Conditional score-based diffusion models synthesize phase maps from magnitude-only MR images to generate k-space datasets that train superior deep learning models for accelerated MRI reconstruction compared to smooth-phase or GAN-based alternatives.
Kingma and Jimmy Ba
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
TM-BSN introduces triangular-masked convolutions that align blind spots with diamond-shaped noise correlations from camera demosaicing, enabling stronger self-supervised denoising at full resolution without downsampling.
citing papers explorer
-
Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
Conditional score-based diffusion models synthesize phase maps from magnitude-only MR images to generate k-space datasets that train superior deep learning models for accelerated MRI reconstruction compared to smooth-phase or GAN-based alternatives.
-
TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising
TM-BSN introduces triangular-masked convolutions that align blind spots with diamond-shaped noise correlations from camera demosaicing, enabling stronger self-supervised denoising at full resolution without downsampling.