Diffusion-based inverse problem solvers are made robust to outliers by combining explicit noise estimation with a Huber-loss IRLS objective solved via conjugate gradient.
Denoising diffu- sion models for plug-and-play image restoration
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Outlier-Robust Diffusion Solvers for Inverse Problems
Diffusion-based inverse problem solvers are made robust to outliers by combining explicit noise estimation with a Huber-loss IRLS objective solved via conjugate gradient.