KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #developer-tools #cli #codex #claude-code #dsh-plugin #mcp #react #agent #dsh #deepseek-harness

ADMM-CSNet

★ 83 Open GitHub ↗

Matlab code for ADMM-CSNet

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ADMM-CSNet
# Generic-ADMM-CSNet

***********************************************************************************************************

These are  testing and training codes for Generic-ADMM-CSNet in "ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing" (TPAMI 2019)
 
If you use thses codes, please cite our paper:

[1] Yan Yang, Jian Sun, Huibin Li, Zongben Xu. ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing (TPAMI 2019).

http://gr.xjtu.edu.cn/web/jiansun/publications

All rights are reserved by the authors.

Yan Yang -2019/04/10. For more detail or traning data, feel free to contact: yangyan92@stu.xjtu.edu.cn

***********************************************************************************************************

## Data link: 
https://pan.baidu.com/s/1nvf07g_OmMAnFAbhG1orIQ 
passwards：sdsq

## Usage:

1. Three folders.
 
 1). 'Generic-ADMM-CSNet-ComplexMRI' are testing and training codes to reconstruct complex-valued MR images with 1D Cartesian masks and 2D random masks. 
 2). 'Generic-ADMM-CSNet-RealMRI' are testing and training codes to reconstruct real-valued MR images with the Pseudo radial mask. 
 3). 'Generic-ADMM-CSNet-Image' are testing and training codes to reconstruct natural images with the randomly permuted coded diffraction operators and Walsh-Hadamard operators. 
 
 ## Please do not add these three folders into the path at the same time, because they contain the functions with the same name.

2. For testing the trained network for a single image. 
 ('./Generic-ADMM-CSNet-ComplexMRI/main_ADMM_CSNet_test.m') 
 ('./Generic-ADMM-CSNet-RealMRI/main_ADMM_CSNet_test.m') 
 ('./Generic-ADMM-CSNet-Image/main_ADMM_CSNet_test.m')

1). Load trained network with different stages in main_ADMM_CSNet_test.m. 
	 If you apply ADMM-CSNet to reconstruct other MR or natural images, it is best to re-train the models.

E.g., The model './net/NET-1D-Cartesian-0.2-complex-S10.mat' is the ADMM-CSNet with 10 stages trained from 100 complex-valued MR images using 1D Cartesian mask with 20% sampling rate.
		 The model './net/NET-Pseudo-radial-0.2-real-S11.mat' is the network with 11 stages trained from 100 real-valued MR images using Pseudo radial mask with 20% sampling rate.
 The model './net/Net-Diffraction-0.05-S10.mat' is the network with 10 stages trained from 100 real-valued natural images using coded diffraction operator with 5% sampling rate.
 
 2). Load test image in main_ADMM_CSNet_test.m 
 The images in './data/Brain_complex_data', './data/Brain_real_data', './data/Image' are fully-sampled images. 
 
 3). Load sampling mask or operator with different sampling ratios in main_ADMM_CSNet_test.m

E.g., The mask './mask/1D-Cartesian-0.2.mat' is a 1D Cartesian mask with 20% sampling rate.
              The mask './mask/D-0.1.mat' is a coded diffraction operator with 10% sampling rate.

4). Network testing setting (network structure or training setting) is in 'config.m '.

5). To test our ADMM-CSNet, run 'main_ADMM_CSNet_test.m'

3. For testing the trained network for our testing dataset. 
 ('./Generic-ADMM-CSNet-ComplexMRI/AverageTesting.m') 
 ('./Generic-ADMM-CSNet-RealMRI/AverageTesting.m') 
 ('./Generic-ADMM-CSNet-Image/AverageTesting.m')

1). Load trained network with different stages in AverageTesting.m. 
	 If you apply ADMM-CSNet to reconstruct other MR or natural images, it is best to re-train the models.

E.g., The model './net/NET-1D-Cartesian-0.2-complex-S10.mat' is the ADMM-CSNet with 10 stages trained from 100 complex-valued MR images using 1D Cartesian mask with 20% sampling rate.
		 The model './net/NET-Pseudo-radial-0.2-real-S11.mat' is the network with 11 stages trained from 100 real-valued MR images using Pseudo radial mask with 20% sampling rate.
 The model './net/Net-Diffraction-0.05-S10.mat' is the network with 10 stages trained from 100 real-valued natural images using coded diffraction operator with 5% sampling rate.
 
 2). Set the data_dir of testing dataset ans load the correspongding mask in AverageTesting.m

E.g., data_dir = './data/DATA-1D-Cartesian-0.2-complex-brain/test/' is the testing dataset including 100 complex-valued brain MR image with 20% 1D-Cartesian mask.  
              data_dir = './data/Testingdata/Sdata10/D/D_0.1_1/' is the first testing dataset including 10 standard image with 10% coded diffraction operator.  
              data_dir = './data/DATA-Pseudo-radial-0.2-real-brain/test/'is the testing dataset including 50 real-valued brain MR image with 20% Pseudo radial mask.

3). Network testing setting (network structure or training setting) is in 'config.m '.

4). To test our ADMM-CSNet, run 'AverageTesting.m'

4. For re-training the ADMM-CSNets

1). Set the data_dir of training dataset ans load the correspongding mask in L_BFGSnetTrain.m. 
 	 
	2). Modify the network setting and trainging setting in 'config.m '.

3). To train ADMM-CSNet by L-BFGS algorithm, run ' L_BFGSnetTrain.m' .

4). After training, the trained network and the training error are saved in './Train_output'.

***********************************************************************************************************

ADMM-CSNet

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README