frankzhangyk Goto Github PK

image-reconstruction-by-cnn-based-pgd

Framework to train CNN and use it in Relaxed Projected Gradient Descent (RPGD) to reconstruct images

improved_wgan_training

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

ista-net

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing, CVPR20108 (Tensorflow Code)

k-space-deep-learning

k-Space Deep Learning for Accelerated MRI

kl_wgan_sim

learn

learned_gradient_tomography

Solving ill-posed inverse problems using iterative deep neural networks

learned_primal_dual

Learned Primal-Dual Reconstruction

matlab-art

Algebraic Reconstruction Technique (ART)

matlab-cg

Conjugate Gradient method (CG)

matlab-dmd

Dynamic Mode Decomposition (DMD)

matlab-ft-properties

Fourier transform properties

matlab-koopman

Koopman operator

matlab-parallel-ct

Parallel-beam CT (MATLAB ver.)

matlab-transpose

Transpose operator

matlab-with-mex-parallel-ct

Parallel-beam CT (C/C++ ver.)

minimal_wgan

A minimal implementation of Wasserstein GAN

modl

MoDL: Model-Based Deep Learning Architecture for Inverse Problems

modl-mussels

Diffusion MRI using Deep Learning

multiscale

multiscale_dense

Efficient implementation of multiscale dense networks

neural-pgd

This code implements the neural proximal gradient descent (PGD) algorithm proposed in https://arxiv.org/abs/1806.03963. The idea is to unroll the proximal gradient descent algorithm and model the proximal using a neural network. Adopting residual network (ResNet) as the proximal, a recurrent neural net (RNN) is implemented to learn the proximal. The code is flexible to incorporate various training costs such as pixel-wise l1/l2 cost, adversarial GAN, LSGAN, and WGAN.

noise2self

A framework for blind denoising with self-supervision.

odl

Operator Discretization Library https://odlgroup.github.io/odl/

onenet

pdf-ml-losses

[PDF] Machine Learning Loss Functions

pnp-sci

Plug-and-play Algorithms for Large-scale Snapshot Compressive Imaging (CVPR 2020, Oral)

prenet

Progressive Image Deraining Networks: A Better and Simpler Baseline (CVPR 2019)

pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

pyro-nn

Python Reconstruction Operators in Neural Networks. High level python API for PYRO-NN-Layers