Search Results for author:
Found 8 papers, 3 papers with code
Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
The deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data.
Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimator
Deep learning image reconstruction algorithms often suffer from model mismatches when the acquisition scheme differs significantly from the forward model used during training.
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
We introduce a novel metric termed the ENsemble Stein's Unbiased Risk Estimate (ENSURE) framework, which can be used to train deep image reconstruction algorithms without fully sampled and noise-free images.
J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality.
Off-the-grid model based deep learning (O-MODL)
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors.
MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI
In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency.
MoDL: Model Based Deep Learning Architecture for Inverse Problems
Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to black-box deep learning approaches, thus reducing the demand for training data and training time.
Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors
Recently an algorithm for finding sparse solution to a linear system of equations has been proposed based on weighted randomized Kaczmarz algorithm.
