… We study the mechanisms and implications of graph Laplacian regularization for inverse
imaging problems in this paper. Different from classical digital signal processing with regular …

… to be smooth in the graph-signal domain. The strength and … of graph Laplacian regularizer
to its corresponding continuous … space and the corresponding graph Laplacian in Section 3. …

… module into a deep learning framework, we are less susceptible to overfitting than pure
CNNbased approaches, achieving higher robustness to small datasets and cross-domain …

… Then obtains the optimal solution of discrete Laplacian regularizer by exploring the optimal
solution in continuous domain. The advantage of doing analysis in continuous domain is that …

… Appropriate signal priors are required to regularize an otherwise under-determined inverse
… , “Graph Laplacian regularization for image denoising: Analysis in the continuous domain,” …

… to address such an ill-posed inverse problem. The framework we explored is a multi-scale …
graphs: Extending highdimensional data analysis to networks and other irregular domains,” …

… to inverse problems in vision. They define the so-called non-local gradient, divergence and
Laplacian operators in the continuous domain, … In the PDE-based imaging formalism, this …

… The most common graph smoothness prior to regularize an inherently illposed signal …
manifold domain, [42] showed that the graph Laplacian matrix converges to the continuous Laplace…

… employs the graph Laplacian as the regularization operator. … On the left side, we report the
space domain where the … cannot work directly in a continuous framework, we must discretize …

… of the graph Laplacian as regularization operator. Firstly, we describe how to construct the …
Graph laplacian regularization for image denoising: Analysis in the continuous domain. IEEE …