ProSAC-CT is a progressive diffusion model for LDCT denoising that combines anatomical conditioning, residual frequency decoupling, and staged decoding to improve fidelity and preserve anatomical details over prior methods.
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Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.
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ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
ProSAC-CT is a progressive diffusion model for LDCT denoising that combines anatomical conditioning, residual frequency decoupling, and staged decoding to improve fidelity and preserve anatomical details over prior methods.
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Unifying Deep Stochastic Processes for Image Enhancement
Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.