only discover interesting variables but also
mine out their intricate interactions. Regularization is often employed to ensure the sparsity …
mine out their intricate interactions. Regularization is often employed to ensure the sparsity …
[BOOK][B] Variational and diffusion problems in random walk spaces
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 …
complexity. Indeed, modern devices allow us to easily obtain images of higher resolution, as …
Sparse graph-regularized dictionary learning for suppressing random seismic noise
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 …
denoising of seismic data. Our approach is based on two components: a sparse data …