A ground-truth-free self-supervised angular deblurring method for photoacoustic reconstruction that approaches supervised performance by modeling finite-detector effects via Noisier2Inverse in polar coordinates.
Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.Magnetic resonance in medicine, 84(6):3172–3191, 2020
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SPLIT partitions projection data to enforce cross-consistency and measurement fidelity, proving that its self-supervised objective matches supervised training in expectation under mild conditions, with strong results on sparse-view multispectral CT.
citing papers explorer
-
Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse
A ground-truth-free self-supervised angular deblurring method for photoacoustic reconstruction that approaches supervised performance by modeling finite-detector effects via Noisier2Inverse in polar coordinates.
-
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
SPLIT partitions projection data to enforce cross-consistency and measurement fidelity, proving that its self-supervised objective matches supervised training in expectation under mild conditions, with strong results on sparse-view multispectral CT.