Year: 2025
Type: conference-paper
Abstract: Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions. Training traditional DBs for image reconstruction requires high-quality reference images, which limits their applicability to settings where such references are unavailable. We propose SelfDB as a novel self-supervised method for training DBs directly on available noisy measurements without any high-quality reference images. SelfDB formulates the diffusion process by further sub-sampling the available measurements two additional times and training a neural network to reverse the corresponding degradation process by using the available measurements as the training targets. We validate SelfDB on compressed sensing MRI, showing its superior performance compared to the denoising diffusion models.
Institution: Washington University in St. Louis
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CAREER: Reconciling Model-Based and Learning-Based Imaging: Theory, Algorithms, and Applications, Neuroinflammation in Cerebral Small Vessel Disease using PET/MR Imaging, MR measured regional hypoxia as an early biomarker in cerebral small vessel disease, G6671297155, G9000052311, G9000186481
OpenAlex ID: W4410295684