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.
Deep learning techniques for inverse problems in imaging.IEEE Journal on Selected Areas in Information Theory, 1(1):39–56, 2020
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A ray-tracing ultrasound simulator is made end-to-end differentiable, allowing automatic optimization of scene and system parameters from image-space losses.
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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.
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Fully Differentiable Ultrasound Simulation Utilizing Ray-Tracing
A ray-tracing ultrasound simulator is made end-to-end differentiable, allowing automatic optimization of scene and system parameters from image-space losses.