GSNR constructs a null-restricted graph Laplacian and projects onto its smoothest modes to regularize only the null-space part of inverse problem solutions, yielding up to 4.3 dB PSNR gains when plugged into PnP, DIP, and diffusion solvers.
Graph signal processing: Overview, challenges, and applications.Proceedings of the IEEE, 106(5):808–828, 2018
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GSNR: Graph Smooth Null-Space Representation for Inverse Problems
GSNR constructs a null-restricted graph Laplacian and projects onto its smoothest modes to regularize only the null-space part of inverse problem solutions, yielding up to 4.3 dB PSNR gains when plugged into PnP, DIP, and diffusion solvers.