First, we present the computational procedures of gradients for the ADMM-Net.
Second, we include more results and comparisons for CT images reconstruction.
Created by Wang Han.SCU on 22/10/16.
Copyright (C) 2016 Deep ADMM NETWORK. SCU. All rights reserved.
特声明，本代码是针对论文Yan Yang, Jian Sun*, Huibin Li, Zongben Xu. Deep ADMM-Net for Compressive Sensing MRI, Advances in Neural Information Processing Systems (NIPS), Accepted, 2016 研究思路的复现，与原作者代码实现无关，如对原作者研究感兴趣，可参照原作者论文链接http://gr.xjtu.edu.cn/web/jiansun/4
DICOM Image Library：
http://www.osirix-viewer.com/resources/dicom-image-library/
OpenfMRI:
https://openfmri.org/
The Center for Imaging Science:
http://www.cis.jhu.edu/data.sets/
BrainWeb: Simulated Brain Database:
http://brainweb.bic.mni.mcgill.ca/brainweb/