Search Results for author:
Found 5 papers, 3 papers with code
Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction
In this work, we first provide an analysis of how DIP recovers information from undersampled imaging measurements by analyzing the training dynamics of the underlying networks in the kernel regime for different architectures.
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
To address this problem, we propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach.
Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification
In particular, we present a robustification strategy that improves the resilience of DL-based MRI reconstruction methods by utilizing pretrained diffusion models as noise purifiers.
SMUG: Towards robust MRI reconstruction by smoothed unrolling
To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation.
Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
In brief, our algorithm alternates between searching for neighbors in a data set that are similar to the test reconstruction, and training a local network on these neighbors followed by updating the test reconstruction.
