FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
Self-validation: Early stopping for single-instance deep generative priors.arXiv:2110.12271
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SDIP is a zero-shot DIP method using sequential autoencoding regularization for denoising followed by Richardson-Lucy-guided DIP for deconvolution, reporting improved SNR and resolution on BioSR cellular structures.
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Saving Foundation Flow-Matching Priors for Inverse Problems
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
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A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy
SDIP is a zero-shot DIP method using sequential autoencoding regularization for denoising followed by Richardson-Lucy-guided DIP for deconvolution, reporting improved SNR and resolution on BioSR cellular structures.