Learnable Douglas-Rachford iteration and its applications in DOT imaging
Abstract
How to overcome the ill-posed nature of inverse problems is a pervasive problem in medical imaging. Most existing solutions are based on regularization techniques. This paper proposed a deep neural network (DNN) based image reconstruction method, the so-called DR-Net, that leverages the interpretability of existing regularization methods and adaptive modeling capacity of DNN. Motivated by a Douglas-Rachford...
Paper Details
Title
Learnable Douglas-Rachford iteration and its applications in DOT imaging
Published Date
Jan 1, 2020
Journal
Volume
14
Issue
4
Pages
683 - 700
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