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.
Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Analy- sis and Machine Intelligence, 2017
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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.