Introduces three algorithms for DIP early stopping via pseudo self-referenced images that outperform prior methods on natural and medical images under varying noise.
Convolutional neural networks for inverse problems in imaging: A review.IEEE Signal Processing Magazine, 34(6):85–95
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NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
citing papers explorer
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A Principled Self-Referenced Early Stopping Approach for Deep Image Prior
Introduces three algorithms for DIP early stopping via pseudo self-referenced images that outperform prior methods on natural and medical images under varying noise.
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Neural Fields for NV-Center Inverse Sensing
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.