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.
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative 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.
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.
citing papers explorer
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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.
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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.
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Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.