STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
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B-FIRE uses a diffusion-optimized CNN-INR to reconstruct instantaneous 3D abdominal anatomy from binning-free, hyper-accelerated non-Cartesian k-space data in motion-resolved MRI.
MSIQ is a scale-invariant, model-free quality metric for single image super-resolution using normalized central geometric moments for direct comparison of different-resolution images.
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
SADM adds a signal attenuation coefficient to the diffusion forward process so that reverse denoising simultaneously recovers brightness and suppresses noise without extra stages or correction modules.
citing papers explorer
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Stochastic Transition-Map Distillation for Fast Probabilistic Inference
STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
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B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
B-FIRE uses a diffusion-optimized CNN-INR to reconstruct instantaneous 3D abdominal anatomy from binning-free, hyper-accelerated non-Cartesian k-space data in motion-resolved MRI.
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MSIQ: Moment-based Scale-Invariant Quality Measure for Single Image Super-Resolution
MSIQ is a scale-invariant, model-free quality metric for single image super-resolution using normalized central geometric moments for direct comparison of different-resolution images.
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On What We Can Learn from Low-Resolution Data
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
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Single-Stage Signal Attenuation Diffusion Model for Low-Light Image Enhancement and Denoising
SADM adds a signal attenuation coefficient to the diffusion forward process so that reverse denoising simultaneously recovers brightness and suppresses noise without extra stages or correction modules.