ADDGAN learns clean image distributions from noisy CT and DBT measurements by feeding generated objects through the known imaging operator, and it beats AmbientGAN baselines on FID and observer-task metrics.
Approximating the ideal observer for joint signal detection and localization tasks by use of supervised learning methods,
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Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data
ADDGAN learns clean image distributions from noisy CT and DBT measurements by feeding generated objects through the known imaging operator, and it beats AmbientGAN baselines on FID and observer-task metrics.